Influence of Fat on Hepatic T2 Relaxation Time Estimation: A Preliminary Investigation

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Tkach, Adam Prasanphanich, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4887537/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Oct, 2024 Read the published version in Abdominal Radiology → Version 1 posted 9 You are reading this latest preprint version Abstract PURPOSE: To examine the influence of fat content on hepatic T2 estimation through T2 mapping without and with fat suppression. MATERIALS AND METHODS: This prospective IRB-approved study included participants without known liver disease (controls), with hepatic steatosis (proton density fat fraction [PDFF] >6%), or with fibrotic (> 3kPa) liver from other chronic liver diseases. Imaging at 1.5T included a quantitative 3D chemical shift encoded six-echo radiofrequency spoiled gradient echo MRI sequence (mDIXON Quant) for PDFF estimation, nine-echo 2D gradient and spin echo (GRASE) for T2 estimation, and nine-echo 2D GRASE with fat suppression for water-specific T2 (wT2) estimation. A single blinded observer traced a large freehand region-of-interest (ROI) in the right hepatic lobe on the T2 and wT2 maps, and a circular ROI on the PDFF map. Consistency and agreement of T2 and wT2 estimates were assessed using linear regression, intra-class correlation coefficients (ICC), and Bland-Altman analysis. Pearson’s correlation evaluated the relationship between differences in hepatic T2 and wT2 estimates and PDFF. RESULTS: A total of 21 participants (7 controls, 9 with hepatic steatosis, 5 with other liver diseases) with a mean age of 18.4±4.7 years (range: 9-27 years; 9 males) were included. Estimated liver PDFF ranged from 2% to 34% (median 4%, IQR 7.5%). T2 estimates (57±7.3 ms [range: 44.5-70.5 ms]) were significantly longer ( p =0.0085) compared to wT2 estimates (52.5±8 ms [range: 43-69.7 ms]) with moderate agreement (ICC = 0.57 [95% CI: 0.19 - 0.80]). For participants with hepatic steatosis, T2 estimates were significantly longer than wT2 estimates ( p =0.0063) with poor agreement (ICC=0.06 [95% CI: -0.59 – 0.67]). Participants without hepatic steatosis showed comparable T2 and wT2 estimates ( p =0.3577, p =0.3954) with excellent agreement (ICC = 0.99 [95% CI: 0.96 - 0.99]). The relative bias of T2 to wT2 was very strongly correlated with PDFF ( r 2 = 0.95), increasing by 0.8 ms (1.22%) for every 1% rise in PDFF. CONCLUSION: Liver fat content proportionally increases estimated T2, potentially confounding the quantification of changes in T2 that can be attributed to alteration in tissue water content. Liver T2 mapping T2 relaxation proton density fat fraction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Non-invasive quantitative magnetic resonance imaging (MRI) can help to objectively stage disease and subsequent progression based on pathological alteration in tissue composition and architecture consequent to disease processes and/or responses to therapies and interventions. In light of the clinical impact of quantitative MRI for the myocardium (1, 2), clinical interest in quantitative MRI has expanded to other organs (3). Accordingly, the clinical efficacy of quantitative MRI is increasingly being evaluated for the comprehensive assessment of hepatic parenchyma in diffuse liver diseases. Early detection and management of liver inflammation and fibrosis are crucial to prevent the progression to cirrhosis and reduce the risk of complications such as portal hypertension, ascites, varices, and liver failure (4). Robust estimation of liver stiffness, proton density fat fraction, and iron quantification with MRI has successfully translated from research into clinical practice (5, 6). The potential clinical utility of relaxation time mapping, including T2 mapping, is being explored in several liver diseases, including steatosis, cirrhosis, and fibrosis (3, 7-11). However, relaxation processes are known to be influenced by various factors including iron deposition, fat deposition, inflammation, and fibrosis (5, 7, 10), complicating the precise identification of the underlying causes of alterations in relaxation times. In the setting of chronic liver disease, patients can often have overlapping histologic features including intra- and/or extracellular edema, extracellular collagen deposition, intracellular iron, and intracellular lipid deposition. For example, in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), histologic features include macro-vesicular steatosis, lobular inflammation, and often some degree of fibrosis (12). These processes have varying effects on the estimation of hepatic T2, depending on the technique utilized for obtaining T2 estimates (13). Hepatic T2 estimates have been reported to increase in association with the advancing histologic degree