3D Proton Resonance Frequency Shift MR Thermometry for Monitoring Clinical Microwave Ablation: Comparison of Stack-of-Stars and Stack-of-Spirals Sequences

3D Proton Resonance Frequency Shift MR Thermometry for Monitoring Clinical Microwave Ablation: Comparison of Stack-of-Stars and Stack-of-Spirals Sequences | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article 3D Proton Resonance Frequency Shift MR Thermometry for Monitoring Clinical Microwave Ablation: Comparison of Stack-of-Stars and Stack-of-Spirals Sequences Dominik Horstmann, Othmar Belker, Daniel Düx, Thomas Gerlach, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6176650/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Microwave ablation (MWA) of hepatic tumors benefits from MR thermometry, enabling real-time temperature monitoring to guide treatment and protect healthy tissue. However, MR thermometry in the abdomen is challenging due to respiratory and intestinal motion. This study evaluates two advanced 3D imaging sequences, Stack-of-Stars (Stars) and Stack-of-Spirals (Spirals), for precise MWA thermometry in phantom and volunteer experiments. Spirals demonstrated superior temperature precision, with a standard deviation of 0.78 ± 0.05°C in unheated regions, compared to 2.92 ± 0.26°C for Stars. In heated regions, Spirals achieved a lower RMSE (0.5 ± 0.1°C vs. 1.2 ± 0.2°C for Stars) and a higher Dice score for ablation zone delineation (0.89 ± 0.01 vs. 0.76 ± 0.10). Spirals also produced sharper images with fewer artifacts under simulated respiratory motion, while Stars showed streaking artifacts due to higher undersampling. These findings highlight Spirals’ potential for accurate real-time thermometry in liver ablation. Future work should focus on improving reconstruction speed and mitigating susceptibility artifacts to enable clinical applications. Physical sciences/Physics/Techniques and instrumentation/Imaging techniques Health sciences/Medical research/Translational research stack-of-stars stack-of-spirals 3D MR thermometry respiratory motion microwave ablation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Microwave ablation (MWA) is a widely used thermal therapy that destroys tumor tissues by applying heat over larger areas compared to other modalities [1]. Unlike ultrasound and computed tomography (CT), magnetic resonance imaging (MRI) offers real-time monitoring of temperature changes during procedures through MR thermometry, making it a valuable tool for precise treatment guidance [2]. Precise temperature monitoring during ablation is crucial for assessing tissue damage and guiding decisions on treatment continuation, duration, and targeting. MR thermometry has been shown to predict post-procedural ablation zones accurately, enabling real-time evaluation of treatment outcomes. This capability reduces recurrence risk by allowing immediate re-ablation of under-treated areas and protects nearby sensitive structures, such as the bowel [3, 4]. Among MR-based techniques, proton resonance frequency shift (PRFS) thermometry is widely used in clinical applications due to its linear temperature dependence and minimal sensitivity to tissue-specific variations, except in adipose tissue [5]. By leveraging phase variations in conventional MRI sequences, PRFS thermometry achieves high spatial and temporal resolution, making it suitable for real-time monitoring during thermal therapies. However, it remains susceptible to motion, phase drift, and susceptibility changes induced by temperature fluctuations, tissue transitions, or gas formation during ablation [6, 7, 8]. For PRFS thermometry to be clinically effective, it must combine motion robustness, high sensitivity, and minimal susceptibility to artifacts, while maintaining sufficient spatial and temporal resolution [9]. Achieving this balance remains a major challenge in abdominal imaging. While 2D and multi-slice 2D MR thermometry have been clinically applied, they are often limited by restricted coverage and susceptibility to motion artifacts, particularly in dynamic organs like the liver [10]. To address these challenges, 3D thermometry approaches are under investigation. Among these, multi-echo Stack-of-Stars (Stars) and Stack-of-Spirals (Spirals) acquisitions show promise, with Stars offering improved motion robustness through radial readouts and Spirals enabling higher spatial and temporal resolution through efficient k-space sampling [11, 12, 13]. This study aims to identify a suitable 3D PRFS-based MR thermometry sequence for hepatic microwave ablation by assessing the feasibility, accuracy, and clinical applicability of Stack-of-Stars and Stack-of-Spirals in phantom and volunteer experiments. Results Phantom study: Analysis of magnitude images and undersampling On average, the Stack-of-Stars (Stars) sequence utilized 13 spokes per breath, each with 8 partitions across 7 echoes, while the Stack-of-Spirals (Spirals) sequence employed 7 interleaves per breath, each with 12 partitions and 2 echoes. For a 480 mm field of view (FOV), as used for the phantoms, the resulting undersampling factors ranged from 8.6 (1 breathing cycle, nBC1) to 2.1 (4 breathing cycles, nBC4) for Spirals and from 69.5 (nBC1) to 17.4 (nBC4) for Stars, where nBC denotes the number of breathing cycles per reconstructed image. These differences in undersampling were reflected in the image quality as shown in Fig. 2 , which compares magnitude images from Spirals (Fig. 2 A-D) and Stars (Fig. 2 E-H) at the smallest and largest echo times (TE). Images acquired with Stars exhibited noticeable streaking artifacts caused by more aggressive undersampling, while those obtained with Spirals were largely free of artifacts, demonstrating cleaner and more consistent quality. As TE increased, signal loss around the needle due to T2* effects became more pronounced, enlarging the needle artifact. This effect was further exacerbated by heating, leading to additional signal loss. Due to its shorter minimum TE of 1.7 ms, Stars exhibited a smaller needle artifact and less signal loss at baseline and during heating compared to Spirals. Temperature Precision in Unheated Regions Temperature accuracy in unheated regions differed significantly across all examined nBCs. The Stack-of-Spirals (Spirals) sequence consistently exhibited superior temperature precision, with mean temperature standard deviations ranging from 0.78 ± 0.05°C (nBC4) to 1.61 ± 0.11°C (nBC1). In comparison, the Stack-of-Stars (Stars) sequence showed higher mean temperature standard deviations, ranging from 2.92 ± 0.26°C (nBC4) to 4.87 ± 0.74°C (nBC1). Pairwise comparisons across all nBCs indicated a progressive and statistically significant reduction in mean temperature standard deviations with increasing nBC for both sequences. This trend demonstrated improved temperature stability at lower temporal resolutions. Notably, comparisons between nBC1 and nBC4 revealed p-values of 0.0078 for Stars and < 0.0001 for Spirals, emphasizing the robustness of Spirals in achieving consistent precision. Temperature Precision in Heated Regions Figure 3 (A-D) presents representative transverse slices from 3D temperature maps and corresponding temperature profiles from a phantom during ablation using Stars and Spirals, compared to data from the temperature sensors. Scatter plots (Fig. 3 E-F) depict the relationship between Root Mean Squared Error (RMSE) and the distance from the ablation center (𝑑), revealing that temperature accuracy, represented by RMSE, improves with increasing distance from the ablation center, a trend seen across all nBCs. Notably, the linear regression fits are significant, except for the nBC4 fit for Spirals, which is near significance. The regression lines for Spirals consistently lie below those for Stars, indicating superior temperature accuracy in heated regions for Spirals. The Analysis of Covariance (ANCOVA) results confirmed significant differences in RMSE between Stars and Spirals for nBC2 (F = 11.06, p = 0.0025), nBC3 (F = 12.30, p = 0.0015), and nBC4 (F = 16.72, p = 0.0003), supporting Spirals' superior temperature accuracy across these conditions. The distance from the ablation center significantly influenced RMSE across all nBCs (e.g., F = 29.22, p < 0.0001 for nBC2), highlighting its critical role in temperature measurement accuracy. The interaction term between method and distance was not significant for any nBC (e.g., F = 0.13, p = 0.7180 for nBC2), suggesting that the RMSE-distance relationship remained consistent across both methods. Analysis of Variance (ANOVA) showed a significant effect on RMSE for Spirals (F = 3.91, p = 0.013), with Tukey's HSD revealing a significant difference only between nBC1 and nBC2. No significant effect was found for Stars (F = 1.12, p = 0.349). Precision of the Calculated Ablation Zone Figure 4 illustrates the comparison between the calculated ablation zones and the ground truth for a representative phantom. Both Stars and Spirals demonstrated close alignment with the ground truth. However, near the ablation center, Spirals exhibited more false positives than Stars, indicating a slight overestimation of the ablation zone. Conversely, in peripheral regions, particularly for Stars, false negatives were more prevalent, reflecting an underestimation of the ablation zone boundaries. Table 1 Metrics Comparing Ablation Zone Accuracy between Stack-of-Stars and Stack-of-Spirals across Temporal Resolutions Metric Method nBC1 nBC2 nBC3 nBC4 µ ± σ ES µ ± σ ES µ ± σ ES µ ± σ ES Dice Spirals 0.88 ± 0.02 1.73 0.89 ± 0.01 1.83 0.89 ± 0.01 1.87 0.89 ± 0.01 1.88 Stars 0.77 ± 0.09 0.77 ± 0.10 0.76 ± 0.10 0.76 ± 0.10 MSD Spirals 0.13 ± 0.02 -1.51 0.11 ± 0.02 -1.61 0.11 ± 0.01 -1.65 0.11 ± 0.01 -1.65 Stars 0.33 ± 0.19 0.34 ± 0.20 0.35 ± 0.21 0.35 ± 0.20 Sensitivity Spirals 0.91 ± 0.03 1.47 0.91 ± 0.03 1.53 0.89 ± 0.04 1.43 0.88 ± 0.04 1.39 Stars 0.75 ± 0.15 0.73 ± 0.16 0.72 ± 0.16 0.72 ± 0.16 Dice = Dice Score, ES = Effect Size, MSD = Mean Surface Distance, µ = Mean Value, nBC = Number of Breathing Cycles, σ = Standard Deviation, Spirals = Stack-of-Spirals, Stars = Stack-of-Stars This observation is corroborated by the statistical metrics presented in Table 1 . Across all nBCs, Spirals consistently outperformed Stars, as evidenced by higher Dice scores, lower Mean Surface Differences (MSD), and greater Sensitivity values, confirming Spirals' superior accuracy in delineating the ablation zone. All differences were statistically significant, with large effect sizes. ANOVA results revealed no significant differences across nBCs for any metric (Dice, MSD, Sensitivity) in either Spirals or Stars, suggesting that temporal resolution did not affect the accuracy of ablation zone measurements. Consequently, no further post hoc comparisons were performed. Volunteer Study Figure 5 presents representative temperature maps for a transverse slice acquired with (Fig. 5 A) Spirals and (Fig. 5 B) Stars, alongside corresponding magnitude images (Fig. 5 . C and D). Both sequences exhibited significant susceptibility artifacts in the temperature maps, primarily due to bowel motion. The magnitude images for Stars displayed more pronounced artifacts, whereas Spirals images provided enhanced anatomical detail, including sharper visualization of vessels and reduced blurriness. The effectiveness of fat saturation in Spirals was evident, with subcutaneous fat—bright in Stars—exhibiting minimal signal in Spirals. Table 2 Temperature Standard Deviation in Liver Regions across Temporal Resolutions with Statistical Comparisons of Stack-of-Stars and Stack-of-Spirals nBC Method Caudal Middle Cranial µ ± σ [°C] p-value ES µ ± σ [°C] p-value ES µ ± σ [°C] p-value ES 1 