3D ultra-short echo time 31P-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI

3D ultra-short echo time 31P-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI | 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 ultra-short echo time 31 P-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI Brian Bozymski, Uzay Emir, Ulrike Dydak, Xin Shen, M. Albert Thomas, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4223790/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Phosphorus-31 magnetic resonance spectroscopic imaging ( 31 P-MRSI) provides valuable non-invasive in vivo information on tissue metabolism but is burdened by poor sensitivity and prolonged scan duration. Ultra-short echo time (UTE) acquisitions minimize signal loss when probing signals with relatively short spin-spin relaxation time (T 2 ), while also preventing first-order dephasing. Here, a three-dimensional (3D) UTE sequence with a rosette k-space trajectory is applied to 31 P-MRSI at 3T. Conventional chemical shift imaging (CSI) employs highly regular Cartesian k-space sampling, susceptible to substantial artifacts when accelerated via undersampling. In contrast, this novel sequence’s “petal-like” pattern offers incoherent sampling more suitable for compressed sensing (CS). These results showcase the competitive performance of UTE rosette 31 P-MRSI against conventional weighted CSI with simulation, phantom, and in vivo leg muscle comparisons. Health sciences/Health care/Medical imaging/Magnetic resonance imaging Health sciences/Medical research/Translational research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Phosphorous-31 magnetic resonance spectroscopy ( 31 P-MRS), the longest-standing in vivo MRS modality, can be an invaluable tool for probing in vivo metabolites such as phosphocreatine (PCr), inorganic phosphate (Pi), phosphomonoesters (PMEs), phosphodiesters (PDEs), and adenosine triphosphate (ATP) . 1 , 2 As fundamental phospholipids and consitituents of the high-energy phosphate pathway, these 31 P metabolites provide noninvasive measures of tissue pH, lipid metabolism, and oxidative bioenergetics. 3 , 4 Thus, 31 P-MRS possesses versatile diagnostic and prognostic potential. For instance, elevated PME/PDE ratios and reduced ATP levels have been reported in diseased and cancerous liver tissue, often correlated with classical plasma markers and Child-Pugh scores. 5 – 9 Furthermore, 31 P-MRS has been used to assess whole-liver treatment efficacy, monitoring metabolite changes in malignant tissues following therapy. 10 Likewise, diminished PCr/ATP ratios and post-exercise PCr recovery rates have been measured in cardiac and skeletal muscles of patients with type 2 diabetes. 11 Numerous endeavors have employed 31 P MRS in the brain, heart, and muscle, seeking out alterations in neurodegenerative, cardiovascular, metabolic, and oncological diseases. 12 – 20 While relevant 1 H-MRS metabolite signals are obscured by background signals such as contaminating fat, water, and macromolecular signals, widely spaced 31 P spectral peaks can be easily elucidated due to the absence of these nuisance signals. However, in contrast to 1 H-MRS, 31 P-MRS is burdened by a lower gyromagnetic ratio and relatively short spin-spin metabolite relaxation times (T 2 ); 21 , 22 these factors engender extremely poor in vivo relative sensitivity and force a delicate balance between SNR, resolution, and scan duration. Low 31 P-MRS tissue concentrations (approximately 2 mM γ-ATP in liver 23 ) further exacerbate SNR challenges, so that commonly used acquisition delays (T E > 300 µs) with conventional methods result in prolonged acquisition, phase distortions, baseline roll, and subsequent operator errors during metabolite quantification. Such complications have been severely limiting factors in the clinical feasibility of 31 P-MRS. Recent advances in coil engineering and the introduction of ultra-high field (UHF, B 0 > 3T) scanners have assisted in mitigating these limiting factors; one experiment demonstrated a 2.8-factor increase in PCr SNR at 7T relative to 3T. 24 Conversely, UHF acquisitions also necessitate larger spectral bandwidth (SBW), with a 40 ppm range requiring approximately 2.0 kHz at 3T but 4.8 kHz at 7T. Still, excessive acquisition durations remain the clear barrier to clinical translation without innovative acceleration. To address these points, we propose a three-dimensional (3D), ultra-short echo time (UTE) sequence with a novel rosette k-space trajectory (previously validated in ultra-short-T 2 imaging 25 , 26 and brain iron mapping 27 , 28 ) for 31 P magnetic resonance spectroscopic imaging (MRSI). 29 Compared to conventional CSI Cartesian k-space trajectories, rosette’s “petal-like” pattern (Fig. 1 ) maps 3D k-space far more efficiently. Additionally, rosette’s relatively inchorent data sampling allows the possibility of significant acceleration through higher undersampling factors and compressed sensing (CS) reconstruction; offering better k-space coverage when compared to radial and spiral trajectories, generalized rosette’s curvature affords superior SNR performance under aggressive acceleration. 30 Furthermore, UTE acquisitions permit the capture of short-T 2 signals before significant transverse signal decay and first-order dephasing occur, enhancing SNR, simplifying spectral pre-processing, and minimizing operator quantification errors. Substantial efforts have been invested towards clinically feasible 31 P-MRSI, experimenting with short repetition times (T R ), measuring multiple k-space points per T R , k-space undersampling, enhanced reconstruction via prior knowledge, and their conceivable combinations. 31 3D extensions of ISIS have shown promise in UHF preclinical and 3T cardiac studies but remain limited by time resolution and motion artifact sensitivity. 32 , 33 Non-localized or FID acquisitions are often preferred to minimize rapid 31 P T 2 -decay, but also to overcome specific absorption rate (SAR) limitations at UHFs. Thus, variations of spatial-spectral encoding (SSE) schemes and their synergies with k-space undersampling appear to be the more promising avenue forward; several Cartesian and non-Cartesian acquisition designs offer varying degrees of SNR efficiency, k-space weighting, gradient system demands, and undersampling acceleration potential. Flyback EPSI has been tested in skeletal calf muscle, 34 offering considerable time savings over conventional phase-encoded when combined with CS acceleration; 35 despite its acceleration potential, EPSI offers lower SNR efficiency and SBW at fine resolutions than other SSE options. Density-weighted concentric ring 36 trajectories (CRTs) offer increased SNR efficiency and SBW limits, enabling faster MRSI even with UHF systems. CRTs boast flexibility in weighting and temporal interleaves, allowing tailoring according to acceleration needs and gradient slew rates. Similarly, spiral-encoded 31 P-MRSI has exhibited faster dynamic calf muscle mapping than conventional weighting phase-encoded acquisition. 37 Though spirals offer high acceleration, SNR efficiency, and customizable weighting, they are also limited by SBW and gradient system hardware. Conventional CSI FIDs commonly possess T E on the order of 1–2 ms, which is restricted by the duration of constant excitation pulses and phase encoding gradients to reach the outermost k-space points. This acquisition delay can be decreased using variable pulse widths and amplitudes. Sampling throughout gradient transition periods, or ramp sampling, remains an additional option for minimizing T E . Studies have showcased T E as low as 480 µs and 520 µs for EPSI and radial EPSI, respectively. 35 , 38 Acquisitions using such UTE CSI techniques 39 have achieved T E = 300 µs at 3T and T E = 500 µs at 7T. 40 , 41 In non-Cartesian center-out k-space trajectories employing ramp sampling, T E is primarily limited by the dead time between coil transmit-receive switching, permitting the shortest possible T E . Despite this possibility, SBW constraints and conventional sequence parameter (T A /FOV) matching have prevented extensive investigation of non-Cartesian UTE 31 P-MRSI. In this study, we evaluate the 3D UTE 31 P-MRSI with a novel rosette k-space trajectory by comparing its performance to conventional 3D-weighted 31 P CSI in the quadriceps muscle at 3T. We ultimately aim to demonstrate its potential value in clinical spectroscopic acquisitions. 2. Methods 2.1 k-space trajectory designs for MRSI 2.1.1 UTE Rosette General sequence parameters were as follows: T A = 36:00, T R = 350 ms, T E = 65 µs, matrix size = 24x24x24, nominal voxel size = 8 mL, FOV = (480x480x480) mm 3 , SBW = 2083 Hz, spectral time samples = 512. Parameters are summarized in Table 1 . Table 1 Table of protocol parameters for conventional 3D weighted CSI, novel 3D UTE rosette MRSI, and additional retrospectively accelerated rosette sequences. Nominal voxel size was matched between methods, with total acquisition time approximately equal between the two full acquisitions. SBWs were matched via interpolation during post-processing. SEQUENCE: UTE Rosette Weighted CSI T A (mm:ss) 36:00 36:56 T R 350 ms 1000 ms T E 70 µs 2.3 ms Number of Averages 4 4 Reconstruction Matrix 24x24x24 16x16x16 Nominal Voxel (mL) 8 8 FOV (mm 3 ) 480x480x480 320x320x320 Bandwidth (Hz) 2083 2200 Time Samples 512 512 T A relative to CSI 0.97 1.00 As in prior work, 25 3D UTE rosette k-space trajectory (Fig. 1 ) for 31 P-MRSI was generated with Equations (1) and (2): $${K}_{xy}=Kx\left(t\right)+i*Ky\left(t\right)$$ $$=\left(K \text{m}\text{a}\text{x}*\text{cos}\left(\phi \right)\right)*\text{sin}\left({\omega }_{1}*t\right)*{e}^{i{\omega }_{2}t+\beta } \left(1\right)$$ $${K}_{z}\left(t\right)=\left(K \text{m}\text{a}\text{x}*\text{sin}\left(\phi \right))*\text{sin}\left({\omega }_{1}*t\right) \right(2)$$ Where K max is the maximum extent of k-space, \({\omega }_{1}\) is the frequency of oscillation in the radial direction, \({\omega }_{2}\) is the frequency of rotation in the angular direction, \(\phi\) determines the location in the z-axis, and \(\beta\) determines the initial phase in the angular direction. For this 31 P-MRSI study, K max = 25/m, \(\phi\) was sampled uniformly in the range of [- \(\pi\) /2, \(\pi\) /2], \(\beta\) was sampled uniformly in the range of [0, 2 \(\pi\) ], RF pulse duration = 50 µs, readout dwell time = 5 µs, and each rosette petal was designed with 96 points. This leads to $${\omega }_{1}={\omega }_{2}=\frac{\pi }{\text{p}\text{o}\text{i}\text{n}\text{t}\text{s} \text{p}\text{e}\text{r} \text{p}\text{e}\text{t}\text{a}\text{l} \left({N}_{pp}\right)\text{*}\text{d}\text{w}\text{e}\text{l}\text{l} \text{t}\text{i}\text{m}\text{e}}=\frac{\pi }{96*5 {\mu }\text{s} }=6545 \text{r}\text{a}\text{d}/\text{s}$$ as well as $$\text{S}\text{B}\text{W}=\frac{1}{{N}_{pp}\text{*}\text{d}\text{w}\text{e}\text{l}\text{l} \text{t}\text{i}\text{m}\text{e}}=\frac{1}{96*5 {\mu }\text{s}}={\left(480 {\mu }\text{s}\right)}^{-1}=2083 \text{H}\text{z}$$ and the resulting − 20 to + 20 ppm 3T spectral range is more than sufficient for 31 P-MRS. In reconstruction, each petal was downsampled from N pp = 96 points to N pp = 48 by averaging the oversampled points. With a 24x24x24 reconstruction matrix, the required number of petals ( N p ) to satisfy Nyquist criterion was calculated as $${N}_{p}=4\pi *{\left(\frac{24}{2}\right)}^{2}=1810$$ However, due to the rosette’s efficient sampling scheme, only 80% coverage ( N p = 1444) was defined as full k-space acquisition. Thus, the acquisition time per average was calculated as $${\text{T}}_{\text{A}}={N}_{p}*{\text{T}}_{\text{R}}=1444*350 \text{m}\text{s}=505 \text{s}$$ or roughly 9 minutes. A complete description including the influence of trajectory parameters, Nyquist criterion, and the specific gradient ramp-up of this 3D rosette k-space pattern is provided in earlier work (Shen et al.). 