of steatosis and/or fibrosis and coexistent inflammation (14, 15). Therefore, the resultant changes in T2 estimates in these patients are non-specific and can be difficult to interpret. When evaluating for edema as a manifestation of inflammation or congestion, the presence of steatosis and/or fibrosis likely confounds T2 estimates. T2 mapping sequences with and without fat suppression could potentially be beneficial to identify and eliminate the influence of hepatic steatosis as a contributing factor when attempting to quantify changes in tissue water content. In this study, we aimed to examine the influence of fat content on the hepatic T2 using a fast multi-echo 2D Gradient and Spin Echo (GRASE) sequence (13, 16). Although the echo planar imaging (EPI) readout used in this GRASE acquisition can result in some T2* weighting, the technique allows T2 mapping with sufficient spatial resolution in reasonable breath-hold times, both without (T2) and with fat suppression (wT2). Methods This HIPAA-compliant study prospectively acquired liver MR images from research participants under Institutional Review Board (IRB) approval (IRB ID 2022-0108). Written informed consent was obtained from participants or from parents/guardian caretakers where applicable. These same patients underwent additional imaging during the same study visit to evaluate different hepatic quantitative mapping sequences for other studies, including T1 mapping and a novel multi-inversion multi-echo spin and gradient echo (MI-SAGE) sequence in the liver. Study Participants Both asymptomatic individuals without known liver disease (controls) and those with known liver disease were recruited for this study. Controls were recruited through hospital-wide emails. A search of Department of Radiology records was used to identify and recruit patients who had previously undergone diagnostic imaging at our institution for fibrotic, autoimmune, or fatty liver disease. Inclusion criteria for the study included age between 8-80 years, the ability to lie still in the MRI scanner and perform breath-holds of at least 15 seconds, and no contraindications to MRI. The aim was to recruit approximately one-third of participants with known hepatic steatosis (HS; proton density fat fraction [PDFF] ≥6% on prior MRI) (17), one-third with fibrotic (> 3kPa) liver from various other chronic liver diseases, and the remaining, asymptomatic individuals without known liver disease (controls). Participants were prospectively reassigned to the hepatic steatosis subgroup, as appropriate, if research liver PDFF was ≥6% (17). MR Image Acquisition All MR imaging was performed on a 1.5-T clinical MR scanner (Ingenia; Philips Healthcare; Best, the Netherlands) using a 28-element torso coil for signal reception. The quantitative imaging sequences included a 3D chemical shift-encoded six-echo radiofrequency spoiled gradient echo MRI (mDIXON Quant) acquisition for quantitative PDFF estimation, and a multi-echo 2D GRASE with and without fat suppression for quantitative wT2 and T2 estimation, each performed within a single 15 second end-expiration breath-hold. For wT2 and T2 2D GRASE acquisitions, a single slice was positioned to encompass the widest part of the mid-liver in the transverse plane. The 3D mDIXON Quant transverse slab was positioned so that the location of the central slices of the volume matched that of the T2 and wT2 2D GRASE slices. For all three acquisitions, the in-plane field of view was 440x440 mm 2 with an acquired in-plane spatial resolution of 3x3 mm 2 and a sensitivity encoding (SENSE) acceleration factor of 2. The T2 mapping GRASE acquisition used 9 spin echoes acquired with an EPI factor of 7 (one startup echo, time of the first spin echo =15 ms, spin echo spacing = 7.4 ms) with a repetition time (TR) of 1000 ms and a slice thickness of 10 mm. The wT2 mapping GRASE acquisition used the same parameters but included an addition of a Spectral Presaturation with Inversion Recovery (SPIR) pulse for fat suppression. The 3D mDIXON Quant acquired 6 gradient echoes (first TE=1.2 ms, echo spacing = 0.8 ms) with a flip angle of 5 0 , a TR of 6.5 ms and a slice thickness of 6 mm covering 24 cm in the caudocephalad direction. The PDFF, wT2 and T2 maps were computed and produced by FDA approved software on the scanner. Image Analysis All liver MRI data, including the scanner-generated PDFF, wT2, and T2 estimate maps, were exported in standard DICOM format to a commercial post-processing workstation (IntelliSpace Portal v10.1; ISP, Philips Healthcare; Best, the Netherlands) for image analysis. Manual measurements of PDFF, wT2, and T2 were made by a blinded clinical pediatric radiology fellow . A single large freehand ROI was drawn on the liver wT2 and T2 maps encompassing as much of the right hepatic lobe as possible. A circular ROI was placed