Spirals 2.5 ± 1.3 0.175 -0.42 2.7 ± 1.4 0.193 -0.40 2.9 ± 1.3 0.189 -0.41 Stars 3.3 ± 0.9 3.5 ± 0.9 4.0 ± 1.8 2 Spirals 1.9 ± 1.1 0.048 -0.58 2.1 ± 1.0 0.051 -0.57 2.1 ± 0.9 0.042 -0.59 Stars 2.9 ± 0.8 3.1 ± 1.0 3.7 ± 1.7 3 Spirals 1.5 ± 0.8 0.021 -0.65 1.6 ± 0.8 0.031 -0.61 1.7 ± 0.7 0.048 -0.58 Stars 2.5 ± 0.7 2.7 ± 1.1 3.1 ± 1.6 4 Spirals 1.5 ± 0.7 0.005 -0.74 1.6 ± 0.7 0.018 -0.66 1.8 ± 0.6 0.038 -0.60 Stars 2.6 ± 0.7 2.7 ± 1.0 3.2 ± 1.6 ES = Effect Sizes, µ = Mean Value of Temperature Standard Deviation over Time, nBC = Number of Breathing Cycles, σ = Standard Deviation of Temperature Standard Deviation over Time, Spirals = Stack-of-Spirals, Stars = Stack-of-Stars Table 2 highlights significant differences in temperature standard deviation (STD) between Spirals and Stars across the caudal, middle, and cranial liver regions. In the caudal region, Spirals showed significantly lower STD for nBC2 and nBC3, reflecting improved temperature stability. In the middle region, Spirals consistently outperformed Stars across all nBCs except nBC4. Similarly, in the cranial region, Spirals demonstrated superior performance with significantly lower STD for nBC2 and nBC3, emphasizing its ability to provide more stable temperature readings. ANOVA for Spirals indicated no significant differences in the caudal (p = 0.125) and middle (p = 0.089) ROIs but revealed a significant effect in the cranial ROI (p = 0.039). However, post hoc Tukey's HSD comparisons showed no significant pairwise differences. For Stars, ANOVA revealed no significant differences in any ROI (caudal: p = 0.108, middle: p = 0.288, cranial: p = 0.649). Pairwise comparisons of STD between caudal, middle, and cranial regions identified a significant difference between the caudal and cranial regions for Spirals at nBC4 (p = 0.05, ES = -0.38), indicating a small to moderate effect size. No other significant pairwise differences were observed for either method. Effect sizes were generally small, suggesting subtle regional variations in temperature stability. Reconstruction Time The reconstruction times for one 3D image for Spirals were 37 s (nBC4), 36 s (nBC3), 35 s (nBC2), and 33 s (nBC1), and for Stars 44 s (nBC4), 43 s (nBC3), 42 s (nBC2), and 40 s (nBC1). Discussion This study demonstrated that the Stack-of-Spirals sequence consistently outperformed the Stack-of-Stars sequence in 3D PRFS-based MR thermometry. Key advantages of Spirals included superior temperature precision, lower root mean squared error (RMSE) in heated regions, and more accurate delineation of ablation zones, reflected by higher Dice scores, lower Mean Surface Distance (MSD), and greater sensitivity values. These findings were consistent across phantom and volunteer experiments. This establishes the Stack-of-Spirals sequence as a promising approach for clinical applications of MR thermometry, particularly in hepatic microwave ablation. The findings of this study align with prior research on MR thermometry: Marx et al. achieved temperature accuracies below 0.5°C in the brain with spiral sequences and high spatial resolution (< 1.5 mm) [13]. While these results demonstrate excellent precision, they were achieved in static 2D brain imaging, a simpler context compared to the challenges of 3D imaging in a moving liver. Similarly, Kim et al. reported liver temperature accuracies of 1–2°C using a multi-baseline strategy in 2D imaging, suggesting potential improvements for 3D imaging if such methods are adapted [14]. Their limited 2D coverage (three 5-mm slices) underscores the advantage of the 60-mm volumetric coverage in this study for larger ablation zones. Dietrich et al. achieved sub-1°C precision using an EPI sequence across 25 slices in static phantom experiments [15]. While their findings provide valuable insights, the absence of motion artifacts in their setup limits their clinical relevance. Ozenne et al. employed SMS-EPI sequences to achieve good spatial and temporal resolutions in 2D volunteer scans, with temperature accuracies of 2°C in unheated regions and below 1°C in phantom ablation zones [10]. Their results are comparable to this study but remain restricted to 2D imaging, which may not fully capture larger ablation zones. EPI-based methods are also more sensitive to magnetic field inhomogeneities, a challenge mitigated here by shorter echo times and spiral/radial sampling strategies. Overall, this study builds upon existing literature by demonstrating a robust 3D thermometry approach that offers comparable accuracy to 2D methods while addressing motion and susceptibility challenges in the liver. Despite its promising results, this study has notable limitations. Reconstruction times of 33–40 seconds per 3D image are insufficient for real-time clinical application and stem from reliance on a single GPU and the absence of temporal regularization in the reconstruction pipeline. Optimizing GPU usage and introducing more efficient reconstruction algorithms are critical next steps. Susceptibility artifacts, especially near air-tissue interfaces and gas bubbles during ablation, remain a challenge. Implementing susceptibility correction methods could enhance accuracy in these areas [16–19]. Furthermore, the volunteer study was conducted under free-breathing conditions without thermal treatment, which may not fully capture the complexities of clinical ablation scenarios. Future work should focus on enhancing the clinical feasibility of the Stack-of-Spirals sequence by addressing current limitations. Real-time reconstruction could be achieved through more efficient use of GPU resources, stronger hardware, and advanced machine learning algorithms. Motion artifacts near the lung diaphragm or the bowel require innovative solutions such as referenceless thermometry and advanced motion correction [14]. Incorporating a multi-baseline strategy, as demonstrated in previous studies, as well as susceptibility correction could further enhance temperature accuracy. Finally, a clinical validation study applying the Stack-of-Spirals sequence during actual microwave ablation procedures is essential to confirm its reliability and utility in guiding real-time treatment decisions. This study demonstrates that the Stack-of-Spirals sequence is a promising approach for real-time 3D PRFS-based MR thermometry in the liver. With a Dice score near 90% and temperature accuracies below 3°C at a temporal resolution of one breathing cycle, it holds potential for clinical application in monitoring hepatic microwave ablation. Future efforts should focus on addressing susceptibility artifacts, enhancing motion robustness, and accelerating reconstruction times to enable real-time use and broader clinical adoption. Methods This study was approved by the Institutional Review Board (IRB) of Hannover Medical School (Approval No. 11019_B0_S_2023) and was conducted in accordance with the principles of the Declaration of Helsinki and relevant national and institutional guidelines. All procedures involving human participants were performed in accordance with the ethical standards of the Hannover Medical School Ethics Committee and the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all participants prior to their inclusion in the study. The aim was to evaluate the performance of Stack-of-Stars (Stars) and Stack-of-Spirals (Spirals) sequences in abdominal 3D PRFS-based MR thermometry. Phantom experiments simulated controlled conditions, while volunteer experiments assessed sequence performance under free-breathing conditions. Sequence Implementation The Stack-of-Stars (Stars) sequence was implemented as a multi-echo spoiled gradient echo with a 4th-order tiny golden angle (38.98°) increment between projections [20]. Projections were rotated after completing Cartesian kz-sampling. The sequence used seven bipolar echoes with alternating gradient polarity per readout, featuring echo times (TE) from 1.7 ms to 14.9 ms and a repetition time (TR) of 17.3 ms. Other parameters included a field of view (FOV) of 480 × 480 × 60 mm³, a spatial resolution of 2.5 × 2.5 × 2.5 mm³, and a bandwidth of 520 Hz/pixel. For the Stack-of-Spirals (Spirals) sequence, a dual-echo spiral-in/spiral-in spoiled gradient echo was implemented with a 12th-order tiny golden angle increment (14.27°). The sequence parameters were TE = 7.5 ms/14.5 ms, TR = 22.6 ms, and a bandwidth of 1040 Hz/pixel. Spiral rotation was applied after completing Cartesian kz-sampling. A variable-density spiral was employed (maximum gradient amplitude: 7.6 mT/m, max. slew rate: 174.0 mT/m/ms), requiring 30 interleaves for Nyquist sampling. The FOV increased linearly from 412 mm to 548 mm, with a spatial resolution of 2.5 mm LEE. Fat saturation was applied every 3rd TR to mitigate off-resonance artifacts caused by fatty tissue [21]. To improve robustness against breathing motion, both Stars and Spirals employed slap-selective excitation and a variable-density pseudo-Cartesian k-space sampling strategy [22]. This approach sampled five central kz-partitions per block while undersampling the outer partitions, achieving a block size of 60 mm with a spatial spacing of 2.5 mm. Acquisition time and signal-to-noise ratio (SNR) were balanced by using undersampling factors of 3 for Stars and 2 for Spirals, tailored to each sequence's specific requirements. Phantom Experiment Setup Nine cylindrical bioprotein phantoms (12 cm diameter, 6 cm height) encased in a gelatin block (25.5 × 17.5 × 10 cm³) were used to mimic abdominal dimensions and evaluate both Stars and Spirals sequences. Ablation was performed using a clinically approved microwave generator (MWG, ECO-100E2, ECO Medical Technologies) [23]. The experimental setup is depicted in Fig. 1 . Although the MWG employs MR-compatible needles and cables, initial scans were disrupted by electromagnetic interference. RF shielding measures, including chokes, copper tape, and copper mesh on the 4 m cable, were implemented, enabling MR thermometry during MWG operation [24]. All safety measures adhered to clinical standards, maintaining medical approval for the device. The microwave needle was centrally inserted into the bioprotein cylinder. Two fiber optic temperature sensors (FOTEMPTrafo, Weidmann Technologies Deutschland GmbH) were positioned 1.5 to 2.5 cm from the needle tip to provide reference temperatures for MR thermometry evaluation. Breathing motion was simulated using a motorized plunger that compressed the phantom at 11.28 cycles per minute during MR thermometry scans [25]. Each scan consisted of a 3-minute baseline (MWG on standby), a 10-minute ablation at 80 W, and a 3-minute cooling period, resulting in a total scan time of 16 minutes. After ablation, the plunger was stopped in the decompressed state (exhalation). The ablation zone was assessed with a post-ablation T2-weighted Turbo-Spin-Echo (TSE) sequence (Turbo Factor = 7, FOV = 448 × 210 × 60 mm³, resolution = 1 × 1 × 1 mm³, TE = 156 ms, TR = 10,960 ms). A radiologist segmented these images to establish the ground truth for the ablation zone. Volunteer Study Design Ten healthy volunteers (4 females, 6 males; aged > 18 years) provided informed consent to participate. Volunteer scans followed the same protocol as the phantom experiments, employing a 16-minute free-breathing protocol with identical sequence parameters. The 3D imaging volume was positioned within a central hepatic transverse plane for each subject to ensure consistent and optimal liver coverage. Data Processing Pipeline Retrospective processing was performed on all acquired data. Gradient delays were calibrated individually for the Stars sequence before each scan, while spiral trajectory calibration was conducted once for all