25 2.1.2 Weighted CSI Conventional Cartesian 3D acquisitions used the vendor-provided 31 P CSI FID with k-space weighting and Hanning filter. Sequence parameters were as follows: T A = 36:56, T R = 1000 ms, T E = 2.3 ms, matrix size = 16x16x16, nominal voxel size = 8 mL, FOV = (320x320x320) mm 3 , SBW = 2200 Hz, spectral time samples = 512. Parameters are summarized in Table 1 . 2.2 Simulations To assess the theoretical performance of the 3D UTE rosette relative to weighted CSI, MATLAB (Mathworks, Natick, USA) simulations were run examining side lobes and SNR relative to the spatial response function (SRF). A simple, constant 3D object was placed at the origin of a 48 x 48 x 48 grid (FOV = 480 mm isotropic producing a 1 mL nominal voxel size) with added noise and reconstructed using the non-uniform FFT (NUFFT) method and k-space information for each in vivo acquisition. We use spatial response function (SRF) instead of point spread function (PSF) since the former specifically estimates side lobes and signal bleed between adjacent voxels, while the latter measures contribution from a single object point to the entire population of voxels. 2.3 Experimental comparison All data acquisition occurred on a 3T MRI system (Prisma, Siemens, Erlangen, Germany) with G max = 80 mT/m and slew rate = 200 mT/m/ms isotropically. Human subject protocols were approved by the Institutional Review Boards of Purdue University, and informed consent was obtained. Sequence parameters are summarized in Table 1 . The rosette and conventional acquisitions were tested with a uniform 2-liter bottle phantom (0.17 mg/mL phosphoric acid) using a dual-tuned 1 H/ 31 P Tx/Rx flexible 11-cm surface coil (RAPID Biomedical). For in vivo comparison, five healthy volunteers (BMI = 26 ± 2 kg/m 2 ; age = 29 ± 5 years; 2 f / 3 m) underwent leg scans with an 8-channel, dual-tuned 1 H/ 31 P Tx/Rx phased array coil 42 (Stark Contrast, Erlangen, Germany). Quadriceps was chosen for its superior PCr SNR and absence of respiratory motion during prolonged scanning. Subjects were positioned feet-first and supine, with the upper quadriceps tightly surrounded by the coil plates. Following localizer imaging, the adjustment volume was manually positioned (spanning both legs), and linewidth was minimized using a 3D GRE field map and interactive SIEMENS shimming. Each subject was scanned first with the conventional weighted Cartesian acquisition followed uninterrupted by the 3D UTE rosette 31 P-MRSI. Sequence parameters are summarized in Table 1 . 2.4 Post-processing and reconstruction Raw data files were exported for reconstruction and pre-processing in MATLAB. Gridding and FFT were completed using adjoint NUFFT (regridding), 43 Hanning filtered, and, when necessary, coil-combined using whitened singular value decomposition (wSVD). 44 Spectra were zero-order phased by maximizing the integral of the largest peak (PCr, 0 ppm) for the 3D UTE rosette. Spectra from the weighted CSI were zero-order phased and first-order phased to correct a 2.3 ms delay. 2.5 SNR and quantification Spectra were fitted within the Oxford Spectroscopy Analysis (OXSA) toolbox 45 using AMARES methods. Metabolite peak SNRs were calculated according to Eq. (3), with noise variance calculated from a residual region lacking metabolite signals. As an additional signal quantification metric, “raw SNR” (Eq. (4)) was estimated by dividing the highest absolute peak point by the noise variance in an off-spectrum region; this method carries the advantage of consistently assessing signal strength regardless of any interfering spectral phase. $${\text{S}\text{N}\text{R}}_{\text{O}\text{X}\text{S}\text{A}}=\frac{\text{P}\text{e}\text{a}\text{k} \text{S}\text{i}\text{g}\text{n}\text{a}\text{l} \text{F}\text{i}\text{t} \left(\text{R}\text{e}\text{a}\text{l}\right)}{\text{R}\text{M}{\text{S}}_{\text{r}\text{e}\text{s}\text{i}\text{d}\text{u}\text{a}\text{l} \text{n}\text{o}\text{i}\text{s}\text{e}}} \left(3\right)$$ $${\text{S}\text{N}\text{R}}_{\text{r}\text{a}\text{w}}=\frac{\text{M}\text{a}\text{x}\text{i}\text{m}\text{u}\text{m} \text{P}\text{e}\text{a}\text{k} \text{A}\text{m}\text{p}\text{l}\text{i}\text{t}\text{u}\text{d}\text{e} \left(\text{A}\text{b}\text{s}\text{o}\text{l}\text{u}\text{t}\text{e}\right)}{\text{R}\text{M}{\text{S}}_{\text{o}\text{f}\text{f}-\text{s}\text{p}\text{e}\text{c}\text{t}\text{r}\text{u}\text{m} \text{n}\text{o}\text{i}\text{s}\text{e}}} \left(4\right)$$ Data acquisition, reconstruction, processing, and analysis workflow is summarized in Figs. 2 and 3 . 2.6 Quantitative analysis The performance of 3D UTE rosette and weighted CSI in phantom solution and quadriceps muscle were assessed using Pi and PCr metabolite signals, respectively. Quantification considered the central, highest signal axial slices within each subject, attempting to quantify every voxel. Only voxels with SNR > 3 and OXSA-AMARES Cramér-Rao lower bound (CRLB) goodness of fit smaller than 20% for PCr peak were included in the final analysis. 3. Results 3.1 Spatial Response Function simulation comparison of UTE Rosette with Weighted CSI The impact of varying k-space sampling trajectories on image quality can be evaluated via SRF simulations as shown in Fig. 4 . FWHMs along the x-axis at the center of the FOV were comparable between rosette (30.7 mm) and weighted CSI (36.1 mm). Both acquisition schemes exhibit noticeable sidelobe noise, albeit with slightly reduced side lobes in the rosette trajectory. 3.2 Phantom comparison of UTE Rosette with Weighted CSI Figure 5 showcases results and setup of phantom experiments with dual-tuned flexible surface coil. With approximately matched acquisition times, mean raw SNR (Eq. (4)) was 69% higher in 3D UTE rosette than in weighted CSI. 3.3 In vivo leg comparison of UTE Rosette with Weighted CSI Figure 6 shows representative 3D UTE rosette and weighted CSI axial PCr maps and spectra in the same volunteer. High-signal muscle regions are clearly distinguishable from low-signal bony femur regions. As expected, PCr predominates the 31 P muscle spectrum alongside smaller Pi and ATP peaks. Quantitative PCr results for all subjects are given in Fig. 7 , highlighting the different SNRs resulting from Equations (3) and (4) respectively. Tables 2 and 3 summarize these distinctions. While 3D UTE rosette consistently outperformed weighted CSI, the advantage was slightly more prominent in AMARES-fitting of the real data at 34% compared to raw SNR of absolute data at 18%. Table 2 Mean in vivo PCr SNR from quantifiable voxels in individual subjects using each method. With matched voxel size and acquisition resolution, 3D UTE rosette consistently outperforms weighted CSI acquisition in measured SNR. OXSA-AMARES (Eq. 3) UTE Rosette Weighted CSI Raw Absolute (Eq. 4) UTE Rosette Weighted CSI Subject 1 10.50 (± 3.49) 8.51 (± 2.66) Subject 1 49.19 (± 25.72) 38.50 (± 16.44) Subject 2 11.26 (± 4.32) 7.37 (± 1.97) Subject 2 37.96 (± 17.97) 38.77 (± 14.02) Subject 3 13.21 (± 4.75) 10.54 (± 3.88) Subject 3 60.74 (± 33.49) 46.31 (± 18.49) Subject 4 12.99 (± 4.83) 9.24 (± 3.35) Subject 4 61.96 (± 33.23) 53.75 (± 20.08) Subject 5 11.78 (± 4.42) 9.11 (± 2.79) Subject 5 53.35 (± 27.57) 49.62 (± 18.81) Total Fitted Voxels 1134 947 Total Fitted Voxels 1325 1378 Overall Leg PCr SNR 11.95 (± 4.39) 8.95 (± 3.00) Overall Leg PCr SNR 52.83 (± 28.18) 44.95 (± 17.70) Table 3 Mean PCr SNR from quantifiable voxels across all five quadriceps subjects and bottle phantom using each method. With matched voxel size and acquisition resolution, 3D UTE rosette consistently outperforms weighted CSI acquisition in measured SNR. SEQUENCE: UTE Rosette Weighted CSI UTE/CSI SNR Ratio OXSA-AMARES Real in vivo PCr SNR 11.95 (± 4.39) 8.95 (± 3.00) 1.34 Raw Absolute in vivo PCr SNR 52.83 (± 28.19) 44.95 (± 17.70) 1.18 Raw Absolute Phantom SNR 52.48 (± 34.6) 31.00 (± 9.84) 1.69 4. Discussion 4.1 Experimental overview This study demonstrates the feasibility of using 3D UTE 31 P-MRSI with a novel rosette k-space trajectory to acquire quality in vivo human subject data. Simulations showed the rosette trajectory produced acceptable image quality and SRF characteristics when compared to a conventional 3D weighted CSI acquisition. Experimental phantom scans utilized a uniform Pi bottle solution and the same scanning parameters later applied to in vivo quadriceps subjects. 4.2 UTE advantages The novel acquisition’s 70 µs acquisition delay is substantially lower than the 300–500 µs delays in previously published UTE 31 P CSI methods 40 , 41 , minimizing transverse signal decay and first-order dephasing. Accurate phasing is key to spectral fitting and quantification of real spectral data; when fitting parameters must be tailored to hundreds of voxels across a large volume, such as for high resolution 3D MRSI, the challenge of avoiding phasing errors is most apparent. As expected, all 3D UTE rosette data were intrinsically devoid of noticeable first-order phasing, thereby streamlining the quantification process. The SNR gap between 3D UTE rosette and weighted CSI acquisitions was narrowed when solely considering absolute data (Eq. (4)). This distinction might be partially explained by the absence of phase in these magnitude spectra, whereby the 3D UTE rosette (T E = 70 µs) acquisition loses a portion of its advantage over conventional weighted CSI (T E = 2.3 ms). Notably, SDs for 3D UTE rosette SNR were significantly higher (50% or more) compared to weighted CSI. This elevated variation is partially explained by the novel acquisition’s significantly higher SNR; moreover, weighted CSI’s wider SRF engenders higher inter-voxel crosstalk, diminishing overall variation among quantified voxels. 