in the right lobe of the liver at the corresponding slice of the PDFF map. The liver capsule, parenchyma edge, visible blood vessels, bile ducts, and areas of visible artifacts were avoided while drawing ROIs (Figure 1 ). The mean and standard deviation of the measured values within each ROI were recorded. Statistical Analysis Continuous data were summarized as means and standard deviations, or medians and interquartile ranges, as appropriate. Linear regression, intra-class correlation coefficients (ICC), and Bland-Altman analysis were employed to assess the consistency and agreement of T2 and wT2 estimates. Pearson’s correlation was computed to determine the relationship between differences in hepatic T2 and wT2 estimates as the dependent variable and the participant’s estimated PDFF as the independent variable. A p-value <0.05 was considered significant for all inference testing, and 95% confidence intervals were calculated as appropriate. ICC values were interpreted as follows: 0.90, excellent agreement (18). Correlation coefficients were interpreted as follows: 0–0.19, very weak; 0.2–0.39, weak; 0.40–0.59, moderate; 0.60–0.79, strong; and 0.80–1.0, very strong (19). All statistical analyses were performed using MATLAB (The MathWorks TM Inc., Natick, Massachusetts, USA). Results Twenty-one participants (9 males, 12 females; mean age 18.4±4.7 years [range: 9–27 years]) successfully underwent research liver MR imaging. Baseline patient diagnoses included 7 control participants, 6 participants with MASLD, 2 participants with metabolic dysfunction associated steatohepatitis (MASH), 1 participant with steatosis and autoimmune hepatitis, and an additional 5 participants with other non-steatotic hepatobiliary pathologies (9 steatotic participants and 12 non-steatotic participants in total). Patient characteristics are summarized in Table 1 . Figure 1 presents the quantitative maps for PDFF, wT2, and T2 for four representative study subjects. Participant liver PDFF ranged from 2% to 34% (median 4%, IQR 7%). There was a moderate agreement (ICC = 0.57 [95% CI: 0.19 - 0.80]) between the T2 and wT2 estimates across all participants with the T2 estimates (57±7.3 ms (range: 44.5-70.5 ms)) significantly longer ( p =0.0085) compared to the wT2 estimates (52.5±8 ms (range: 43-69.7 ms)) for all participants. In the participants without hepatic steatosis (controls and participants with other liver diseases), liver PDFF ranged from 2% to 5% (3.4±0.8%), whereas in participants with hepatic steatosis, liver PDFF ranged from 7% to 34% (median 12%, IQR 14.8% 16.1±9.4 %). The distributions of estimated T2 and wT2 in the controls, participants with hepatic steatosis, and participants with other liver diseases are depicted in Figure 2 . T2 estimates were significantly ( p =0.0063) longer than wT2 estimates in participants with hepatic steatosis, while these values were not significantly different in controls ( p =0.3577) and participants with other liver diseases ( p =0.3954). Therefore, for further analysis, controls and participants with other liver diseases were grouped together as a non-steatotic group. The linear regression and Bland-Altman plots for T2 and wT2 estimates for participants with and without hepatic steatosis are depicted in Figure 3 . There was excellent agreement (ICC = 0.99 [95% CI: 0.96 – 0.99]) between the T2 and wT2 estimates in the non-steatotic group with the negligible mean bias of 0.45 ms (95% limits of agreement; -1.7 to 2.6 ms). However, there was poor agreement (ICC = 0.06 [95% CI: -0.59 – 0.67]) between estimates in participants with hepatic steatosis. In participants with hepatic steatosis, a non-significant ( p =0.133) proportional bias was observed with relative overestimation of T2 compared to wT2. Figure 4 depicts linear regression plots for T2 and wT2 estimates against PDFF for participants with and without hepatic steatosis. In participants with hepatic steatosis, there was a strong correlation ( r 2 = 0.7) of estimated T2 with PDFF with 0.66ms increase in estimated T2 for every 1% rise in PDFF. In the non-steatotic group there was a very weak ( r 2 = 0.18) correlation of estimated wT2 with PDFF. Figure 5 illustrates the linear regression plots for overestimation of T2 compared to wT2 against PDFF for all participants. The overestimation of T2 relative to wT2 across all participants was very strongly ( r 2 = 0.95) correlated with PDFF, showing an increase of 0.8 ms (1.22%) for every 1% rise in PDFF. Discussion Our study compared hepatic T2 estimates obtained without fat suppression (T2) to water-specific T2 (wT2) estimates obtained with fat suppression using multi-echo 2D GRASE sequence in a sample of participants exhibiting a range of liver PDFF similar to those observed in routine clinical practice. Across all participants, we found moderate agreement