scans [26, 27]. Motion correction relied on projection profiles derived from kz-direction surrogate signals of respiratory motion. These profiles were calculated by applying a 1D Fourier transform to central kz-sampling points for each radial projection and spiral interleave [28]. Data from approximately one-third of the respiratory cycle near end-expiration were used for reconstruction. Temporal resolution was evaluated by reconstructing each volume with data combined over 1 to 4 breathing cycles (nBC). Reconstructions utilized compressed sensing and parallel imaging (PICS) with the BART toolbox. Sensitivity maps were generated from low-resolution baseline images [29]. Regularization was applied to improve image quality, including total variation (TV) regularization over 3D space (Spirals: 5×10⁻⁴; Stars: 1×10⁻⁵), TV over time (Spirals: 5×10⁻²; Stars: 5×10⁻³), and L2 regularization (Spirals: 5×10⁻³; Stars: 1×10⁻⁶). Temporal regularization was applied by incorporating data from the preceding four reconstructed images. Baseline images were reconstructed using PICS without regularization, integrating all data collected during exhalation within the initial 3 minutes before ablation. Phase drift was estimated through linear regression of phase images in an unheated region of interest (ROI) and corrected by subtracting the global phase drift. Proton resonance frequency shift (PRFS) thermometry was performed by computing phase differences relative to baseline images, with all echoes combined using a weighted sum [30]. Ablation Zone Estimation In the phantom experiments, ablation zones were derived from temperature maps using the cumulative equivalent minutes at 43°C (CEM43) model, applying a 240-minute threshold [31]. Data Analysis and Statistical Tests A range of statistical tests was conducted using Scipy 1.14.1 to validate the results, with statistical significance set at a type I error rate (α) of 0.05 unless otherwise specified. Temperature Precision in Non-Heated Regions Temperature stability in non-heated regions was evaluated by calculating the standard deviation of temperature values over time within a non-heated 3D region of interest (ROI). The Shapiro-Wilk test was used to assess normality, while Levene’s test determined the homogeneity of variances. Based on normality results, Welch’s t-test or the Mann-Whitney U test was applied to compare temperature accuracy between the Spirals and Stars sequences across different nBCs. Additionally, within each sequence, pairwise comparisons across all nBCs were performed using either a paired t-test or a Wilcoxon test, depending on normality. Temperature Precision in Heated Regions Temperature accuracy in heated regions was evaluated by calculating the root mean squared error (RMSE) between MR thermometry measurements and temperature readings from two fiber optic sensors. Sensor positions were visually verified in MR images, and the voxel with the lowest RMSE within a distance of \(\:\sqrt{2}\) voxels of the sensor location was selected for analysis [15]. An Analysis of Covariance (ANCOVA) was performed to compare RMSE values between the Stars and Spirals sequences, adjusting for the distance from the ablation center. All ANCOVA assumptions were satisfied. The dependent variable was RMSE (thermometry vs. sensor), the independent variable was the sequence type (Stars vs. Spirals), and the covariate was the sensor’s distance from the ablation center. RMSE differences across nBCs were analyzed using ANOVA, followed by Tukey’s HSD test if significant. Accuracy of the Calculated Ablation Zones Ablation zones from MR thermometry were compared to ground truth zones segmented from T2-weighted TSE images to assess accuracy. Three metrics were used: Dice Score : Measures the overlap between calculated and ground truth ablation zones. Higher values indicate better overlap: \(\:\text{D}\text{S}\text{C}=\frac{2Tp}{2Tp+Fp+Fn}\) where Tp=true positive, Fp=false positive, Fn=false negative. Mean Surface Distance (MSD) : Reflects the average distance between the surfaces of the calculated and ground truth zones. Lower values indicate better precision: \(\:MSD\:=\:\frac{1}{‖G‖\:+\:\:‖S‖}\:\left(\sum\:_{s\:\in\:S}\underset{{g\:}\in\:G}{\text{min}}‖s\:-g‖\:+\sum\:_{g\:\in\:G}\underset{{s\:}\in\:S}{\text{min}}‖g-s‖\:\right)\) , where S=surface points of the predicted zone and G=surface points of the ground truth. Sensitivity : Represents the proportion of correctly identified ablation, with higher values reflecting improved detection: \(\:\text{S}\text{e}\text{n}\text{s}\text{i}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}=\frac{Tp}{Tp+Fn}\) . Metrics across nBCs were analyzed to evaluate temporal resolution effects. Normality was tested with Shapiro-Wilk, followed by a t-test or Mann-Whitney U based on data distribution. Effect sizes quantified differences between methods. All metrics across nBCs were analyzed with ANOVA, followed by Tukey’s HSD if significant. Temperature Stability in Volunteers Scans In volunteer scans, temperature stability and accuracy were assessed by evaluating the standard deviation of temperature in unheated regions during natural breathing. Three circular regions of interest (ROIs), each 12 voxels in diameter, were manually placed in the liver of each participant, ensuring avoidance of susceptibility artifacts or non-liver structures. ROIs were positioned in caudal, cranial (near the lung diaphragm), and middle slices. Temperature standard deviations for Stars and Spirals sequences across all ROIs were compared using a paired t-test, following normality verification with the Shapiro-Wilk test. If normality was not met, the Wilcoxon signed-rank test was applied. Effect sizes were calculated using Cohen’s d. The influence of nBC on temperature stability within each ROI and sequence was examined using ANOVA, with Tukey’s HSD test applied if significant. Pairwise comparisons of temperature standard deviations between caudal, middle, and cranial ROIs were conducted for each nBC and sequence using t-tests or Wilcoxon tests, depending on normality. Hardware and Reconstruction Performance Reconstruction time was recorded for each nBC. All reconstructions were executed on a high-performance computing server equipped with dual Intel® Xeon® Gold 6342 CPUs (2.80 GHz, 48 cores per processor, 96 threads total), 503 GiB of RAM, and 4 NVIDIA RTX A6000 GPUs (each with 48 GiB VRAM). Despite the availability of multiple GPUs, the BART reconstruction framework utilizes only a single GPU. The system operated on CUDA 12.2 with NVIDIA driver version 535.183.01. The CPU architecture was configured with Non-Uniform Memory Access (NUMA) across two nodes to optimize parallel memory access. Declarations Competing Interests Statement The authors declare no competing financial or non-financial interests. The work presented in this paper was funded by the Federal Ministry of Education and Research within the Forschungscampus STIMULATE. The funding organization had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript. Author Contribution DH, BH, and MG conceptualized and designed the study. DH was primarily responsible for programming the MRI sequences and developing the data processing software. Data acquisition was carried out by DH, MG, OB, DD, and SS. DH and MG performed the data analysis and interpretation. IV and TG provided technical and methodological support, contributing to the refinement of imaging protocols and the interpretation of specific results. DH drafted the manuscript, with critical revisions provided by MG, BH, and FW. All authors reviewed and approved the final manuscript.All authors meet the authorship criteria and have made substantial contributions to the study. Each author agrees to be personally accountable for their contributions and ensures that any questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. DH is the corresponding author and responsible for all communication with the journal. Data Availability The datasets generated and/or analyzed during the current study are not publicly available but can be obtained from the corresponding author upon reasonable request. Requests will be considered on a case-by-case basis. References Sparchez, Z. et al. Microwave ablation in the treatment of liver tumors: A better tool or simply more power? Med Ultrason 22 , 451–460 (2020). https://doi.org/10.11152/mu-2556 Gedroyc, W.M. Magnetic resonance guidance of thermal ablation. Top Magn Reson Imaging 16 , 339–353 (2005). https://doi.org/10.1097/00002142-200510000-00002 Rieke, V., Butts Pauly, K. MR thermometry. J Magn Reson Imaging 27 , 376–390 (2008). https://doi.org/10.1002/jmri.21265 Öcal, O. et al. Predicting liver ablation volumes with real-time MRI thermometry. JHEP Rep 6 , 101199 (2024). https://doi.org/10.1016/j.jhepr.2024.101199 Le Bihan, D., Delannoy, J., Levin R.L. Temperature mapping with MR imaging of molecular diffusion: application to hyperthermia. Radiology 171 , 853–857 (1989). https://doi.org/10.1148/radiology.171.3.2717764 Stollberger, R., Ascher, P.W., Huber, D., Renhart, W., Radner, H., Ebner, F. Temperature monitoring of interstitial thermal tissue coagulation using MR phase images. J Magn Reson Imaging 8 , 188–196 (1998). https://doi.org/10.1002/jmri.1880080132 El-Sharkawy, A.M., Schär, M., Bottomley, P.A., Atalar, E. Monitoring and correcting spatio-temporal variations of the MR scanner's static magnetic field. MAGMA 19 , 223–236 (2006). https://doi.org/10.1007/s10334-006-0050-2 Viallon, M., Terraz, S., Roland, J., Dumont, E., Becker, C.D., Salomir, R. Observation and correction of transient cavitation-induced PRFS thermometry artifacts during radiofrequency ablation, using simultaneous ultrasound/MR imaging. Med Phys 37 , 1491–1506 (2010). https://doi.org/10.1118/1.3309439 De Poorter, J., De Wagter, C., De Deene, Y., Thomsen, C., Ståhlberg, F., Achten, E. Noninvasive MRI thermometry with the proton resonance frequency (PRF) method: in vivo results in human muscle. Magn Reson Med 33 ,74–81 (1995). https://doi.org/10.1002/mrm.1910330111 Ozenne, V., Bour, P., Denis de Senneville, B., Quesson, B. 3D motion strategy for online volumetric thermometry using simultaneous multi-slice EPI at 1.5T: an evaluation study. Int J Hyperthermia 40 , 2194595 (2023). https://doi.org/10.1080/02656736.2023.2194595 Svedin, B.T., Payne, A., Bolster, B.D. Jr., Parker, D.L. Multiecho pseudo-golden angle stack of stars thermometry with high spatial and temporal resolution using k-space weighted image contrast. Magn Reson Med 79 , 1407–1419 (2018). https://doi.org/10.1002/mrm.26797 Lee, J.H., Hargreaves, B.A., Hu, B.S., Nishimura, D.G. Fast 3D imaging using variable-density spiral trajectories with applications to limb perfusion. Magn Reson Med 50 , 1276–1285 (2003). https://doi.org/10.1002/mrm.10644 Marx, M., Butts Pauly, K. Improved MRI thermometry with multiple-echo spirals. Magn Reson Med 76 , 747–756 (2016). https://doi.org/10.1002/mrm.25914 Kim, K., Narsinh, K., Ozhinsky, E. Technical advances in motion-robust MR thermometry. Magn Reson Med 92(1) ,15–27 (2024). https://doi.org/10.1002/mrm.30057 Dietrich, O., Lentini, S., Öcal, O., Bour, P., Faller, T.L., Ozenne, V., Ricke, J., Seidensticker M. Accuracy of 3D real-time MRI temperature mapping in gel phantoms during microwave heating. Eur Radiol Exp 8(1) , 92 (2024). https://doi.org/10.1186/s41747-024-00479-5 Boehm C., Goeger-Neff M., Mulder H.T., Zilles B., Lindner L.H., van Rhoon G.C., Karampinos D.C., Wu M. Susceptibility artifact correction in MR thermometry for monitoring of mild radiofrequency hyperthermia using total field inversion. Magn. Reson. Med. 87 , 2919–2931 (2022). https://doi.org/10.1002/mrm.29191 