4.3 Acceleration potential Although these 36-minute acquisitions are quite lengthy, conventional ungated in vivo 3D 31 P-MRSI typically requires a minimum of 20 minutes at 3T. Compared to a weighted Cartesian trajectory, this novel rosette k-space pattern’s relative incoherence makes it a very suitable candidate to CS acceleration via undersampling. Applying undersampling factors of 2 to 4, as demonstrated previously in uT 2 brain imaging 25 , could reduce 31 P-MRSI’s TA to 9–18 minutes (or less with fewer averages). 46 Such an acceleration would allow implementation of 31 P-MRS within realistic clinical constraints, while also being translatable to UHF research systems and higher resolutions. As with all 31 P-MRS, spectral quality can also see potential improvement via proton decoupling and nuclear Overhauser effect (NOE) enhancement, albeit with implications for SAR and measured metabolite ratios. Furthermore, appropriately applied low-rank approximation and principal component analysis denoising have seen use in heightening SNR of MRSI data sets; 47 – 49 nevertheless, in the absence of ground truths or precise simulation, care must be taken in estimating metabolite concentration uncertainties after denoising. 4.4 Resolution and SBW Many non-Cartesian acquisitions face restrictions in spatial resolution, SBW, and SNR due to available gradient hardware. 31 For example, spiral trajectories face reduced SNR while waiting to return to k-space center between spirals; this inefficiency is addressed by closed-loop, out-in trajectories, but these remain impractical outside UHF animal gradient systems. 50 Concentric rings can be similarly adjusted to meet needs with temporal interleaves. 36 Still, SBW limitations remain a significant challenge; while 2.0 kHz might be sufficient for 31 P-MRS at 3T, such a SBW would only offer a spectral range of around 17 ppm at 7T. While this rosette acquisition sampled 48 points per petal every 480 µs, the sequence remains highly customizable. By leveraging the second half of each petal (Fig. 1 ), it is possible to partially satisfy Nyquist criterion at even higher bandwidths and enable finer resolution reconstructions than the relatively coarse 8 mL voxels shown here. Additionally, this permits greater SBW acquisitions, opening the door to 3D UTE rosette 31 P-MRSI at UHF and 1 H MRSI at 3T. However, these petal halves are analogous to odd and even echoes of EPSI MRSI; since the timings between individual N pp are not equidistant, “full-petal” spectra will suffer from some degree of noise amplification and aliasing artifact. 4.5 Other limitations Further experimentation is required in exploring the potential and limitations of 3D UTE rosette MRSI. Notably, these quadriceps scans focused on quadriceps muscle with plentiful PCr signal in a healthy volunteer population. However, 31 P-MRSI is frequently applied in measuring diverse brain, cardiac, and liver spectra, where nearby tissues may introduce contaminating metabolite signals. Minimal signal contamination was observed in noisy voxels within bony regions. Nonetheless, due to the relative uniformity of skeletal muscle spectra, it would be difficult to discern the rosette acquisition’s relatively incoherent aliasing. Future work will aim to assess accelerated performance in patient populations. 5. Conclusions Using the quadriceps of five healthy volunteers at 3T, we investigated a potential application to 31 P-MRSI using a novel 3D UTE rosette sequence. In comparison to a conventional 3D weighted CSI with matched bandwidth, nominal resolution, and acquisition time, the novel rosette acquisition provided competitive resolution and superior SNR with straightforward quantification. As this proof-of-concept study was limited to five subjects and a relatively homogeneous region of PCr-plentiful muscle, additional testing is required to demonstrate efficacy in differentiating diverse and diseased tissue regions. Declarations Competing interests The authors declare no competing interests. Author Contribution Conceptualization by U.E. & U.D.; Methodology contributions from all authors; Data collection by B.B., X.S. & U.E.; Data analysis by B.B., U.E., X.S. & W.C.; Results and conclusions reviewed and agreed upon by all authors. First manuscript draft and revision by B.B., U.E. & X.S.; Final revision and approval by all authors. Acknowledgement This work was supported by grants to U.E. and S.S. from the Wellcome Trust Collaborative Award (223131/Z/21/Z). 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Contraction and recovery of living muscles studies by 31P nuclear magnetic resonance. J Physiol . 1977;267(3):703-735. doi:10.1113/JPHYSIOL.1977.SP011835 Ross BD, Radda GK, Gadian DG, Rocker G, Esiri M, Falconer-Smith J. Examination of a case of suspected McArdle’s syndrome by 31P nuclear magnetic resonance. N Engl J Med . 1981;304(22):1338-1342. doi:10.1056/NEJM198105283042206 Valkovič L, Chmelík M, Krššák M. In-vivo31P-MRS of skeletal muscle and liver: A way for non-invasive assessment of their metabolism. Anal Biochem . 2017;529:193-215. doi:10.1016/J.AB.2017.01.018 Meyerspeer M, Krššák M, Moser E. Relaxation times of 31P-metabolites in human calf muscle at 3 T. Magn Reson Med . 2003;49(4):620-625. doi:10.1002/MRM.10426 Meyerspeer M, Boesch C, Cameron D, et al. 31 P magnetic resonance spectroscopy in skeletal muscle: Experts’ consensus recommendations. NMR Biomed . 2020;34(5). doi:10.1002/NBM.4246 Jonuscheit M, Wierichs S, Rothe M, et al. Reproducibility of absolute quantification of adenosine triphosphate and inorganic phosphate in the liver with localized 31P-magnetic resonance spectroscopy at 3-T using different coils. NMR Biomed . 2024. doi:10.1002/NBM.5120 Rodgers CT, Clarke WT, Snyder C, Vaughan JT, Neubauer S, Robson MD. Human cardiac 31P magnetic resonance spectroscopy at 7 tesla. Magn Reson Med . 2014;72(2):304. doi:10.1002/MRM.24922 Shen X, Özen AC, Sunjar A, et al. Ultra-short T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern. Magn Reson Med . 2023;89(2):508-521. doi:10.1002/MRM.29451 Shen X, Caverzasi E, Yang Y, et al. 3D balanced SSFP UTE MRI for multiple contrasts whole brain imaging. 2024. doi:10.1002/mrm.30093 Shen X, Özen AC, Monsivais H, et al. High-resolution 3D ultra-short echo time MRI with Rosette k-space pattern for brain iron content mapping. J Trace Elem Med Biol . 2023;77. doi:10.1016/J.JTEMB.2023.127146 Monsivais H, Nossa G, Hong S, et al. Ultrashort-echo time magnetization transfer (UTE-MT) for brain iron imaging. Presented during ISMRM Annual Meeting & Exhibition; June 3-8, 2023; Toronto, CA . Bozymski B, Shen X, Ozen AC, et al. Comparison of Compressed Sensing Accelerated Rosette UTE and Conventional 31P 3D MRSI at 3T in Leg Muscle. Presented during ISMRM Annual Meeting & Exhibition; June 3-8, 2023; Toronto, CA . Li Y, Yang R, Zhang C, Zhang J, Jia S, Zhou Z. Analysis of generalized rosette trajectory for compressed sensing MRI. Med Phys . 2015;42(9):5530-5544. doi:10.1118/1.4928152 Bogner W, Otazo R, Henning A. Accelerated MR spectroscopic imaging—a review of current and emerging techniques. NMR Biomed . 2021;34(5):e4314. doi:10.1002/NBM.4314 Bakermans AJ, Abdurrachim D, van Nierop BJ, et al. In vivo mouse myocardial (31)P MRS using three-dimensional image-selected in vivo spectroscopy (3D ISIS): technical considerations and biochemical validations. NMR Biomed . 2015;28(10):1218-1227. doi:10.1002/NBM.3371 de Wit-Verheggen VHW, Schrauwen-Hinderling VB, Brouwers K, et al. PCr/ATP ratios and mitochondrial function in the heart. A comparative study in humans. Scientific Reports 2023 13:1 . 2023;13(1):1-10. doi:10.1038/s41598-023-35041-7 Santos-Díaz A, Obruchkov SI, Schulte RF, Noseworthy MD. Phosphorus magnetic resonance spectroscopic imaging using flyback echo planar readout trajectories. MAGMA . 2018;31(4):553-564. doi:10.1007/S10334-018-0675-Y Santos-Díaz A, Harasym D, Noseworthy MD. Dynamic 31 P spectroscopic imaging of skeletal muscles combining flyback echo-planar spectroscopic imaging and compressed sensing. Magn Reson Med . 2019;81(6):3453-3461. doi:10.1002/MRM.27682 Clarke WT, Hingerl L, Strasser B, Bogner W, Valkovič L, Rodgers CT. Three-dimensional, 2.5-minute, 7T phosphorus magnetic resonance spectroscopic imaging of the human heart using concentric rings. NMR Biomed . 2023;36(1). doi:10.1002/NBM.4813 Valkovič L, Chmelík M, Meyerspeer M, et al. Dynamic 31P–MRSI using spiral spectroscopic imaging can map mitochondrial capacity in muscles of the human calf during plantar flexion exercise at 7 T. NMR Biomed . 2016;29(12):1825. doi:10.1002/NBM.3662 Ludwig D, Korzowski A, Ruhm L, Ladd ME, Bachert P. Three-dimensional 31P radial echo-planar spectroscopic imaging in vivo at 7T. Presented during ISMRM 25th Annual Meeting and Exhibition; April 22-27, 2017; Honolulu, HI . Robson MD, Tyler DJ, Neubauer S. Ultrashort TE Chemical Shift Imaging (UTE-CSI). Magn Reson Med . 2005;53:267-274. doi:10.1002/mrm.20344 Tyler DJ, Emmanuel Y, Cochlin LE, et al. Reproducibility of 31P cardiac magnetic resonance spectroscopy at 3 T. NMR Biomed . 2009;22(4):405-413. doi:10.1002/NBM.1350 Ellis J, Valkovič L, Purvis LAB, Clarke WT, Rodgers CT. Reproducibility of human cardiac phosphorus MRS (31P‐MRS) at 7 T. NMR Biomed . 2019;32(6). doi:10.1002/NBM.4095 Panda A, Jones S, Stark H, et al. Phosphorus liver MRSI at 3 T using a novel dual-tuned eight-channel 31 P/ 1 H H coil. Magn Reson Med . 2012;68(5):1346-1356. doi:10.1002/MRM.24164 Fessler JA. On NUFFT-based gridding for non-Cartesian MRI. J Magn Reson . 2007;188(2):191-195. doi:10.1016/J.JMR.2007.06.012 Rodgers CT, Robson MD. Receive array magnetic resonance spectroscopy: Whitened singular value decomposition (WSVD) gives optimal Bayesian solution. Magn Reson Med . 2010;63(4):881-891. doi:10.1002/MRM.22230 Purvis LAB, Clarke WT, Biasiolli L, Valkovič L, Robson MD, Rodgers CT. OXSA: An open-source magnetic resonance spectroscopy analysis toolbox in MATLAB. PLoS One . 