between T2 and wT2 estimates, with the T2 estimates being significantly longer compared to wT2 estimates. In the subgroup of participants without hepatic steatosis, there was excellent agreement between the T2 and wT2 estimates. In contrast, among participants with hepatic steatosis, there was poor agreement between T2 and wT2 estimates, with a strong correlation between liver PDFF and the extent of overestimation of T2 compared to wT2. Specifically, we observed an increase of 0.8 ms for every 1% increase in PDFF across all participants. These results highlight the potential contributing effect of hepatic fat on T2 estimates when characterizing tissue water accumulation as a manifestation of diffuse liver disease. Our findings are concordant with those of Idilman et al. who demonstrated a stepwise increase in T2 estimates as the histologic degree of hepatic steatosis increased (15). Although we demonstrated that fat content contributes to elevated T2 estimates, several other tissue characteristics also elevate T2 estimates. This is evident in estimates for several participants without steatosis where prolonged T2 estimates were identified regardless of whether fat suppression was applied or not. For instance, the two participants with FALD who were recruited for this study exhibited PDFF values of 2 to 3%. However, the T2 estimates for both patients were significantly elevated (>65 msec) irrespective of whether fat suppression was applied or not (as shown in Figure 1 ). This likely reflects venous and lymphatic congestion consequent to the shunting of the blood from inferior vena cava to the pulmonary circulation after the Fontan procedure, with associated elevation of central venous pressure and eventual development of liver fibrosis (20). Fibrosis was also shown by Guimaraes et al. to be associated with a stepwise increase in T2 estimates as the histologic degree of fibrosis (Ishak classification) increased (14). This suggests that while water-specific hepatic T2 estimates may contribute to the differential diagnosis of diffuse liver diseases, multiple factors likely contribute to T2 prolongation, including inflammation and/or fibrosis. Water, fat, and iron content may change simultaneously during the progression of liver disease. An increase in fat fraction or intra- and extracellular water can concurrently prolong hepatic T2, while an increase in iron content can lead to T2 underestimation. Therefore, minimizing, correcting, and interpreting these individual parameters in conjunction with each other should enhance the differential diagnosis of liver diseases and improve treatment monitoring over time. When evaluating estimated T2 values, it is advisable to obtain both a T2 and a water-sensitive T2 map to assess the contributing effect of fat content. This approach can potentially help differentiate the alteration in water and fat content due to various diffuse disease processes. Overall, estimated hepatic T2 values should be interpreted in conjunction with estimates of other individual parameters, including PDFF, T1, and T2*. The findings of this study emphasize the importance of considering the combined influence of fat and water content on hepatic T2 alterations. We utilized the GRASE acquisition technique because it enables T2 mapping with adequate spatial resolution, reasonable breath-hold times, and ability to suppress fat, thereby allowing for comparison between T2 and wT2. Although the GRASE technique is sensitive to relatively short T1 and T2* values due to the EPI readout (13), it provides valuable insights into the contributions of water and fat components to the T2 estimation. Apart from the SPIR fat suppression applied for wT2 estimation, identical methods—including the pulse sequence, imaging parameters, and fitting to exponential decay model—were used for both T2 and wT2 estimation. This ensures that other confounding factors, including T1 and T2* effects, remain consistent for both estimates, making the observed differences in T2 and wT2 estimates primarily attributable to fat content. However, other effects such as magnetization transfer of the SPIR pulse and variations in fat suppression across the field of view may still be present and result in small contributions to the differences in T2 and wT2 estimates. The influence of fat content on hepatic T2 estimation will likely vary across different T2 mapping approaches, depending on the acquisition technique (e.g., spin echo, fast spin echo, GRASE, T2-prepared; MR fingerprinting), the sequence parameters (e.g., number of echoes, time between echoes, T2-prep durations), and the algorithms used (e.g., fitting exponential decay model, dictionary matching). There are a few limitations to this study, including its single-center, single-field strength, single-scanner, single-vendor design, and the evaluation