Nouwens, S. A. N., Paulides, M. M., Fölker, J., VilasBoas-Ribeiro, I., de Jager, B. & Heemels, W. P. M. H. Integrated thermal and magnetic susceptibility modeling for air-motion artifact correction in proton resonance frequency shift thermometry. J. Electromagn. Waves Appl. 36 , 967–976 (2022). https://doi.org/10.1080/02656736.2022.2094475 Wu, M., Mulder, H. T., Baron, P., Coello, E., Menzel, M. I., van Rhoon, G. C. & Haase, A. Correction of motion-induced susceptibility artifacts and B₀ drift during proton resonance frequency shift-based MR thermometry in the pelvis with background field removal methods. Magn. Reson. Med. 84 , 2495–2511 (2020). https://doi.org/10.1002/mrm.28302 Hensen, B., Hellms, S., Werlein, C., Jonigk, D., Gronski, P. A. & Bruesch, I. Correction of heat-induced susceptibility changes in respiratory-triggered 2D-PRF-based thermometry for monitoring of magnetic resonance-guided hepatic microwave ablation in a human-like in vivo porcine model. Int. J. Hyperthermia 39 , 1387–1396 (2022). https://doi.org/10.1080/02656736.2022.2138987 Wundrak, S. et al. Golden ratio sparse MRI using tiny golden angles. Magn Reson Med 75 , 2372–2378 (2016). https://doi.org/10.1002/mrm.25831 Josset, A., Vappou, J., Ishak, O., Cabras, P. & Breton, É. Effectiveness of fat suppression methods and influence on proton-resonance frequency shift (PRFS) MR thermometry. Magn. Reson. Imaging 118 , 110340 (2025). https://doi.org/10.1016/j.mri.2025.110340 Lustig, M., Donoho, D.L., Pauly, J.M. Rapid MR imaging with compressed sensing and randomly under-sampled 3DFT trajectories. In: Proceedings of the 14th Annual Meeting of ISMRM. Seattle, WA (2006). Bu-Lin, Z., Bing, H., Sheng-Li, K., Huang, Y., Rong, W., Jia, L. A polyacrylamide gel phantom for radiofrequency ablation. Int J Hyperthermia 24(7) , 568–576 (2008). https://doi.org/10.1080/02656730802104732 Schröer, S. et al. Reducing electromagnetic interference in MR thermometry: A comparison of setup configurations for MR-guided microwave ablations. Med Phys. (2024) https://doi.org/10.1016/j.zemedi.2024.07.004 Belker, O. et al. MR-thermometry on moving organs by a reproducible respiratory simulation. Presented at the 6th IGIC, Mannheim, Germany (2023). Block, K.T., Uecker, M. Simple method for adaptive gradient-delay compensation in radial MRI. Presented at the 19th Annual Meeting of ISMRM, Montréal, Canada (2011). Duyn, J.H., Yang, Y., Frank, J.A., van der Veen, J.W. Simple correction method for k-space trajectory deviations in MRI. J Magn Reson 132 , 150–153 (1998). https://doi.org/10.1006/jmre.1998.1396 Feng, L., Axel, L., Chandarana, H., Block, K.T., Sodickson, D.K., Otazo, R. XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. Magn Reson Med 75 , 775–788 (2016). https://doi.org/10.1002/mrm.25665 Uecker, M. et al. Berkeley advanced reconstruction toolbox. 23rd Annu. Meeting ISMRM, Toronto, Canada (2015). Madore, B., Panych, L.P., Mei, C.S., Yuan, J., Chu, R. Multipathway sequences for MR thermometry. Magn Reson Med 66(3) , 658–668 (2011). https://doi.org/10.1002/mrm.22844 Pearce J.A. Comparative analysis of mathematical models of cell death and thermal damage processes. Int J Hyperthermia 29(4) , 262–280 (2013). https://doi.org/10.3109/02656736.2013.786140 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviews received at journal 30 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers invited by journal 17 Mar, 2025 Editor assigned by journal 17 Mar, 2025 Editor invited by journal 14 Mar, 2025 Submission checks completed at journal 13 Mar, 2025 First submitted to journal 07 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6176650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430444706,"identity":"26a1e685-3d98-48ee-b78e-19ebc701421a","order_by":0,"name":"Dominik Horstmann","email":"data:image/png;base64,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","orcid":"","institution":"Hannover Medical School","correspondingAuthor":true,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Horstmann","suffix":""},{"id":430444707,"identity":"777be37d-1524-40b6-be22-313992f9a738","order_by":1,"name":"Othmar Belker","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Othmar","middleName":"","lastName":"Belker","suffix":""},{"id":430444708,"identity":"3b692c13-1ea9-40a5-9779-8f53c9df670c","order_by":2,"name":"Daniel Düx","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Düx","suffix":""},{"id":430444709,"identity":"f51ccecf-449c-437a-856d-385fce4f1c2a","order_by":3,"name":"Thomas Gerlach","email":"","orcid":"","institution":"Research Campus STIMULATE Magdeburg","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Gerlach","suffix":""},{"id":430444710,"identity":"5231c119-08ae-4905-8bf0-cf58bc48509b","order_by":4,"name":"Moritz Gutt","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Moritz","middleName":"","lastName":"Gutt","suffix":""},{"id":430444711,"identity":"9e0b5a9e-d485-44ae-b026-7adedd764a59","order_by":5,"name":"Simon Schröer","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Schröer","suffix":""},{"id":430444712,"identity":"d03f5e57-7a64-4589-8e31-7023ad207a5f","order_by":6,"name":"Ivan Vogt","email":"","orcid":"","institution":"Research Campus STIMULATE Magdeburg","correspondingAuthor":false,"prefix":"","firstName":"Ivan","middleName":"","lastName":"Vogt","suffix":""},{"id":430444713,"identity":"a4c80bfa-2d38-46e1-981a-20321163887b","order_by":7,"name":"Frank Wacker","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Wacker","suffix":""},{"id":430444714,"identity":"95e6dcd1-6e87-4604-926c-ba5a0ecac83d","order_by":8,"name":"Bennet Hensen","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Bennet","middleName":"","lastName":"Hensen","suffix":""},{"id":430444715,"identity":"fb171a75-e6fa-4624-8e34-5a9ee474682e","order_by":9,"name":"Marcel Gutberlet","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Marcel","middleName":"","lastName":"Gutberlet","suffix":""}],"badges":[],"createdAt":"2025-03-07 08:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6176650/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6176650/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-20588-4","type":"published","date":"2025-09-24T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78871777,"identity":"4b787b55-a2fc-4b02-afe3-4c11f5c005b5","added_by":"auto","created_at":"2025-03-20 06:08:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental setup for phantom studies\u003c/strong\u003e\u003cbr\u003e\n(A) Gelatin phantom with embedded bioprotein phantom.\u003cbr\u003e\n(B) Bioprotein phantom sliced in half.\u003cbr\u003e\n(C) Complete phantom consisting of a smaller bioprotein-containing gelatin block inserted into a larger gelatin block.\u003cbr\u003e\n(D) Gelatin phantom placed in the motorized plunger setup.\u003cbr\u003e\n(E) Entire experimental setup including the motorized plunger, gelatin phantom with attached coil, shielded microwave cable, and microwave generator (MWG).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6176650/v1/0f8d9a608fbec0dfbec26030.jpg"},{"id":78869683,"identity":"826aebfa-3d22-4f6d-83ee-7ec3ee26f3d4","added_by":"auto","created_at":"2025-03-20 05:36:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":379890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMagnitude images of the phantom during simulated breathing\u003c/strong\u003e\u003cbr\u003e\n(A-D) Magnitude images of the phantom acquired with the Stack-of-Spirals (1st and 2nd row) and Stack-of-Stars (3rd and 4th row) sequence during simulated breathing with temporal resolution of three breathing cycles (nBC3) during baseline (1st column) and shortly before the end (2nd column) of 10 minutes ablation. For both Spirals and Stars, the first (TE = 7.5 ms / 1.7 ms) (1st and 3rd row) and last echo (TE = 14.7 ms / 14.9 ms) (2nd and 4th row) is given.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6176650/v1/dc1a34a9f85d602850779217.jpg"},{"id":78869690,"identity":"8f2de1e2-3848-4757-9f5d-8db2cde5e153","added_by":"auto","created_at":"2025-03-20 05:36:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemperature precision in heated areas\u003c/strong\u003e\u003cbr\u003e\nTransversal slice from a 3D temperature map acquired with the Stack-of-Spirals (A) and Stack-of-Stars (C) sequence over two breathing cycles (nBC2), including both temperature sensors (marked by white crosses), captured shortly before the end of the 10-minute ablation. Temperature profiles for the two sensors (S1/S2) in solid lines and the corresponding thermometry (T1/T2) in dotted lines for Spirals (B) and Stars (D). Distance d of the sensors to the center of the ablation zone and root Mean Squared Error (RMSE) between the sensors and thermometry are indicated in the legend. Scatter plots of RMSE results dependent on d for each nBC using Spirals (E) and Stars (F). Linear regression lines are fitted to the data, with correlation coefficient (r), significance (p-value), and slope (m) in °C/mm detailed in the legend.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6176650/v1/18e0298bee37580a11020de1.jpg"},{"id":78869684,"identity":"6dbd89ce-0613-438e-b5a9-98aa7bc35d9b","added_by":"auto","created_at":"2025-03-20 05:36:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalculated ablation zones overlaid with ground truth\u003c/strong\u003e\u003cbr\u003e\nTransverse slices of the calculated ablation zones overlaid with the ground truth, with black, white, orange, and cyan areas representing true negatives, true positives, false positives, and false negatives for Stack-of-Spirals (first row) and Stack-of-Stars (2nd row). Slices are taken at different distances to the ablation center.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6176650/v1/98071260e4f5149f3fd46b5b.jpg"},{"id":78871275,"identity":"abdb29bf-d21d-4246-b415-7d7e388b1fb9","added_by":"auto","created_at":"2025-03-20 06:00:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":277328,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMagnitude images and temperature maps of a volunteer\u003c/strong\u003e\u003cbr\u003e\n(A) Transverse slice of the temperature map and magnitude image of the first echo from a volunteer acquired with the Stack-of-Spirals (1st row) and Stack-of-Stars (2nd row) sequence at a temporal resolution of four breathing cycles (nBC4).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6176650/v1/025eaf98125506b873bd3166.jpg"},{"id":92430598,"identity":"4f2d2c89-c085-4e07-9a6a-f6a2febfc68e","added_by":"auto","created_at":"2025-09-29 16:06:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2408659,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6176650/v1/0e9bf919-8139-4bbb-bc60-e03f04f04558.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"3D Proton Resonance Frequency Shift MR Thermometry for Monitoring Clinical Microwave Ablation: Comparison of Stack-of-Stars and Stack-of-Spirals Sequences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicrowave ablation (MWA) is a widely used thermal therapy that destroys tumor tissues by applying heat over larger areas compared to other modalities [1]. Unlike ultrasound and computed tomography (CT), magnetic resonance imaging (MRI) offers real-time monitoring of temperature changes during procedures through MR thermometry, making it a valuable tool for precise treatment guidance [2].\u003c/p\u003e \u003cp\u003ePrecise temperature monitoring during ablation is crucial for assessing tissue damage and guiding decisions on treatment continuation, duration, and targeting. MR thermometry has been shown to predict post-procedural ablation zones accurately, enabling real-time evaluation of treatment outcomes. This capability reduces recurrence risk by allowing immediate re-ablation of under-treated areas and protects nearby sensitive structures, such as the bowel [3, 4].