2017;12(9):e0185356. doi:10.1371/JOURNAL.PONE.0185356 Farley N, Bozymski B, Dydak U, Emir U. Fast 3D-P31-MRSI Using Custom Rosette Petal Trajectory at 3T with 4x Accelerated Compressed Sensing. Presented during ISMRM Annual Meeting & Exhibition; June 3-8, 2023; Toronto, CA . Nguyen HM, Peng X, Do MN, Liang ZP. Denoising MR spectroscopic imaging data with low-rank approximations. IEEE Trans Biomed Eng . 2013;60(1):78-89. doi:10.1109/TBME.2012.2223466 Clarke WT, Chiew M. Uncertainty in denoising of MRSI using low-rank methods. Magn Reson Med . 2022;87(2):574-588. doi:10.1002/MRM.29018 van den Wildenberg L, Gursan A, Seelen LWF, et al. In vivo phosphorus magnetic resonance spectroscopic imaging of the whole human liver at 7 T using a phosphorus whole-body transmit coil and 16-channel receive array: Repeatability and effects of principal component analysis-based denoising. NMR Biomed . 2023;36(5). doi:10.1002/NBM.4877 Esmaeili M, Strasser B, Bogner W, Moser P, Wang Z, Andronesi OC. Whole-Slab 3D MR Spectroscopic Imaging of the Human Brain With Spiral-Out-In Sampling at 7T. J Magn Reson Imaging . 2021;53(4):1237-1250. doi:10.1002/JMRI.27437 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Jun, 2024 Reviews received at journal 23 Jun, 2024 Reviews received at journal 31 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers agreed at journal 15 May, 2024 Reviewers invited by journal 13 May, 2024 Editor assigned by journal 06 May, 2024 Editor invited by journal 19 Apr, 2024 Submission checks completed at journal 19 Apr, 2024 First submitted to journal 05 Apr, 2024 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. 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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-4223790","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288039963,"identity":"78494e4c-5376-4cd5-8fe1-e390410e1262","order_by":0,"name":"Brian Bozymski","email":"","orcid":"","institution":"School of Health Sciences, Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Bozymski","suffix":""},{"id":288039964,"identity":"af492544-dad3-46d6-b4c5-d85bfd2a818a","order_by":1,"name":"Uzay Emir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYJCCAwwVICoBRDATq+UMqVoYGNtI0WJwvDvxcOW8O4nb23MMHzBUWCc2ENRy5uyGg2e3PUucc+aNsQHDmXQitNzI3XCwcdvhxBkSOWYSjG2HidBy/y1QyxywFvMfjP+I0XKDF6ilAWILA2MDEVokzwAd1nDsmfEMnmfFEgnH0o0JauE7fnbzx4aaO7Iz2JM3fvhQYy1LUAsUHIBQCUQqR9IyCkbBKBgFowAbAADRHkmadp7zSgAAAABJRU5ErkJggg==","orcid":"","institution":"School of Health Sciences, Purdue University","correspondingAuthor":true,"prefix":"","firstName":"Uzay","middleName":"","lastName":"Emir","suffix":""},{"id":288039965,"identity":"639beb49-5f57-4e14-9220-36ae2ca30b2a","order_by":2,"name":"Ulrike Dydak","email":"","orcid":"","institution":"School of Health Sciences, Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Ulrike","middleName":"","lastName":"Dydak","suffix":""},{"id":288039966,"identity":"ac5d6b36-3425-4f6b-b23f-aaeaf5d15621","order_by":3,"name":"Xin Shen","email":"","orcid":"","institution":"Radiology and Biomedical Imaging, University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Shen","suffix":""},{"id":288039967,"identity":"d9b01359-869a-43ca-a742-e24b5e80b51d","order_by":4,"name":"M. Albert Thomas","email":"","orcid":"","institution":"Department of Radiology, University of California","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Albert","lastName":"Thomas","suffix":""},{"id":288039968,"identity":"5d807c63-b469-46b3-b485-0573727f59f6","order_by":5,"name":"Ali Özen","email":"","orcid":"","institution":"Department of Radiology, Medical Physics, Medical Center - University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Özen","suffix":""},{"id":288039969,"identity":"23e379f3-845a-4edd-a2f0-4b8010db8d30","order_by":6,"name":"Mark Chiew","email":"","orcid":"","institution":"Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Chiew","suffix":""},{"id":288039970,"identity":"694496bb-9881-4f78-8a7e-671547f18a43","order_by":7,"name":"William Clarke","email":"","orcid":"","institution":"Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Clarke","suffix":""},{"id":288039971,"identity":"784692bb-6ccc-4726-83d5-fbf92d25ada7","order_by":8,"name":"Stephen Sawiak","email":"","orcid":"","institution":"Department of Clinical Neuroscience, University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Sawiak","suffix":""}],"badges":[],"createdAt":"2024-04-05 15:15:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4223790/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4223790/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90630-y","type":"published","date":"2025-02-22T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54319974,"identity":"01ef1b3f-9deb-40c5-aebe-79d75d1666ae","added_by":"auto","created_at":"2024-04-08 19:01:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":524571,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of 3D rosette k-space trajectory and gradients. \u003cstrong\u003e(A-C) \u003c/strong\u003eAcquisition begins at k-space center for every petal, crossing k-space origin twice at each petal’s beginning and end. Petals can be manually separated into two halves, similar to odd and even echoes in EPSI MRSI. \u003cstrong\u003e(D, E) \u003c/strong\u003eVaried petal rotations form the rosette pattern, providing sufficient k-space coverage. \u003cstrong\u003e(F)\u003c/strong\u003e With the closed-loop trajectory, acquisition delay is further minimized by enabling the analog to digital converter (ADC) for sampling during gradient ramp-up.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/c711f35496eb290948e71b7b.jpeg"},{"id":54320291,"identity":"a2d462ef-583a-4fac-b276-1fd31d9a5f80","added_by":"auto","created_at":"2024-04-08 19:09:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":239209,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of data acquisition, reconstruction, processing, and analysis. Subjects were positioned feet-first supine with both quadriceps positioned between the 30-cm phased array coil plates. Raw data were exported, appropriately reconstructed, coil-combined, and phased prior to fitting and quantification.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/12f369074ac35ed029c0e3ef.png"},{"id":54320292,"identity":"bf42bee3-e605-4327-9e71-6553b2e61201","added_by":"auto","created_at":"2024-04-08 19:09:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":393057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eSignal intensity and \u003cstrong\u003eselected central axial slices\u003c/strong\u003e for Subject 3’s rosette UTE acquisition.\u0026nbsp; \u003cstrong\u003e(B) \u003c/strong\u003eVisualization of “raw SNR” calculation on \u003csup\u003e31\u003c/sup\u003eP-MRS muscle spectrum in one voxel.\u0026nbsp; \u003cstrong\u003e(C)\u003c/strong\u003e Results for quantifiable (SNR \u0026gt;3) voxels within \u003cstrong\u003eselection\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/4672d7f01f53bd11553dc559.jpeg"},{"id":54319980,"identity":"8d1d643a-3210-4ffd-9a64-31ea9ee14989","added_by":"auto","created_at":"2024-04-08 19:01:12","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":384462,"visible":true,"origin":"","legend":"\u003cp\u003eResults of spatial response function (SRF) simulation for conventional 3D weighted CSI and novel 3D rosette MRSI sequences.\u0026nbsp; \u003cstrong\u003e(A)\u003c/strong\u003e 2D (xy-plane) SRFs for simulated object and each k-space trajectory at center of the FOV.\u0026nbsp; \u003cstrong\u003e(B)\u003c/strong\u003e 1D (x-axis) log\u003csub\u003e10\u003c/sub\u003e decibel comparison between k-space trajectories at center of the FOV.\u0026nbsp; \u003cstrong\u003e(C)\u003c/strong\u003e 1D (x-axis) comparisons between each normalized reconstruction and the true simulated object at center of the FOV.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/255808669a34c78e52aaab80.jpeg"},{"id":54319976,"identity":"e65f425b-80ff-41bf-82e4-1a9298374897","added_by":"auto","created_at":"2024-04-08 19:01:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195982,"visible":true,"origin":"","legend":"\u003cp\u003eResults from phantom measurements using “raw SNR” (Equation (4)) of absolute inorganic phosphate (Pi) metabolite signal. \u003cstrong\u003e(A)\u003c/strong\u003eWith approximately matched acquisition times, 3D UTE rosette’s mean SNR was 69% higher. \u003cstrong\u003e(B) \u003c/strong\u003eAs both sequences share the same nominal resolution, example axial SNR maps show clear signal intensity across a width of 5 voxels (equivalent to 100 mm). \u003cstrong\u003e(C)\u003c/strong\u003e A uniform 100-mm diameter Pi bottle phantom was prepared and used for both measurements.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/6e958c7768dc7a72ab29f349.png"},{"id":54320293,"identity":"ffac76c8-88e5-4b04-ac5a-8085d969b518","added_by":"auto","created_at":"2024-04-08 19:09:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127011,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eLeft\u003c/strong\u003e) Example axial slice raw PCr SNR maps from both acquisitions for one quadriceps subject. Both protocols clearly discriminate between high signal muscle tissue and low signal femur region. (\u003cstrong\u003eRight\u003c/strong\u003e) Unfiltered magnitude spectra from each method in the highlighted muscle (\u003cstrong\u003ered\u003c/strong\u003e) and bone (\u003cstrong\u003egreen\u003c/strong\u003e) voxels\u003cstrong\u003e \u003c/strong\u003escaled to the maximum PCr peak amplitude. Stated spectral SNR is for PCr peak.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/0827eb5a33dfa80613af5c21.png"},{"id":54319978,"identity":"21bdddc3-c1ec-4f45-a42c-ee0350493454","added_by":"auto","created_at":"2024-04-08 19:01:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":99780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eMeasured PCr SNR (mean ± SD) from OXSA-AMARES quantifiable voxels across all five quadriceps subjects using each acquisition scheme and real data (Equation (3)). By this quantification metric, 3D UTE rosette outperforms weighted CSI by approximately 34% \u003cem\u003ein vivo\u003c/em\u003e. \u003cstrong\u003e(B) \u003c/strong\u003eMeasured raw PCr SNR (mean ± SD) from quantifiable voxels across all five quadriceps subjects and bottle phantom using each acquisition scheme and absolute data (Equation (4)). By this quantification metric, 3D UTE rosette outperformed weighted CSI by approximately 69% in phantom and 18% \u003cem\u003ein vivo\u003c/em\u003e. Detailed results are provided in Tables 2 and 3.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/adda52e4a44c0943e463b60e.png"},{"id":77052716,"identity":"683784ec-3759-4227-9548-d31874ceb5f4","added_by":"auto","created_at":"2025-02-24 16:23:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3070620,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4223790/v1/b7ee0d16-2113-49c6-a7cc-801d81c87434.