of only 21 research participants. Additionally, although there was a representative, balanced mixture of individuals with healthy, steatotic, and other diseased livers, there was no histological validation in these participants to determine the underlying factors driving alterations in estimated T2 values. Lastly, the influence of fat on T2 estimation was evaluated only using the GRASE technique. The influence of fat on T2 estimates using other T2 mapping approaches will likely differ and should be similarly evaluated. Nonetheless, the study findings provide sufficient evidence to warrant further prospective clinical evaluations of the influence of fat on hepatic T2 estimation in larger samples of patients that encompass a wide range and variety of diseases. Conclusion Liver fat content proportionally increases estimated T2 potentially confounding quantification of changes in T2 that can be attributed to alteration in tissue water content. 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Pediatr Radiol 2020;50(11):1528-1541. doi: 10.1007/s00247-020-04776-0 Table 1 Table 1: Characteristics of the study population Number of patients 21 Age (year) 18.4±4.7 (9-27) Female-to-male ratio 9:12 ­Height (cm) 165.9±10.5 (142-188) Weight (kg) 81.7±35.0 (38-165) BSA (m 2 ) BMI (kg/m 2 ) 1.91±0.44 (1.29-2.86) 29.1±10.2(16.2-51.9) Clinical Indications Asymptomatic 7 MASLD 6 MASH 2 Primary sclerosing cholangitis 2 Fontan-associate liver disease 2 Autoimmune hepatitis Alagille syndrome 1 1 The data are presented as the mean ± standard deviation (minimum – maximum) or as the number of subjects. BSA = body surface area, BMI = body mass index, MASLD = metabolic associated steatotic liver disease, MASH = metabolic dysfunction associated steatohepatitis. Additional Declarations Competing interest reported. JK: No disclosures MKM: No disclosures JT: No disclosures AP: No disclosures AT:Consulted for GE Healthcare; has received research support from GE Healthcare, Siemens Healthineers, and Perspectum Inc. No support was received for the current work. JD: Received unrelated in-kind research support from Perspectum, Philips Healthcare, GE HealthCare, Motilent, Guerbet, and Bracco Imaging. No support was received for the current work. MKM: No disclosures ASP: No disclosures Cite Share Download PDF Status: Published Journal Publication published 12 Oct, 2024 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 29 Aug, 2024 Reviews received at journal 28 Aug, 2024 Reviews received at journal 20 Aug, 2024 Reviewers agreed at journal 12 Aug, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers invited by journal 10 Aug, 2024 Editor assigned by journal 09 Aug, 2024 Submission checks completed at journal 09 Aug, 2024 First submitted to journal 09 Aug, 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-4887537","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347074962,"identity":"5c261068-7610-46c0-89df-ae63f10d71c5","order_by":0,"name":"Justine Kemp","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACxgYgkQDEEiDeBwiboBbGBpgWxhnEaIFZBNbCzEOMFuYZ6c8fPNxhwyDZ3p322KbGLk+3/QDj44pfeKyYkWPYkHgmjUGa5+x245xjycVmZxKYDc/24dXC2JDYdphBTiJ3m3Ruw4HEbTcY2CQbe/BpSX8I1PKfQU7+7TZpS+K0JAAd1naAQVqCd5s0I0xLww88WnreGM5IbEvmkezJ3W7Ycyw5cduZxGbDxgbcWgzb0x98/NlmJydx/Oy2Bz9q7BK3HT988GHDHzxaoMYBY4SBDWYzMHrbcGuRR2KzIbHx2DIKRsEoGAUjDgAABLRXOpY5CPkAAAAASUVORK5CYII=","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Justine","middleName":"","lastName":"Kemp","suffix":""},{"id":347074963,"identity":"75bbe363-06b2-4a66-a360-2bda7483817b","order_by":1,"name":"Mary Kate Manhard","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"Kate","lastName":"Manhard","suffix":""},{"id":347074964,"identity":"e63de615-3efc-493d-80e2-bea655a14aae","order_by":2,"name":"Jean A. Tkach","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"A.","lastName":"Tkach","suffix":""},{"id":347074965,"identity":"3fb2a002-45c0-44ec-b3e7-ab2c1bbc5c5b","order_by":3,"name":"Adam Prasanphanich","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Prasanphanich","suffix":""},{"id":347074966,"identity":"52dfb25f-f423-4d70-b460-843608a387a4","order_by":4,"name":"Andrew Trout","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Trout","suffix":""},{"id":347074967,"identity":"859361da-d65f-465f-8177-e4821196fab5","order_by":5,"name":"Johnathan Dillman","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Johnathan","middleName":"","lastName":"Dillman","suffix":""},{"id":347074968,"identity":"79a54632-e386-4499-8e05-a7f88fb3aee6","order_by":6,"name":"Amol Pednekar","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Amol","middleName":"","lastName":"Pednekar","suffix":""}],"badges":[],"createdAt":"2024-08-09 