\u003c/p\u003e \u003cp\u003eAmong MR-based techniques, proton resonance frequency shift (PRFS) thermometry is widely used in clinical applications due to its linear temperature dependence and minimal sensitivity to tissue-specific variations, except in adipose tissue [5]. By leveraging phase variations in conventional MRI sequences, PRFS thermometry achieves high spatial and temporal resolution, making it suitable for real-time monitoring during thermal therapies. However, it remains susceptible to motion, phase drift, and susceptibility changes induced by temperature fluctuations, tissue transitions, or gas formation during ablation [6, 7, 8].\u003c/p\u003e \u003cp\u003eFor PRFS thermometry to be clinically effective, it must combine motion robustness, high sensitivity, and minimal susceptibility to artifacts, while maintaining sufficient spatial and temporal resolution [9]. Achieving this balance remains a major challenge in abdominal imaging.\u003c/p\u003e \u003cp\u003eWhile 2D and multi-slice 2D MR thermometry have been clinically applied, they are often limited by restricted coverage and susceptibility to motion artifacts, particularly in dynamic organs like the liver [10]. To address these challenges, 3D thermometry approaches are under investigation. Among these, multi-echo Stack-of-Stars (Stars) and Stack-of-Spirals (Spirals) acquisitions show promise, with Stars offering improved motion robustness through radial readouts and Spirals enabling higher spatial and temporal resolution through efficient k-space sampling [11, 12, 13]. This study aims to identify a suitable 3D PRFS-based MR thermometry sequence for hepatic microwave ablation by assessing the feasibility, accuracy, and clinical applicability of Stack-of-Stars and Stack-of-Spirals in phantom and volunteer experiments.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhantom study: Analysis of magnitude images and undersampling\u003c/h2\u003e \u003cp\u003eOn average, the Stack-of-Stars (Stars) sequence utilized 13 spokes per breath, each with 8 partitions across 7 echoes, while the Stack-of-Spirals (Spirals) sequence employed 7 interleaves per breath, each with 12 partitions and 2 echoes. For a 480 mm field of view (FOV), as used for the phantoms, the resulting undersampling factors ranged from 8.6 (1 breathing cycle, nBC1) to 2.1 (4 breathing cycles, nBC4) for Spirals and from 69.5 (nBC1) to 17.4 (nBC4) for Stars, where nBC denotes the number of breathing cycles per reconstructed image.\u003c/p\u003e \u003cp\u003eThese differences in undersampling were reflected in the image quality as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which compares magnitude images from Spirals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D) and Stars (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-H) at the smallest and largest echo times (TE). Images acquired with Stars exhibited noticeable streaking artifacts caused by more aggressive undersampling, while those obtained with Spirals were largely free of artifacts, demonstrating cleaner and more consistent quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs TE increased, signal loss around the needle due to T2* effects became more pronounced, enlarging the needle artifact. This effect was further exacerbated by heating, leading to additional signal loss. Due to its shorter minimum TE of 1.7 ms, Stars exhibited a smaller needle artifact and less signal loss at baseline and during heating compared to Spirals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTemperature Precision in Unheated Regions\u003c/h3\u003e\n\u003cp\u003eTemperature accuracy in unheated regions differed significantly across all examined nBCs. The Stack-of-Spirals (Spirals) sequence consistently exhibited superior temperature precision, with mean temperature standard deviations ranging from 0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u0026deg;C (nBC4) to 1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u0026deg;C (nBC1). In comparison, the Stack-of-Stars (Stars) sequence showed higher mean temperature standard deviations, ranging from 2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u0026deg;C (nBC4) to 4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u0026deg;C (nBC1).\u003c/p\u003e \u003cp\u003ePairwise comparisons across all nBCs indicated a progressive and statistically significant reduction in mean temperature standard deviations with increasing nBC for both sequences. This trend demonstrated improved temperature stability at lower temporal resolutions. Notably, comparisons between nBC1 and nBC4 revealed p-values of 0.0078 for Stars and \u0026lt;\u0026thinsp;0.0001 for Spirals, emphasizing the robustness of Spirals in achieving consistent precision.\u003c/p\u003e\n\u003ch3\u003eTemperature Precision in Heated Regions\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e (A-D) presents representative transverse slices from 3D temperature maps and corresponding temperature profiles from a phantom during ablation using Stars and Spirals, compared to data from the temperature sensors. Scatter plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F) depict the relationship between Root Mean Squared Error (RMSE) and the distance from the ablation center (\u0026#119889;), revealing that temperature accuracy, represented by RMSE, improves with increasing distance from the ablation center, a trend seen across all nBCs. Notably, the linear regression fits are significant, except for the nBC4 fit for Spirals, which is near significance. The regression lines for Spirals consistently lie below those for Stars, indicating superior temperature accuracy in heated regions for Spirals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Analysis of Covariance (ANCOVA) results confirmed significant differences in RMSE between Stars and Spirals for nBC2 (F\u0026thinsp;=\u0026thinsp;11.06, p\u0026thinsp;=\u0026thinsp;0.0025), nBC3 (F\u0026thinsp;=\u0026thinsp;12.30, p\u0026thinsp;=\u0026thinsp;0.0015), and nBC4 (F\u0026thinsp;=\u0026thinsp;16.72, p\u0026thinsp;=\u0026thinsp;0.0003), supporting Spirals' superior temperature accuracy across these conditions. The distance from the ablation center significantly influenced RMSE across all nBCs (e.g., F\u0026thinsp;=\u0026thinsp;29.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for nBC2), highlighting its critical role in temperature measurement accuracy.\u003c/p\u003e \u003cp\u003eThe interaction term between method and distance was not significant for any nBC (e.g., F\u0026thinsp;=\u0026thinsp;0.13, p\u0026thinsp;=\u0026thinsp;0.7180 for nBC2), suggesting that the RMSE-distance relationship remained consistent across both methods. Analysis of Variance (ANOVA) showed a significant effect on RMSE for Spirals (F\u0026thinsp;=\u0026thinsp;3.91, p\u0026thinsp;=\u0026thinsp;0.013), with Tukey's HSD revealing a significant difference only between nBC1 and nBC2. No significant effect was found for Stars (F\u0026thinsp;=\u0026thinsp;1.12, p\u0026thinsp;=\u0026thinsp;0.349).\u003c/p\u003e\n\u003ch3\u003ePrecision of the Calculated Ablation Zone\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the comparison between the calculated ablation zones and the ground truth for a representative phantom. Both Stars and Spirals demonstrated close alignment with the ground truth. However, near the ablation center, Spirals exhibited more false positives than Stars, indicating a slight overestimation of the ablation zone. Conversely, in peripheral regions, particularly for Stars, false negatives were more prevalent, reflecting an underestimation of the ablation zone boundaries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetrics Comparing Ablation Zone Accuracy between Stack-of-Stars and Stack-of-Spirals across Temporal Resolutions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003enBC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003enBC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003enBC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003enBC4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eDice\u0026thinsp;=\u0026thinsp;Dice Score, ES\u0026thinsp;=\u0026thinsp;Effect Size, MSD\u0026thinsp;=\u0026thinsp;Mean Surface Distance, \u0026micro;\u0026thinsp;=\u0026thinsp;Mean Value, nBC\u0026thinsp;=\u0026thinsp;Number of Breathing Cycles, σ\u0026thinsp;=\u0026thinsp;Standard Deviation, Spirals\u0026thinsp;=\u0026thinsp;Stack-of-Spirals, Stars\u0026thinsp;=\u0026thinsp;Stack-of-Stars\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis observation is corroborated by the statistical metrics presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Across all nBCs, Spirals consistently outperformed Stars, as evidenced by higher Dice scores, lower Mean Surface Differences (MSD), and greater Sensitivity values, confirming Spirals' superior accuracy in delineating the ablation zone. All differences were statistically significant, with large effect sizes.\u003c/p\u003e \u003cp\u003eANOVA results revealed no significant differences across nBCs for any metric (Dice, MSD, Sensitivity) in either Spirals or Stars, suggesting that temporal resolution did not affect the accuracy of ablation zone measurements. Consequently, no further post hoc comparisons were performed.\u003c/p\u003e\n\u003ch3\u003eVolunteer Study\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents representative temperature maps for a transverse slice acquired with (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) Spirals and (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) Stars, alongside corresponding magnitude images (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. C and D). Both sequences exhibited significant susceptibility artifacts in the temperature maps, primarily due to bowel motion. The magnitude images for Stars displayed more pronounced artifacts, whereas Spirals images provided enhanced anatomical detail, including sharper visualization of vessels and reduced blurriness. The effectiveness of fat saturation in Spirals was evident,\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ewith subcutaneous fat\u0026mdash;bright in Stars\u0026mdash;exhibiting minimal signal in Spirals.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemperature Standard Deviation in Liver Regions across Temporal Resolutions with Statistical Comparisons of Stack-of-Stars and Stack-of-Spirals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003enBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCaudal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eCranial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ [\u0026deg;C]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ [\u0026deg;C]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026micro;\u0026thinsp;\u0026plusmn;\u0026thinsp;σ [\u0026deg;C]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpirals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eES\u0026thinsp;=\u0026thinsp;Effect Sizes, \u0026micro;\u0026thinsp;=\u0026thinsp;Mean Value of Temperature Standard Deviation over Time, nBC\u0026thinsp;=\u0026thinsp;Number of Breathing Cycles, σ\u0026thinsp;=\u0026thinsp;Standard Deviation of Temperature Standard Deviation over Time, Spirals\u0026thinsp;=\u0026thinsp;Stack-of-Spirals, Stars\u0026thinsp;=\u0026thinsp;Stack-of-Stars\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights significant differences in temperature standard deviation (STD) between Spirals and Stars across the caudal, middle, and cranial liver regions. In the caudal region, Spirals showed significantly lower STD for nBC2 and nBC3, reflecting improved temperature stability. In the middle region, Spirals consistently outperformed Stars across all nBCs except nBC4. Similarly, in the cranial region, Spirals demonstrated superior performance with significantly lower STD for nBC2 and nBC3, emphasizing its ability to provide more stable temperature readings.