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e3D ultra-short echo time \u003csup\u003e31\u003c/sup\u003eP-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePhosphorous-31 magnetic resonance spectroscopy (\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS), the longest-standing \u003cem\u003ein vivo\u003c/em\u003e MRS modality, can be an invaluable tool for probing \u003cem\u003ein vivo\u003c/em\u003e metabolites such as phosphocreatine (PCr), inorganic phosphate (Pi), phosphomonoesters (PMEs), phosphodiesters (PDEs), and adenosine triphosphate (ATP) .\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e As fundamental phospholipids and consitituents of the high-energy phosphate pathway, these \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP metabolites provide noninvasive measures of tissue pH, lipid metabolism, and oxidative bioenergetics.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Thus, \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS possesses versatile diagnostic and prognostic potential. For instance, elevated PME/PDE ratios and reduced ATP levels have been reported in diseased and cancerous liver tissue, often correlated with classical plasma markers and Child-Pugh scores.\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Furthermore, \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS has been used to assess whole-liver treatment efficacy, monitoring metabolite changes in malignant tissues following therapy.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Likewise, diminished PCr/ATP ratios and post-exercise PCr recovery rates have been measured in cardiac and skeletal muscles of patients with type 2 diabetes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Numerous endeavors have employed \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP MRS in the brain, heart, and muscle, seeking out alterations in neurodegenerative, cardiovascular, metabolic, and oncological diseases.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile relevant \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH-MRS metabolite signals are obscured by background signals such as contaminating fat, water, and macromolecular signals, widely spaced \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP spectral peaks can be easily elucidated due to the absence of these nuisance signals. However, in contrast to \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH-MRS, \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS is burdened by a lower gyromagnetic ratio and relatively short spin-spin metabolite relaxation times (T\u003csub\u003e2\u003c/sub\u003e);\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e these factors engender extremely poor \u003cem\u003ein vivo\u003c/em\u003e relative sensitivity and force a delicate balance between SNR, resolution, and scan duration. Low \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS tissue concentrations (approximately 2 mM γ-ATP in liver\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e) further exacerbate SNR challenges, so that commonly used acquisition delays (T\u003csub\u003eE\u003c/sub\u003e \u0026gt; 300 \u0026micro;s) with conventional methods result in prolonged acquisition, phase distortions, baseline roll, and subsequent operator errors during metabolite quantification. Such complications have been severely limiting factors in the clinical feasibility of \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS. Recent advances in coil engineering and the introduction of ultra-high field (UHF, B\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;3T) scanners have assisted in mitigating these limiting factors; one experiment demonstrated a 2.8-factor increase in PCr SNR at 7T relative to 3T.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Conversely, UHF acquisitions also necessitate larger spectral bandwidth (SBW), with a 40 ppm range requiring approximately 2.0 kHz at 3T but 4.8 kHz at 7T. Still, excessive acquisition durations remain the clear barrier to clinical translation without innovative acceleration.\u003c/p\u003e \u003cp\u003eTo address these points, we propose a three-dimensional (3D), ultra-short echo time (UTE) sequence with a novel rosette k-space trajectory (previously validated in ultra-short-T\u003csub\u003e2\u003c/sub\u003e imaging\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and brain iron mapping\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e) for \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP magnetic resonance spectroscopic imaging (MRSI).\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Compared to conventional CSI Cartesian k-space trajectories, rosette\u0026rsquo;s \u0026ldquo;petal-like\u0026rdquo; pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) maps 3D k-space far more efficiently. Additionally, rosette\u0026rsquo;s relatively inchorent data sampling allows the possibility of significant acceleration through higher undersampling factors and compressed sensing (CS) reconstruction; offering better k-space coverage when compared to radial and spiral trajectories, generalized rosette\u0026rsquo;s curvature affords superior SNR performance under aggressive acceleration.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Furthermore, UTE acquisitions permit the capture of short-T\u003csub\u003e2\u003c/sub\u003e signals before significant transverse signal decay and first-order dephasing occur, enhancing SNR, simplifying spectral pre-processing, and minimizing operator quantification errors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubstantial efforts have been invested towards clinically feasible \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI, experimenting with short repetition times (T\u003csub\u003eR\u003c/sub\u003e), measuring multiple k-space points per T\u003csub\u003eR\u003c/sub\u003e, k-space undersampling, enhanced reconstruction via prior knowledge, and their conceivable combinations.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e 3D extensions of ISIS have shown promise in UHF preclinical and 3T cardiac studies but remain limited by time resolution and motion artifact sensitivity.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Non-localized or FID acquisitions are often preferred to minimize rapid \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP T\u003csub\u003e2\u003c/sub\u003e-decay, but also to overcome specific absorption rate (SAR) limitations at UHFs. Thus, variations of spatial-spectral encoding (SSE) schemes and their synergies with k-space undersampling appear to be the more promising avenue forward; several Cartesian and non-Cartesian acquisition designs offer varying degrees of SNR efficiency, k-space weighting, gradient system demands, and undersampling acceleration potential.\u003c/p\u003e \u003cp\u003eFlyback EPSI has been tested in skeletal calf muscle,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e offering considerable time savings over conventional phase-encoded when combined with CS acceleration;\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e despite its acceleration potential, EPSI offers lower SNR efficiency and SBW at fine resolutions than other SSE options. Density-weighted concentric ring\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e trajectories (CRTs) offer increased SNR efficiency and SBW limits, enabling faster MRSI even with UHF systems. CRTs boast flexibility in weighting and temporal interleaves, allowing tailoring according to acceleration needs and gradient slew rates. Similarly, spiral-encoded \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI has exhibited faster dynamic calf muscle mapping than conventional weighting phase-encoded acquisition.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Though spirals offer high acceleration, SNR efficiency, and customizable weighting, they are also limited by SBW and gradient system hardware.\u003c/p\u003e \u003cp\u003eConventional CSI FIDs commonly possess T\u003csub\u003eE\u003c/sub\u003e on the order of 1\u0026ndash;2 ms, which is restricted by the duration of constant excitation pulses and phase encoding gradients to reach the outermost k-space points. This acquisition delay can be decreased using variable pulse widths and amplitudes. Sampling throughout gradient transition periods, or ramp sampling, remains an additional option for minimizing T\u003csub\u003eE\u003c/sub\u003e. Studies have showcased T\u003csub\u003eE\u003c/sub\u003e as low as 480 \u0026micro;s and 520 \u0026micro;s for EPSI and radial EPSI, respectively.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Acquisitions using such UTE CSI techniques\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e have achieved T\u003csub\u003eE\u003c/sub\u003e = 300 \u0026micro;s at 3T and T\u003csub\u003eE\u003c/sub\u003e = 500 \u0026micro;s at 7T.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e In non-Cartesian center-out k-space trajectories employing ramp sampling, T\u003csub\u003eE\u003c/sub\u003e is primarily limited by the dead time between coil transmit-receive switching, permitting the shortest possible T\u003csub\u003eE\u003c/sub\u003e. Despite this possibility, SBW constraints and conventional sequence parameter (T\u003csub\u003eA\u003c/sub\u003e/FOV) matching have prevented extensive investigation of non-Cartesian UTE \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI.\u003c/p\u003e \u003cp\u003eIn this study, we evaluate the 3D UTE \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI with a novel rosette k-space trajectory by comparing its performance to conventional 3D-weighted \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP CSI in the quadriceps muscle at 3T. We ultimately aim to demonstrate its potential value in clinical spectroscopic acquisitions.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 k-space trajectory designs for MRSI\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 UTE Rosette\u003c/h2\u003e \u003cp\u003eGeneral sequence parameters were as follows: T\u003csub\u003eA\u003c/sub\u003e = 36:00, T\u003csub\u003eR\u003c/sub\u003e = 350 ms, T\u003csub\u003eE\u003c/sub\u003e = 65 \u0026micro;s, matrix size\u0026thinsp;=\u0026thinsp;24x24x24, nominal voxel size\u0026thinsp;=\u0026thinsp;8 mL, FOV = (480x480x480) mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, SBW\u0026thinsp;=\u0026thinsp;2083 Hz, spectral time samples\u0026thinsp;=\u0026thinsp;512. Parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eTable of protocol parameters for conventional 3D weighted CSI, novel 3D UTE rosette MRSI, and additional retrospectively accelerated rosette sequences. Nominal voxel size was matched between methods, with total acquisition time approximately equal between the two full acquisitions. SBWs were matched via interpolation during post-processing.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e SEQUENCE:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUTE Rosette\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted CSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(mm:ss)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36:00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36:56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eR\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 ms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eE\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 \u0026micro;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 ms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Averages\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReconstruction Matrix\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24x24x24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16x16x16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNominal Voxel (mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFOV (mm\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e480x480x480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320x320x320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBandwidth (Hz)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime Samples\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003eA\u003c/b\u003e\u003c/sub\u003e \u003cb\u003erelative to CSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs in prior work,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e 3D UTE rosette k-space trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI was generated with Equations (1) and (2):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${K}_{xy}=Kx\\left(t\\right)+i*Ky\\left(t\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$=\\left(K \\text{m}\\text{a}\\text{x}*\\text{cos}\\left(\\phi \\right)\\right)*\\text{sin}\\left({\\omega }_{1}*t\\right)*{e}^{i{\\omega }_{2}t+\\beta } \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${K}_{z}\\left(t\\right)=\\left(K \\text{m}\\text{a}\\text{x}*\\text{sin}\\left(\\phi \\right))*\\text{sin}\\left({\\omega }_{1}*t\\right) \\right(2)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eK\u003c/em\u003e max is the maximum extent of k-space, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega }_{1}\\)\u003c/span\u003e\u003c/span\u003e is the frequency of oscillation in the radial direction, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\omega }_{2}\\)\u003c/span\u003e\u003c/span\u003e is the frequency of rotation in the angular direction, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\phi\\)\u003c/span\u003e\u003c/span\u003e determines the location in the z-axis, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e determines the initial phase in the angular direction. For this \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI study, \u003cem\u003eK\u003c/em\u003e max\u0026thinsp;=\u0026thinsp;25/m, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\phi\\)\u003c/span\u003e\u003c/span\u003e was sampled uniformly in the range of [-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pi\\)\u003c/span\u003e\u003c/span\u003e/2, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pi\\)\u003c/span\u003e\u003c/span\u003e/2], \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e was sampled uniformly in the range of [0, 2\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pi\\)\u003c/span\u003e\u003c/span\u003e], RF pulse duration\u0026thinsp;=\u0026thinsp;50 \u0026micro;s, readout dwell time\u0026thinsp;=\u0026thinsp;5 \u0026micro;s, and each rosette petal was designed with 96 points. This leads to\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${\\omega }_{1}={\\omega }_{2}=\\frac{\\pi }{\\text{p}\\text{o}\\text{i}\\text{n}\\text{t}\\text{s} \\text{p}\\text{e}\\text{r} \\text{p}\\text{e}\\text{t}\\text{a}\\text{l} \\left({N}_{pp}\\right)\\text{*}\\text{d}\\text{w}\\text{e}\\text{l}\\text{l} \\text{t}\\text{i}\\text{m}\\text{e}}=\\frac{\\pi }{96*5 {\\mu }\\text{s} }=6545 \\text{r}\\text{a}\\text{d}/\\text{s}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eas well as\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{B}\\text{W}=\\frac{1}{{N}_{pp}\\text{*}\\text{d}\\text{w}\\text{e}\\text{l}\\text{l} \\text{t}\\text{i}\\text{m}\\text{e}}=\\frac{1}{96*5 {\\mu }\\text{s}}={\\left(480 {\\mu }\\text{s}\\right)}^{-1}=2083 \\text{H}\\text{z}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eand the resulting \u0026minus;\u0026thinsp;20 to +\u0026thinsp;20 ppm 3T spectral range is more than sufficient for \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS. In reconstruction, each petal was downsampled from \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003epp\u003c/em\u003e\u003c/sub\u003e = 96 points to \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003epp\u003c/em\u003e\u003c/sub\u003e = 48 by averaging the oversampled points. With a 24x24x24 reconstruction matrix, the required number of petals (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e) to satisfy Nyquist criterion was calculated as\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${N}_{p}=4\\pi *{\\left(\\frac{24}{2}\\right)}^{2}=1810$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHowever, due to the rosette\u0026rsquo;s efficient sampling scheme, only 80% coverage (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e = 1444) was defined as full k-space acquisition. Thus, the acquisition time per average was calculated as\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$${\\text{T}}_{\\text{A}}={N}_{p}*{\\text{T}}_{\\text{R}}=1444*350 \\text{m}\\text{s}=505 \\text{s}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eor roughly 9 minutes. A complete description including the influence of trajectory parameters, Nyquist criterion, and the specific gradient ramp-up of this 3D rosette k-space pattern is provided in earlier work (Shen et al.).\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Weighted CSI\u003c/h2\u003e \u003cp\u003eConventional Cartesian 3D acquisitions used the vendor-provided \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP CSI FID with k-space weighting and Hanning filter. Sequence parameters were as follows: T\u003csub\u003eA\u003c/sub\u003e = 36:56, T\u003csub\u003eR\u003c/sub\u003e = 1000 ms, T\u003csub\u003eE\u003c/sub\u003e = 2.3 ms, matrix size\u0026thinsp;=\u0026thinsp;16x16x16, nominal voxel size\u0026thinsp;=\u0026thinsp;8 mL, FOV = (320x320x320) mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, SBW\u0026thinsp;=\u0026thinsp;2200 Hz, spectral time samples\u0026thinsp;=\u0026thinsp;512. Parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Simulations\u003c/h2\u003e \u003cp\u003eTo assess the theoretical performance of the 3D UTE rosette relative to weighted CSI, MATLAB (Mathworks, Natick, USA) simulations were run examining side lobes and SNR relative to the spatial response function (SRF). A simple, constant 3D object was placed at the origin of a 48 x 48 x 48 grid (FOV\u0026thinsp;=\u0026thinsp;480 mm isotropic producing a 1 mL nominal voxel size) with added noise and reconstructed using the non-uniform FFT (NUFFT) method and k-space information for each \u003cem\u003ein vivo\u003c/em\u003e acquisition. We use spatial response function (SRF) instead of point spread function (PSF) since the former specifically estimates side lobes and signal bleed between adjacent voxels, while the latter measures contribution from a single object point to the entire population of voxels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental comparison\u003c/h2\u003e \u003cp\u003eAll data acquisition occurred on a 3T MRI system (Prisma, Siemens, Erlangen, Germany) with G\u003csub\u003emax\u003c/sub\u003e = 80 mT/m and slew rate\u0026thinsp;=\u0026thinsp;200 mT/m/ms isotropically. Human subject protocols were approved by the Institutional Review Boards of Purdue University, and informed consent was obtained. Sequence parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe rosette and conventional acquisitions were tested with a uniform 2-liter bottle phantom (0.17 mg/mL phosphoric acid) using a dual-tuned \u003csup\u003e1\u003c/sup\u003eH/\u003csup\u003e31\u003c/sup\u003eP Tx/Rx flexible 11-cm surface coil (RAPID Biomedical). For \u003cem\u003ein vivo\u003c/em\u003e comparison, five healthy volunteers (BMI\u0026thinsp;=\u0026thinsp;26\u0026thinsp;\u0026plusmn;\u0026thinsp;2 kg/m\u003csup\u003e2\u003c/sup\u003e; age\u0026thinsp;=\u0026thinsp;29\u0026thinsp;\u0026plusmn;\u0026thinsp;5 years; 2 f / 3 m) underwent leg scans with an 8-channel, dual-tuned \u003csup\u003e1\u003c/sup\u003eH/\u003csup\u003e31\u003c/sup\u003eP Tx/Rx phased array coil\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (Stark Contrast, Erlangen, Germany). Quadriceps was chosen for its superior PCr SNR and absence of respiratory motion during prolonged scanning. Subjects were positioned feet-first and supine, with the upper quadriceps tightly surrounded by the coil plates. Following localizer imaging, the adjustment volume was manually positioned (spanning both legs), and linewidth was minimized using a 3D GRE field map and interactive SIEMENS shimming. Each subject was scanned first with the conventional weighted Cartesian acquisition followed uninterrupted by the 3D UTE rosette \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI. Sequence parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Post-processing and reconstruction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRaw data files were exported for reconstruction and pre-processing in MATLAB. Gridding and FFT were completed using adjoint NUFFT (regridding),\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Hanning filtered, and, when necessary, coil-combined using whitened singular value decomposition (wSVD).\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Spectra were zero-order phased by maximizing the integral of the largest peak (PCr, 0 ppm) for the 3D UTE rosette. Spectra from the weighted CSI were zero-order phased and first-order phased to correct a 2.3 ms delay.