\u003c/p\u003e \u003cp\u003eANOVA for Spirals indicated no significant differences in the caudal (p\u0026thinsp;=\u0026thinsp;0.125) and middle (p\u0026thinsp;=\u0026thinsp;0.089) ROIs but revealed a significant effect in the cranial ROI (p\u0026thinsp;=\u0026thinsp;0.039). However, post hoc Tukey's HSD comparisons showed no significant pairwise differences. For Stars, ANOVA revealed no significant differences in any ROI (caudal: p\u0026thinsp;=\u0026thinsp;0.108, middle: p\u0026thinsp;=\u0026thinsp;0.288, cranial: p\u0026thinsp;=\u0026thinsp;0.649). Pairwise comparisons of STD between caudal, middle, and cranial regions identified a significant difference between the caudal and cranial regions for Spirals at nBC4 (p\u0026thinsp;=\u0026thinsp;0.05, ES = -0.38), indicating a small to moderate effect size. No other significant pairwise differences were observed for either method. Effect sizes were generally small, suggesting subtle regional variations in temperature stability.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReconstruction Time\u003c/h2\u003e \u003cp\u003eThe reconstruction times for one 3D image for Spirals were 37 s (nBC4), 36 s (nBC3), 35 s (nBC2), and 33 s (nBC1), and for Stars 44 s (nBC4), 43 s (nBC3), 42 s (nBC2), and 40 s (nBC1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that the Stack-of-Spirals sequence consistently outperformed the Stack-of-Stars sequence in 3D PRFS-based MR thermometry. Key advantages of Spirals included superior temperature precision, lower root mean squared error (RMSE) in heated regions, and more accurate delineation of ablation zones, reflected by higher Dice scores, lower Mean Surface Distance (MSD), and greater sensitivity values. These findings were consistent across phantom and volunteer experiments. This establishes the Stack-of-Spirals sequence as a promising approach for clinical applications of MR thermometry, particularly in hepatic microwave ablation.\u003c/p\u003e \u003cp\u003eThe findings of this study align with prior research on MR thermometry: Marx et al. achieved temperature accuracies below 0.5\u0026deg;C in the brain with spiral sequences and high spatial resolution (\u0026lt;\u0026thinsp;1.5 mm) [13]. While these results demonstrate excellent precision, they were achieved in static 2D brain imaging, a simpler context compared to the challenges of 3D imaging in a moving liver. Similarly, Kim et al. reported liver temperature accuracies of 1\u0026ndash;2\u0026deg;C using a multi-baseline strategy in 2D imaging, suggesting potential improvements for 3D imaging if such methods are adapted [14]. Their limited 2D coverage (three 5-mm slices) underscores the advantage of the 60-mm volumetric coverage in this study for larger ablation zones.\u003c/p\u003e \u003cp\u003eDietrich et al. achieved sub-1\u0026deg;C precision using an EPI sequence across 25 slices in static phantom experiments [15]. While their findings provide valuable insights, the absence of motion artifacts in their setup limits their clinical relevance. Ozenne et al. employed SMS-EPI sequences to achieve good spatial and temporal resolutions in 2D volunteer scans, with temperature accuracies of 2\u0026deg;C in unheated regions and below 1\u0026deg;C in phantom ablation zones [10]. Their results are comparable to this study but remain restricted to 2D imaging, which may not fully capture larger ablation zones. EPI-based methods are also more sensitive to magnetic field inhomogeneities, a challenge mitigated here by shorter echo times and spiral/radial sampling strategies.\u003c/p\u003e \u003cp\u003eOverall, this study builds upon existing literature by demonstrating a robust 3D thermometry approach that offers comparable accuracy to 2D methods while addressing motion and susceptibility challenges in the liver.\u003c/p\u003e \u003cp\u003eDespite its promising results, this study has notable limitations. Reconstruction times of 33\u0026ndash;40 seconds per 3D image are insufficient for real-time clinical application and stem from reliance on a single GPU and the absence of temporal regularization in the reconstruction pipeline. Optimizing GPU usage and introducing more efficient reconstruction algorithms are critical next steps. Susceptibility artifacts, especially near air-tissue interfaces and gas bubbles during ablation, remain a challenge. Implementing susceptibility correction methods could enhance accuracy in these areas [16\u0026ndash;19]. Furthermore, the volunteer study was conducted under free-breathing conditions without thermal treatment, which may not fully capture the complexities of clinical ablation scenarios.\u003c/p\u003e \u003cp\u003eFuture work should focus on enhancing the clinical feasibility of the Stack-of-Spirals sequence by addressing current limitations. Real-time reconstruction could be achieved through more efficient use of GPU resources, stronger hardware, and advanced machine learning algorithms. Motion artifacts near the lung diaphragm or the bowel require innovative solutions such as referenceless thermometry and advanced motion correction [14]. Incorporating a multi-baseline strategy, as demonstrated in previous studies, as well as susceptibility correction could further enhance temperature accuracy. Finally, a clinical validation study applying the Stack-of-Spirals sequence during actual microwave ablation procedures is essential to confirm its reliability and utility in guiding real-time treatment decisions.\u003c/p\u003e \u003cp\u003eThis study demonstrates that the Stack-of-Spirals sequence is a promising approach for real-time 3D PRFS-based MR thermometry in the liver. With a Dice score near 90% and temperature accuracies below 3\u0026deg;C at a temporal resolution of one breathing cycle, it holds potential for clinical application in monitoring hepatic microwave ablation. Future efforts should focus on addressing susceptibility artifacts, enhancing motion robustness, and accelerating reconstruction times to enable real-time use and broader clinical adoption.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This study was approved by the Institutional Review Board (IRB) of Hannover Medical School (Approval No. 11019_B0_S_2023) and was conducted in accordance with the principles of the Declaration of Helsinki and relevant national and institutional guidelines. All procedures involving human participants were performed in accordance with the ethical standards of the Hannover Medical School Ethics Committee and the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e \u003cp\u003eThe aim was to evaluate the performance of Stack-of-Stars (Stars) and Stack-of-Spirals (Spirals) sequences in abdominal 3D PRFS-based MR thermometry. Phantom experiments simulated controlled conditions, while volunteer experiments assessed sequence performance under free-breathing conditions.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSequence Implementation\u003c/h2\u003e \u003cp\u003eThe Stack-of-Stars (Stars) sequence was implemented as a multi-echo spoiled gradient echo with a 4th-order tiny golden angle (38.98\u0026deg;) increment between projections [20]. Projections were rotated after completing Cartesian kz-sampling. The sequence used seven bipolar echoes with alternating gradient polarity per readout, featuring echo times (TE) from 1.7 ms to 14.9 ms and a repetition time (TR) of 17.3 ms. Other parameters included a field of view (FOV) of 480 \u0026times; 480 \u0026times; 60 mm\u0026sup3;, a spatial resolution of 2.5 \u0026times; 2.5 \u0026times; 2.5 mm\u0026sup3;, and a bandwidth of 520 Hz/pixel.\u003c/p\u003e \u003cp\u003eFor the Stack-of-Spirals (Spirals) sequence, a dual-echo spiral-in/spiral-in spoiled gradient echo was implemented with a 12th-order tiny golden angle increment (14.27\u0026deg;). The sequence parameters were TE\u0026thinsp;=\u0026thinsp;7.5 ms/14.5 ms, TR\u0026thinsp;=\u0026thinsp;22.6 ms, and a bandwidth of 1040 Hz/pixel. Spiral rotation was applied after completing Cartesian kz-sampling. A variable-density spiral was employed (maximum gradient amplitude: 7.6 mT/m, max. slew rate: 174.0 mT/m/ms), requiring 30 interleaves for Nyquist sampling. The FOV increased linearly from 412 mm to 548 mm, with a spatial resolution of 2.5 mm LEE. Fat saturation was applied every 3rd TR to mitigate off-resonance artifacts caused by fatty tissue [21].\u003c/p\u003e \u003cp\u003eTo improve robustness against breathing motion, both Stars and Spirals employed slap-selective excitation and a variable-density pseudo-Cartesian k-space sampling strategy [22]. This approach sampled five central kz-partitions per block while undersampling the outer partitions, achieving a block size of 60 mm with a spatial spacing of 2.5 mm. Acquisition time and signal-to-noise ratio (SNR) were balanced by using undersampling factors of 3 for Stars and 2 for Spirals, tailored to each sequence's specific requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePhantom Experiment Setup\u003c/h2\u003e \u003cp\u003eNine cylindrical bioprotein phantoms (12 cm diameter, 6 cm height) encased in a gelatin block (25.5 \u0026times; 17.5 \u0026times; 10 cm\u0026sup3;) were used to mimic abdominal dimensions and evaluate both Stars and Spirals sequences. Ablation was performed using a clinically approved microwave generator (MWG, ECO-100E2, ECO Medical Technologies) [23]. The experimental setup is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Although the MWG employs MR-compatible needles and cables, initial scans were disrupted by electromagnetic interference. RF shielding measures, including chokes, copper tape, and copper mesh on the 4 m cable, were implemented, enabling MR thermometry during MWG operation [24]. All safety measures adhered to clinical standards, maintaining medical approval for the device.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe microwave needle was centrally inserted into the bioprotein cylinder. Two fiber optic temperature sensors (FOTEMPTrafo, Weidmann Technologies Deutschland GmbH) were positioned 1.5 to 2.5 cm from the needle tip to provide reference temperatures for MR thermometry evaluation. Breathing motion was simulated using a motorized plunger that compressed the phantom at 11.28 cycles per minute during MR thermometry scans [25]. Each scan consisted of a 3-minute baseline (MWG on standby), a 10-minute ablation at 80 W, and a 3-minute cooling period, resulting in a total scan time of 16 minutes. After ablation, the plunger was stopped in the decompressed state (exhalation). The ablation zone was assessed with a post-ablation T2-weighted Turbo-Spin-Echo (TSE) sequence (Turbo Factor\u0026thinsp;=\u0026thinsp;7, FOV\u0026thinsp;=\u0026thinsp;448 \u0026times; 210 \u0026times; 60 mm\u0026sup3;, resolution\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u0026sup3;, TE\u0026thinsp;=\u0026thinsp;156 ms, TR\u0026thinsp;=\u0026thinsp;10,960 ms). A radiologist segmented these images to establish the ground truth for the ablation zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eVolunteer Study Design\u003c/h2\u003e \u003cp\u003eTen healthy volunteers (4 females, 6 males; aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years) provided informed consent to participate. Volunteer scans followed the same protocol as the phantom experiments, employing a 16-minute free-breathing protocol with identical sequence parameters. The 3D imaging volume was positioned within a central hepatic transverse plane for each subject to ensure consistent and optimal liver coverage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData Processing Pipeline\u003c/h2\u003e \u003cp\u003eRetrospective processing was performed on all acquired data. Gradient delays were calibrated individually for the Stars sequence before each scan, while spiral trajectory calibration was conducted once for all scans [26, 27]. Motion correction relied on projection profiles derived from kz-direction surrogate signals of respiratory motion. These profiles were calculated by applying a 1D Fourier transform to central kz-sampling points for each radial projection and spiral interleave [28]. Data from approximately one-third of the respiratory cycle near end-expiration were used for reconstruction. Temporal resolution was evaluated by reconstructing each volume with data combined over 1 to 4 breathing cycles (nBC). Reconstructions utilized compressed sensing and parallel imaging (PICS) with the BART toolbox. Sensitivity maps were generated from low-resolution baseline images [29]. Regularization was applied to improve image quality, including total variation (TV) regularization over 3D space (Spirals: 5\u0026times;10⁻⁴; Stars: 1\u0026times;10⁻⁵), TV over time (Spirals: 5\u0026times;10⁻\u0026sup2;; Stars: 5\u0026times;10⁻\u0026sup3;), and L2 regularization (Spirals: 5\u0026times;10⁻\u0026sup3;; Stars: 1\u0026times;10⁻⁶).