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 SNR and quantification\u003c/h2\u003e \u003cp\u003eSpectra were fitted within the Oxford Spectroscopy Analysis (OXSA) toolbox\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e using AMARES methods. Metabolite peak SNRs were calculated according to Eq.\u0026nbsp;(3), with noise variance calculated from a residual region lacking metabolite signals. As an additional signal quantification metric, \u0026ldquo;raw SNR\u0026rdquo; (Eq.\u0026nbsp;(4)) was estimated by dividing the highest absolute peak point by the noise variance in an off-spectrum region; this method carries the advantage of consistently assessing signal strength regardless of any interfering spectral phase.\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$${\\text{S}\\text{N}\\text{R}}_{\\text{O}\\text{X}\\text{S}\\text{A}}=\\frac{\\text{P}\\text{e}\\text{a}\\text{k} \\text{S}\\text{i}\\text{g}\\text{n}\\text{a}\\text{l} \\text{F}\\text{i}\\text{t} \\left(\\text{R}\\text{e}\\text{a}\\text{l}\\right)}{\\text{R}\\text{M}{\\text{S}}_{\\text{r}\\text{e}\\text{s}\\text{i}\\text{d}\\text{u}\\text{a}\\text{l} \\text{n}\\text{o}\\text{i}\\text{s}\\text{e}}} \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$${\\text{S}\\text{N}\\text{R}}_{\\text{r}\\text{a}\\text{w}}=\\frac{\\text{M}\\text{a}\\text{x}\\text{i}\\text{m}\\text{u}\\text{m} \\text{P}\\text{e}\\text{a}\\text{k} \\text{A}\\text{m}\\text{p}\\text{l}\\text{i}\\text{t}\\text{u}\\text{d}\\text{e} \\left(\\text{A}\\text{b}\\text{s}\\text{o}\\text{l}\\text{u}\\text{t}\\text{e}\\right)}{\\text{R}\\text{M}{\\text{S}}_{\\text{o}\\text{f}\\text{f}-\\text{s}\\text{p}\\text{e}\\text{c}\\text{t}\\text{r}\\text{u}\\text{m} \\text{n}\\text{o}\\text{i}\\text{s}\\text{e}}} \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eData acquisition, reconstruction, processing, and analysis workflow is summarized in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quantitative analysis\u003c/h2\u003e \u003cp\u003eThe performance of 3D UTE rosette and weighted CSI in phantom solution and quadriceps muscle were assessed using Pi and PCr metabolite signals, respectively. Quantification considered the central, highest signal axial slices within each subject, attempting to quantify every voxel. Only voxels with SNR\u0026thinsp;\u0026gt;\u0026thinsp;3 and OXSA-AMARES Cram\u0026eacute;r-Rao lower bound (CRLB) goodness of fit smaller than 20% for PCr peak were included in the final analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial Response Function simulation comparison of UTE Rosette with Weighted CSI\u003c/h2\u003e \u003cp\u003eThe impact of varying k-space sampling trajectories on image quality can be evaluated via SRF simulations as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. FWHMs along the x-axis at the center of the FOV were comparable between rosette (30.7 mm) and weighted CSI (36.1 mm). Both acquisition schemes exhibit noticeable sidelobe noise, albeit with slightly reduced side lobes in the rosette trajectory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Phantom comparison of UTE Rosette with Weighted CSI\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showcases results and setup of phantom experiments with dual-tuned flexible surface coil. With approximately matched acquisition times, mean raw SNR (Eq.\u0026nbsp;(4)) was 69% higher in 3D UTE rosette than in weighted CSI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eIn vivo\u003c/em\u003e leg comparison of UTE Rosette with Weighted CSI\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows representative 3D UTE rosette and weighted CSI axial PCr maps and spectra in the same volunteer. High-signal muscle regions are clearly distinguishable from low-signal bony femur regions. As expected, PCr predominates the \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP muscle spectrum alongside smaller Pi and ATP peaks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantitative PCr results for all subjects are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, highlighting the different SNRs resulting from Equations (3) and (4) respectively. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarize these distinctions. While 3D UTE rosette consistently outperformed weighted CSI, the advantage was slightly more prominent in AMARES-fitting of the real data at 34% compared to raw SNR of absolute data at 18%.\u003c/p\u003e \u003cp\u003e \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\u003eMean \u003cem\u003ein vivo\u003c/em\u003e PCr SNR from quantifiable voxels in individual subjects using each method. With matched voxel size and acquisition resolution, 3D UTE rosette consistently outperforms weighted CSI acquisition in measured SNR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOXSA-AMARES (Eq.\u0026nbsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUTE Rosette\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted CSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Absolute (Eq.\u0026nbsp;4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUTE Rosette\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWeighted CSI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.50 (\u0026plusmn;\u0026thinsp;3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.51 (\u0026plusmn;\u0026thinsp;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubject 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.19 (\u0026plusmn;\u0026thinsp;25.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.50 (\u0026plusmn;\u0026thinsp;16.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.26 (\u0026plusmn;\u0026thinsp;4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.37 (\u0026plusmn;\u0026thinsp;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubject 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.96 (\u0026plusmn;\u0026thinsp;17.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.77 (\u0026plusmn;\u0026thinsp;14.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.21 (\u0026plusmn;\u0026thinsp;4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.54 (\u0026plusmn;\u0026thinsp;3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubject 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.74 (\u0026plusmn;\u0026thinsp;33.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.31 (\u0026plusmn;\u0026thinsp;18.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.99 (\u0026plusmn;\u0026thinsp;4.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.24 (\u0026plusmn;\u0026thinsp;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubject 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.96 (\u0026plusmn;\u0026thinsp;33.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.75 (\u0026plusmn;\u0026thinsp;20.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubject 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.78 (\u0026plusmn;\u0026thinsp;4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.11 (\u0026plusmn;\u0026thinsp;2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubject 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.35 (\u0026plusmn;\u0026thinsp;27.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.62 (\u0026plusmn;\u0026thinsp;18.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Fitted Voxels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Fitted Voxels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Leg PCr SNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11.95\u003c/b\u003e (\u0026plusmn;\u0026thinsp;4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.95\u003c/b\u003e (\u0026plusmn;\u0026thinsp;3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eOverall Leg PCr SNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e52.83\u003c/b\u003e (\u0026plusmn;\u0026thinsp;28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e44.95\u003c/b\u003e (\u0026plusmn;\u0026thinsp;17.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean PCr SNR from quantifiable voxels across all five quadriceps subjects and bottle phantom using each method. With matched voxel size and acquisition resolution, 3D UTE rosette consistently outperforms weighted CSI acquisition in measured SNR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEQUENCE:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUTE Rosette\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted CSI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUTE/CSI SNR Ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOXSA-AMARES Real \u003cem\u003ein vivo\u003c/em\u003e PCr SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11.95\u003c/b\u003e (\u0026plusmn;\u0026thinsp;4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.95\u003c/b\u003e (\u0026plusmn;\u0026thinsp;3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw Absolute \u003cem\u003ein vivo\u003c/em\u003e PCr SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e52.83\u003c/b\u003e (\u0026plusmn;\u0026thinsp;28.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e44.95\u003c/b\u003e (\u0026plusmn;\u0026thinsp;17.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw Absolute Phantom SNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e52.48\u003c/b\u003e (\u0026plusmn;\u0026thinsp;34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e31.00\u003c/b\u003e (\u0026plusmn;\u0026thinsp;9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Experimental overview\u003c/h2\u003e \u003cp\u003eThis study demonstrates the feasibility of using 3D UTE \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI with a novel rosette k-space trajectory to acquire quality \u003cem\u003ein vivo\u003c/em\u003e human subject data. Simulations showed the rosette trajectory produced acceptable image quality and SRF characteristics when compared to a conventional 3D weighted CSI acquisition. Experimental phantom scans utilized a uniform Pi bottle solution and the same scanning parameters later applied to \u003cem\u003ein vivo\u003c/em\u003e quadriceps subjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 UTE advantages\u003c/h2\u003e \u003cp\u003eThe novel acquisition\u0026rsquo;s 70 \u0026micro;s acquisition delay is substantially lower than the 300\u0026ndash;500 \u0026micro;s delays in previously published UTE \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP CSI methods\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, minimizing transverse signal decay and first-order dephasing. Accurate phasing is key to spectral fitting and quantification of real spectral data; when fitting parameters must be tailored to hundreds of voxels across a large volume, such as for high resolution 3D MRSI, the challenge of avoiding phasing errors is most apparent. As expected, all 3D UTE rosette data were intrinsically devoid of noticeable first-order phasing, thereby streamlining the quantification process. The SNR gap between 3D UTE rosette and weighted CSI acquisitions was narrowed when solely considering absolute data (Eq.\u0026nbsp;(4)). This distinction might be partially explained by the absence of phase in these magnitude spectra, whereby the 3D UTE rosette (T\u003csub\u003eE\u003c/sub\u003e = 70 \u0026micro;s) acquisition loses a portion of its advantage over conventional weighted CSI (T\u003csub\u003eE\u003c/sub\u003e = 2.3 ms). Notably, SDs for 3D UTE rosette SNR were significantly higher (50% or more) compared to weighted CSI. This elevated variation is partially explained by the novel acquisition\u0026rsquo;s significantly higher SNR; moreover, weighted CSI\u0026rsquo;s wider SRF engenders higher inter-voxel crosstalk, diminishing overall variation among quantified voxels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Acceleration potential\u003c/h2\u003e \u003cp\u003eAlthough these 36-minute acquisitions are quite lengthy, conventional ungated \u003cem\u003ein vivo\u003c/em\u003e 3D \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI typically requires a minimum of 20 minutes at 3T. Compared to a weighted Cartesian trajectory, this novel rosette k-space pattern\u0026rsquo;s relative incoherence makes it a very suitable candidate to CS acceleration via undersampling. Applying undersampling factors of 2 to 4, as demonstrated previously in uT\u003csub\u003e2\u003c/sub\u003e brain imaging\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, could reduce \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI\u0026rsquo;s TA to 9\u0026ndash;18 minutes (or less with fewer averages).\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e Such an acceleration would allow implementation of \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS within realistic clinical constraints, while also being translatable to UHF research systems and higher resolutions.\u003c/p\u003e \u003cp\u003eAs with all \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS, spectral quality can also see potential improvement via proton decoupling and nuclear Overhauser effect (NOE) enhancement, albeit with implications for SAR and measured metabolite ratios. Furthermore, appropriately applied low-rank approximation and principal component analysis denoising have seen use in heightening SNR of MRSI data sets;\u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e nevertheless, in the absence of ground truths or precise simulation, care must be taken in estimating metabolite concentration uncertainties after denoising.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Resolution and SBW\u003c/h2\u003e \u003cp\u003eMany non-Cartesian acquisitions face restrictions in spatial resolution, SBW, and SNR due to available gradient hardware.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e For example, spiral trajectories face reduced SNR while waiting to return to k-space center between spirals; this inefficiency is addressed by closed-loop, out-in trajectories, but these remain impractical outside UHF animal gradient systems.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Concentric rings can be similarly adjusted to meet needs with temporal interleaves.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStill, SBW limitations remain a significant challenge; while 2.0 kHz might be sufficient for \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRS at 3T, such a SBW would only offer a spectral range of around 17 ppm at 7T. While this rosette acquisition sampled 48 points per petal every 480 \u0026micro;s, the sequence remains highly customizable. By leveraging the second half of each petal (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), it is possible to partially satisfy Nyquist criterion at even higher bandwidths and enable finer resolution reconstructions than the relatively coarse 8 mL voxels shown here. Additionally, this permits greater SBW acquisitions, opening the door to 3D UTE rosette \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI at UHF and \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH MRSI at 3T. However, these petal halves are analogous to odd and even echoes of EPSI MRSI; since the timings between individual N\u003csub\u003epp\u003c/sub\u003e are not equidistant, \u0026ldquo;full-petal\u0026rdquo; spectra will suffer from some degree of noise amplification and aliasing artifact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Other limitations\u003c/h2\u003e \u003cp\u003eFurther experimentation is required in exploring the potential and limitations of 3D UTE rosette MRSI. Notably, these quadriceps scans focused on quadriceps muscle with plentiful PCr signal in a healthy volunteer population. However, \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI is frequently applied in measuring diverse brain, cardiac, and liver spectra, where nearby tissues may introduce contaminating metabolite signals. Minimal signal contamination was observed in noisy voxels within bony regions. Nonetheless, due to the relative uniformity of skeletal muscle spectra, it would be difficult to discern the rosette acquisition\u0026rsquo;s relatively incoherent aliasing. Future work will aim to assess accelerated performance in patient populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eUsing the quadriceps of five healthy volunteers at 3T, we investigated a potential application to \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003eP-MRSI using a novel 3D UTE rosette sequence. In comparison to a conventional 3D weighted CSI with matched bandwidth, nominal resolution, and acquisition time, the novel rosette acquisition provided competitive resolution and superior SNR with straightforward quantification. As this proof-of-concept study was limited to five subjects and a relatively homogeneous region of PCr-plentiful muscle, additional testing is required to demonstrate efficacy in differentiating diverse and diseased tissue regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization by U.E. \u0026amp; U.D.; Methodology contributions from all authors; Data collection by B.B., X.S. \u0026amp; U.E.; Data analysis by B.B., U.E., X.S. \u0026amp; W.C.; Results and conclusions reviewed and agreed upon by all authors. First manuscript draft and revision by B.B., U.E. \u0026amp; X.S.; Final revision and approval by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by grants to U.E. and S.S. from the Wellcome Trust Collaborative Award (223131/Z/21/Z). This project was funded with support from the Indiana Clinical and Translational Sciences Institute, funded in part by Award Number UM1TR004402 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available from the authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAckerman JJH, Grove TH, Wong GG, Gadian DG, Radda GK. 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Whole-Slab 3D MR Spectroscopic Imaging of the Human Brain With Spiral-Out-In Sampling at 7T. \u003cem\u003eJ Magn Reson Imaging\u003c/em\u003e. 2021;53(4):1237-1250. doi:10.1002/JMRI.27437\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4223790/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4223790/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhosphorus-31 magnetic resonance spectroscopic imaging (\u003csup\u003e31\u003c/sup\u003eP-MRSI) provides valuable non-invasive \u003cem\u003ein vivo\u003c/em\u003e information on tissue metabolism but is burdened by poor sensitivity and prolonged scan duration.\u0026nbsp; Ultra-short echo time (UTE) acquisitions minimize signal loss when probing signals with relatively short spin-spin relaxation time (T\u003csub\u003e2\u003c/sub\u003e), while also preventing first-order dephasing.\u0026nbsp; Here, a three-dimensional (3D) UTE sequence with a rosette k-space trajectory is applied to \u003csup\u003e31\u003c/sup\u003eP-MRSI at 3T.\u0026nbsp; Conventional chemical shift imaging (CSI) employs highly regular Cartesian k-space sampling, susceptible to substantial artifacts when accelerated via undersampling.\u0026nbsp; In contrast, this novel sequence’s “petal-like” pattern offers incoherent sampling more suitable for compressed sensing (CS).\u0026nbsp; These results showcase the competitive performance of UTE rosette \u003csup\u003e31\u003c/sup\u003eP-MRSI against conventional weighted CSI with simulation, phantom, and \u003cem\u003ein vivo\u003c/em\u003e leg muscle comparisons.\u003c/p\u003e","manuscriptTitle":"3D ultra-short echo time 31P-MRSI with rosette k-space pattern: Feasibility and comparison with conventional weighted CSI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 19:01:07","doi":"10.21203/rs.3.rs-4223790/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-28T05:10:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-24T01:28:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T15:14:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149331709117768024381738549192581509601","date":"2024-05-21T06:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320084658954481706232353606675673998441","date":"2024-05-15T20:31:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-13T12:54:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-06T12:27:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-19T05:43:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-19T05:30:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-05T15:14:06+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":"7f98f31b-367b-4976-83b2-6105566f7e66","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30385497,"name":"Health sciences/Health care/Medical imaging/Magnetic resonance imaging"},{"id":30385498,"name":"Health sciences/Medical research/Translational research"}],"tags":[],"updatedAt":"2025-02-24T16:05:01+00:00","versionOfRecord":{"articleIdentity":"rs-4223790","link":"https://doi.org/10.1038/s41598-025-90630-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-22 15:58:06","publishedOnDateReadable":"February 22nd, 2025"},"versionCreatedAt":"2024-04-08 19:01:07","video":"","vorDoi":"10.1038/s41598-025-90630-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90630-y","workflowStages":[]},"version":"v1","identity":"rs-4223790","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4223790","identity":"rs-4223790","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}