\u003c/p\u003e \u003cp\u003eTemporal regularization was applied by incorporating data from the preceding four reconstructed images. Baseline images were reconstructed using PICS without regularization, integrating all data collected during exhalation within the initial 3 minutes before ablation. Phase drift was estimated through linear regression of phase images in an unheated region of interest (ROI) and corrected by subtracting the global phase drift. Proton resonance frequency shift (PRFS) thermometry was performed by computing phase differences relative to baseline images, with all echoes combined using a weighted sum [30].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAblation Zone Estimation\u003c/h2\u003e \u003cp\u003eIn the phantom experiments, ablation zones were derived from temperature maps using the cumulative equivalent minutes at 43\u0026deg;C (CEM43) model, applying a 240-minute threshold [31].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis and Statistical Tests\u003c/h2\u003e \u003cp\u003eA range of statistical tests was conducted using Scipy 1.14.1 to validate the results, with statistical significance set at a type I error rate (α) of 0.05 unless otherwise specified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTemperature Precision in Non-Heated Regions\u003c/h2\u003e \u003cp\u003eTemperature stability in non-heated regions was evaluated by calculating the standard deviation of temperature values over time within a non-heated 3D region of interest (ROI). The Shapiro-Wilk test was used to assess normality, while Levene\u0026rsquo;s test determined the homogeneity of variances. Based on normality results, Welch\u0026rsquo;s t-test or the Mann-Whitney U test was applied to compare temperature accuracy between the Spirals and Stars sequences across different nBCs. Additionally, within each sequence, pairwise comparisons across all nBCs were performed using either a paired t-test or a Wilcoxon test, depending on normality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTemperature Precision in Heated Regions\u003c/h2\u003e \u003cp\u003eTemperature accuracy in heated regions was evaluated by calculating the root mean squared error (RMSE) between MR thermometry measurements and temperature readings from two fiber optic sensors. Sensor positions were visually verified in MR images, and the voxel with the lowest RMSE within a distance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{2}\\)\u003c/span\u003e\u003c/span\u003e voxels of the sensor location was selected for analysis [15]. An Analysis of Covariance (ANCOVA) was performed to compare RMSE values between the Stars and Spirals sequences, adjusting for the distance from the ablation center. All ANCOVA assumptions were satisfied. The dependent variable was RMSE (thermometry vs. sensor), the independent variable was the sequence type (Stars vs. Spirals), and the covariate was the sensor\u0026rsquo;s distance from the ablation center. RMSE differences across nBCs were analyzed using ANOVA, followed by Tukey\u0026rsquo;s HSD test if significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAccuracy of the Calculated Ablation Zones\u003c/h2\u003e \u003cp\u003eAblation zones from MR thermometry were compared to ground truth zones segmented from T2-weighted TSE images to assess accuracy. Three metrics were used:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDice Score\u003c/b\u003e: Measures the overlap between calculated and ground truth ablation zones. Higher values indicate better overlap: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{D}\\text{S}\\text{C}=\\frac{2Tp}{2Tp+Fp+Fn}\\)\u003c/span\u003e\u003c/span\u003e where Tp=true positive, Fp=false positive, Fn=false negative.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMean Surface Distance (MSD)\u003c/b\u003e: Reflects the average distance between the surfaces of the calculated and ground truth zones. Lower values indicate better precision: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MSD\\:=\\:\\frac{1}{‖G‖\\:+\\:\\:‖S‖}\\:\\left(\\sum\\:_{s\\:\\in\\:S}\\underset{{g\\:}\\in\\:G}{\\text{min}}‖s\\:-g‖\\:+\\sum\\:_{g\\:\\in\\:G}\\underset{{s\\:}\\in\\:S}{\\text{min}}‖g-s‖\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e, where S=surface points of the predicted zone and G=surface points of the ground truth.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSensitivity\u003c/b\u003e: Represents the proportion of correctly identified ablation, with higher values reflecting improved detection: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{S}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{i}\\text{t}\\text{y}=\\frac{Tp}{Tp+Fn}\\)\u003c/span\u003e\u003c/span\u003e .\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eMetrics across nBCs were analyzed to evaluate temporal resolution effects. Normality was tested with Shapiro-Wilk, followed by a t-test or Mann-Whitney U based on data distribution. Effect sizes quantified differences between methods. All metrics across nBCs were analyzed with ANOVA, followed by Tukey\u0026rsquo;s HSD if significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTemperature Stability in Volunteers Scans\u003c/h2\u003e \u003cp\u003eIn volunteer scans, temperature stability and accuracy were assessed by evaluating the standard deviation of temperature in unheated regions during natural breathing. Three circular regions of interest (ROIs), each 12 voxels in diameter, were manually placed in the liver of each participant, ensuring avoidance of susceptibility artifacts or non-liver structures. ROIs were positioned in caudal, cranial (near the lung diaphragm), and middle slices. Temperature standard deviations for Stars and Spirals sequences across all ROIs were compared using a paired t-test, following normality verification with the Shapiro-Wilk test. If normality was not met, the Wilcoxon signed-rank test was applied. Effect sizes were calculated using Cohen\u0026rsquo;s d. The influence of nBC on temperature stability within each ROI and sequence was examined using ANOVA, with Tukey\u0026rsquo;s HSD test applied if significant. Pairwise comparisons of temperature standard deviations between caudal, middle, and cranial ROIs were conducted for each nBC and sequence using t-tests or Wilcoxon tests, depending on normality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHardware and Reconstruction Performance\u003c/h2\u003e \u003cp\u003eReconstruction time was recorded for each nBC. All reconstructions were executed on a high-performance computing server equipped with dual Intel\u0026reg; Xeon\u0026reg; Gold 6342 CPUs (2.80 GHz, 48 cores per processor, 96 threads total), 503 GiB of RAM, and 4 NVIDIA RTX A6000 GPUs (each with 48 GiB VRAM). Despite the availability of multiple GPUs, the BART reconstruction framework utilizes only a single GPU. The system operated on CUDA 12.2 with NVIDIA driver version 535.183.01. The CPU architecture was configured with Non-Uniform Memory Access (NUMA) across two nodes to optimize parallel memory access.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial or non-financial interests. The work presented in this paper was funded by the Federal Ministry of Education and Research within the Forschungscampus STIMULATE. The funding organization had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDH, BH, and MG conceptualized and designed the study. DH was primarily responsible for programming the MRI sequences and developing the data processing software. Data acquisition was carried out by DH, MG, OB, DD, and SS. DH and MG performed the data analysis and interpretation. IV and TG provided technical and methodological support, contributing to the refinement of imaging protocols and the interpretation of specific results. DH drafted the manuscript, with critical revisions provided by MG, BH, and FW. All authors reviewed and approved the final manuscript.All authors meet the authorship criteria and have made substantial contributions to the study. Each author agrees to be personally accountable for their contributions and ensures that any questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. DH is the corresponding author and responsible for all communication with the journal.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available but can be obtained from the corresponding author upon reasonable request. Requests will be considered on a case-by-case basis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSparchez, Z. et al. Microwave ablation in the treatment of liver tumors: A better tool or simply more power? \u003cem\u003eMed Ultrason\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 451\u0026ndash;460 (2020). https://doi.org/10.11152/mu-2556 \u003c/li\u003e\n\u003cli\u003eGedroyc, W.M. Magnetic resonance guidance of thermal ablation. \u003cem\u003eTop Magn Reson Imaging\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 339\u0026ndash;353 (2005). https://doi.org/10.1097/00002142-200510000-00002 \u003c/li\u003e\n\u003cli\u003eRieke, V., Butts Pauly, K. MR thermometry. \u003cem\u003eJ Magn Reson Imaging\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 376\u0026ndash;390 (2008). https://doi.org/10.1002/jmri.21265 \u003c/li\u003e\n\u003cli\u003e\u0026Ouml;cal, O. et al. Predicting liver ablation volumes with real-time MRI thermometry. \u003cem\u003eJHEP Rep\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 101199 (2024). https://doi.org/10.1016/j.jhepr.2024.101199 \u003c/li\u003e\n\u003cli\u003eLe Bihan, D., Delannoy, J., Levin R.L. Temperature mapping with MR imaging of molecular diffusion: application to hyperthermia. \u003cem\u003eRadiology\u003c/em\u003e \u003cstrong\u003e171\u003c/strong\u003e, 853\u0026ndash;857 (1989). https://doi.org/10.1148/radiology.171.3.2717764 \u003c/li\u003e\n\u003cli\u003eStollberger, R., Ascher, P.W., Huber, D., Renhart, W., Radner, H., Ebner, F. Temperature monitoring of interstitial thermal tissue coagulation using MR phase images. J Magn Reson Imaging \u003cstrong\u003e8\u003c/strong\u003e, 188\u0026ndash;196 (1998). https://doi.org/10.1002/jmri.1880080132 \u003c/li\u003e\n\u003cli\u003eEl-Sharkawy, A.M., Sch\u0026auml;r, M., Bottomley, P.A., Atalar, E. Monitoring and correcting spatio-temporal variations of the MR scanner\u0026apos;s static magnetic field. \u003cem\u003eMAGMA\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 223\u0026ndash;236 (2006). https://doi.org/10.1007/s10334-006-0050-2 \u003c/li\u003e\n\u003cli\u003eViallon, M., Terraz, S., Roland, J., Dumont, E., Becker, C.D., Salomir, R. Observation and correction of transient cavitation-induced PRFS thermometry artifacts during radiofrequency ablation, using simultaneous ultrasound/MR imaging. \u003cem\u003eMed Phys\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 1491\u0026ndash;1506 (2010). https://doi.org/10.1118/1.3309439 \u003c/li\u003e\n\u003cli\u003eDe Poorter, J., De Wagter, C., De Deene, Y., Thomsen, C., St\u0026aring;hlberg, F., Achten, E. Noninvasive MRI thermometry with the proton resonance frequency (PRF) method: in vivo results in human muscle. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e,74\u0026ndash;81 (1995). https://doi.org/10.1002/mrm.1910330111 \u003c/li\u003e\n\u003cli\u003eOzenne, V., Bour, P., Denis de Senneville, B., Quesson, B. 3D motion strategy for online volumetric thermometry using simultaneous multi-slice EPI at 1.5T: an evaluation study. \u003cem\u003eInt J Hyperthermia\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 2194595 (2023). https://doi.org/10.1080/02656736.2023.2194595 \u003c/li\u003e\n\u003cli\u003eSvedin, B.T., Payne, A., Bolster, B.D. Jr., Parker, D.L. Multiecho pseudo-golden angle stack of stars thermometry with high spatial and temporal resolution using k-space weighted image contrast. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 1407\u0026ndash;1419 (2018). https://doi.org/10.1002/mrm.26797 \u003c/li\u003e\n\u003cli\u003eLee, J.H., Hargreaves, B.A., Hu, B.S., Nishimura, D.G. Fast 3D imaging using variable-density spiral trajectories with applications to limb perfusion. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 1276\u0026ndash;1285 (2003). https://doi.org/10.1002/mrm.10644 \u003c/li\u003e\n\u003cli\u003eMarx, M., Butts Pauly, K. Improved MRI thermometry with multiple-echo spirals. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 747\u0026ndash;756 (2016). https://doi.org/10.1002/mrm.25914 \u003c/li\u003e\n\u003cli\u003eKim, K., Narsinh, K., Ozhinsky, E. Technical advances in motion-robust MR thermometry. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e92(1)\u003c/strong\u003e,15\u0026ndash;27 (2024). https://doi.org/10.1002/mrm.30057\u003c/li\u003e\n\u003cli\u003eDietrich, O., Lentini, S., \u0026Ouml;cal, O., Bour, P., Faller, T.L., Ozenne, V., Ricke, J., Seidensticker M. Accuracy of 3D real-time MRI temperature mapping in gel phantoms during microwave heating. \u003cem\u003eEur Radiol Exp\u003c/em\u003e \u003cstrong\u003e8(1)\u003c/strong\u003e, 92 (2024). https://doi.org/10.1186/s41747-024-00479-5\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBoehm C., Goeger-Neff M., Mulder H.T., Zilles B., Lindner L.H., van Rhoon G.C., Karampinos D.C., Wu M.\u003c/strong\u003eSusceptibility artifact correction in MR thermometry for monitoring of mild radiofrequency hyperthermia using total field inversion. \u003cem\u003eMagn. Reson. Med.\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 2919\u0026ndash;2931 (2022). https://doi.org/10.1002/mrm.29191\u003c/li\u003e\n\u003cli\u003eNouwens, S. A. N., Paulides, M. M., F\u0026ouml;lker, J., VilasBoas-Ribeiro, I., de Jager, B. \u0026amp; Heemels, W. P. M. H. \u003cem\u003eIntegrated thermal and magnetic susceptibility modeling for air-motion artifact correction in proton resonance frequency shift thermometry.\u003c/em\u003e \u003cem\u003eJ. Electromagn. Waves Appl.\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 967\u0026ndash;976 (2022). https://doi.org/10.1080/02656736.2022.2094475\u003c/li\u003e\n\u003cli\u003eWu, M., Mulder, H. T., Baron, P., Coello, E., Menzel, M. I., van Rhoon, G. C. \u0026amp; Haase, A. \u003cem\u003eCorrection of motion-induced susceptibility artifacts and B₀ drift during proton resonance frequency shift-based MR thermometry in the pelvis with background field removal methods.\u003c/em\u003e \u003cem\u003eMagn. Reson. Med.\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 2495\u0026ndash;2511 (2020). https://doi.org/10.1002/mrm.28302\u003c/li\u003e\n\u003cli\u003eHensen, B., Hellms, S., Werlein, C., Jonigk, D., Gronski, P. A. \u0026amp; Bruesch, I. \u003cem\u003eCorrection of heat-induced susceptibility changes in respiratory-triggered 2D-PRF-based thermometry for monitoring of magnetic resonance-guided hepatic microwave ablation in a human-like in vivo porcine model.\u003c/em\u003e \u003cem\u003eInt. J. Hyperthermia\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1387\u0026ndash;1396 (2022). https://doi.org/10.1080/02656736.2022.2138987\u003c/li\u003e\n\u003cli\u003eWundrak, S. et al. Golden ratio sparse MRI using tiny golden angles. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 2372\u0026ndash;2378 (2016). https://doi.org/10.1002/mrm.25831\u003c/li\u003e\n\u003cli\u003eJosset, A., Vappou, J., Ishak, O., Cabras, P. \u0026amp; Breton, \u0026Eacute;. \u003cem\u003eEffectiveness of fat suppression methods and influence on proton-resonance frequency shift (PRFS) MR thermometry.\u003c/em\u003e \u003cem\u003eMagn. Reson. Imaging\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 110340 (2025). https://doi.org/10.1016/j.mri.2025.110340\u003c/li\u003e\n\u003cli\u003eLustig, M., Donoho, D.L., Pauly, J.M. Rapid MR imaging with compressed sensing and randomly under-sampled 3DFT trajectories. In: Proceedings of the \u003cem\u003e14th Annual Meeting of ISMRM. Seattle, WA\u003c/em\u003e (2006).\u003c/li\u003e\n\u003cli\u003eBu-Lin, Z., Bing, H., Sheng-Li, K., Huang, Y., Rong, W., Jia, L. A polyacrylamide gel phantom for radiofrequency ablation. \u003cem\u003eInt J Hyperthermia\u003c/em\u003e \u003cstrong\u003e24(7)\u003c/strong\u003e, 568\u0026ndash;576 (2008). https://doi.org/10.1080/02656730802104732\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;er, S. et al. Reducing electromagnetic interference in MR thermometry: A comparison of setup configurations for MR-guided microwave ablations. \u003cem\u003eMed Phys.\u003c/em\u003e (2024) https://doi.org/10.1016/j.zemedi.2024.07.004\u003c/li\u003e\n\u003cli\u003eBelker, O. et al. MR-thermometry on moving organs by a reproducible respiratory simulation. Presented at the \u003cem\u003e6th IGIC, Mannheim, Germany\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eBlock, K.T., Uecker, M. Simple method for adaptive gradient-delay compensation in radial MRI. Presented at the\u003cem\u003e 19th Annual Meeting of ISMRM, Montr\u0026eacute;al, Canada\u003c/em\u003e (2011).\u003c/li\u003e\n\u003cli\u003eDuyn, J.H., Yang, Y., Frank, J.A., van der Veen, J.W. Simple correction method for k-space trajectory deviations in MRI. \u003cem\u003eJ Magn Reson\u003c/em\u003e \u003cstrong\u003e132\u003c/strong\u003e, 150\u0026ndash;153 (1998). https://doi.org/10.1006/jmre.1998.1396\u003c/li\u003e\n\u003cli\u003eFeng, L., Axel, L., Chandarana, H., Block, K.T., Sodickson, D.K., Otazo, R. XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 775\u0026ndash;788 (2016). https://doi.org/10.1002/mrm.25665\u003c/li\u003e\n\u003cli\u003eUecker, M. et al. Berkeley advanced reconstruction toolbox. \u003cem\u003e23rd Annu. Meeting ISMRM, Toronto, Canada\u003c/em\u003e (2015).\u003c/li\u003e\n\u003cli\u003eMadore, B., Panych, L.P., Mei, C.S., Yuan, J., Chu, R. Multipathway sequences for MR thermometry. \u003cem\u003eMagn Reson Med\u003c/em\u003e \u003cstrong\u003e66(3)\u003c/strong\u003e, 658\u0026ndash;668 (2011). https://doi.org/10.1002/mrm.22844\u003c/li\u003e\n\u003cli\u003ePearce J.A. Comparative analysis of mathematical models of cell death and thermal damage processes. \u003cem\u003eInt J Hyperthermia\u003c/em\u003e \u003cstrong\u003e29(4)\u003c/strong\u003e, 262\u0026ndash;280 (2013). https://doi.org/10.3109/02656736.2013.786140\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"stack-of-stars, stack-of-spirals, 3D MR thermometry, respiratory motion, microwave ablation","lastPublishedDoi":"10.21203/rs.3.rs-6176650/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6176650/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMicrowave ablation (MWA) of hepatic tumors benefits from MR thermometry, enabling real-time temperature monitoring to guide treatment and protect healthy tissue. However, MR thermometry in the abdomen is challenging due to respiratory and intestinal motion. This study evaluates two advanced 3D imaging sequences, Stack-of-Stars (Stars) and Stack-of-Spirals (Spirals), for precise MWA thermometry in phantom and volunteer experiments. Spirals demonstrated superior temperature precision, with a standard deviation of 0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u0026deg;C in unheated regions, compared to 2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u0026deg;C for Stars. In heated regions, Spirals achieved a lower RMSE (0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026deg;C vs. 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026deg;C for Stars) and a higher Dice score for ablation zone delineation (0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 vs. 0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10). Spirals also produced sharper images with fewer artifacts under simulated respiratory motion, while Stars showed streaking artifacts due to higher undersampling. These findings highlight Spirals\u0026rsquo; potential for accurate real-time thermometry in liver ablation. Future work should focus on improving reconstruction speed and mitigating susceptibility artifacts to enable clinical applications.\u003c/p\u003e","manuscriptTitle":"3D Proton Resonance Frequency Shift MR Thermometry for Monitoring Clinical Microwave Ablation: Comparison of Stack-of-Stars and Stack-of-Spirals Sequences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 05:36:25","doi":"10.21203/rs.3.rs-6176650/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-09T11:28:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T01:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80437878560021556390648076120063891085","date":"2025-04-02T07:48:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T05:25:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12996225201020023005695436200872411651","date":"2025-03-19T13:43:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-17T13:36:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T13:35:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-14T09:35:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-13T11:30:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-07T08:51:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9633afa3-9769-4f9c-bb82-c74c918203ae","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45844712,"name":"Physical sciences/Physics/Techniques and instrumentation/Imaging techniques"},{"id":45844713,"name":"Health sciences/Medical research/Translational research"}],"tags":[],"updatedAt":"2025-09-29T16:02:41+00:00","versionOfRecord":{"articleIdentity":"rs-6176650","link":"https://doi.org/10.1038/s41598-025-20588-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-24 15:58:22","publishedOnDateReadable":"September 24th, 2025"},"versionCreatedAt":"2025-03-20 05:36:25","video":"","vorDoi":"10.1038/s41598-025-20588-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-20588-4","workflowStages":[]},"version":"v1","identity":"rs-6176650","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6176650","identity":"rs-6176650","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}