13:46:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4887537/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4887537/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-024-04623-y","type":"published","date":"2024-10-12T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66117424,"identity":"d5a7e936-614b-4ce9-a869-fa50006a1b65","added_by":"auto","created_at":"2024-10-08 00:53:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142052,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images from 4 subjects (columns) with ROIs and mean values obtained for PDFF (yellow), T2 and wT2 (dark blue). The top row shows the Water image, and the second row displays the PDFF map from mDIXON Quant. The third row presents the T2 map obtained without fat suppression, and the bottom row shows the wT2 map obtained with fat suppression. The columns show: Control (12-year-old female), FALD (12-year-old female), MASLD (18-year-old female), and MASLD (22-year-old male) from left to right. In the patient with FALD, T2 estimates remain elevated regardless of whether fat suppression was applied or not. However, in the patients with MASLD, wT2 estimates appear significantly lower. Abbreviations: ROI = region of interest, FF = proton density fat fraction (PDFF) map from 3D six-echo fat quantification MRI (mDIXON Quant), FALD = Fontan associated liver disease, MASLD = metabolic associated steatotic liver disease, MASH = metabolic dysfunction associated steatohepatitis, T2 = estimated T2 values without fat suppression, and wT2 = estimated water-specific T2 values with fat suppression, W – water image from mDIXON Quant.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887537/v1/8d17dc790aaa21d28a35ac76.jpg"},{"id":66117425,"identity":"d63d8f1e-872f-4c2f-a178-7adc8c56059a","added_by":"auto","created_at":"2024-10-08 00:53:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47836,"visible":true,"origin":"","legend":"\u003cp\u003eTukey box plots of T2 (left) and wT2 (right) estimates measured in the three different participant groups. Center red line = median, whiskers = minimum and maximum within 1.5 times interquartile distance, red ★ = outliers beyond 1.5 times the interquartile distance. Non-overlapping notches indicate that the medians of the two groups differ at the 5% significance level. HS = participants with hepatic steatosis, otherLD = participants with other liver diseases, T2 = estimated T2 values without fat suppression; wT2 = estimated water-specific T2 values with fat suppression.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887537/v1/aa2550412b6689319c95d909.jpg"},{"id":66117428,"identity":"d977968b-8068-499d-98fc-58e0df153fbd","added_by":"auto","created_at":"2024-10-08 00:53:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70987,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression (left) and Bland-Altman (right) plots comparing T2 and wT2 estimates measured in HS (blue edges and lines) and nonHS (black edges and lines) participants. AH = autoimmune hepatitis, AL = Alagille syndrome, FALD = Fontan associated liver disease, HS = participants with evidence of hepatic steatosis, nonHS = controls and participants with other liver diseases, MASLD = metabolic associated steatotic liver disease, \u0026nbsp;MASH = metabolic dysfunction associated steatohepatitis, PSC = primary sclerosing cholangitis, T2 = estimated T2 values without fat suppression; \u0026nbsp;wT2 = estimated water-specific T2 values with fat suppression.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887537/v1/9113094b4fc0a64fed633723.jpg"},{"id":66117426,"identity":"92ed02ae-57b4-43b1-9356-f9e2d84fd3e9","added_by":"auto","created_at":"2024-10-08 00:53:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56175,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression plots of T2 (left) and wT2 (right) estimates against PDFF measured in HS (blue edges and line) and nonHS (black edges) participants. AH = autoimmune hepatitis, AL = Alagille syndrome, FALD = Fontan associated liver disease, HS = participants with evidence of hepatic steatosis, nonHS = controls and participants with other liver diseases, MASLD = metabolic associated steatotic liver disease, \u0026nbsp;MASH = metabolic dysfunction associated steatohepatitis, PSC = primary sclerosing cholangitis, T2 = estimated T2 values without fat suppression; \u0026nbsp;wT2 = estimated water-specific T2 values with fat suppression.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887537/v1/d22793e66b61dda5add21ebc.jpg"},{"id":66117427,"identity":"c4e8f8b2-af6b-4cfe-b68b-2939f37a1d7c","added_by":"auto","created_at":"2024-10-08 00:53:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56111,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression plots showing the overestimation of T2 compared to wT2 in absolute values (left) and as a percentage relative to T2 (right) against PDFF for all participants. AH = autoimmune hepatitis, AL = Alagille syndrome, FALD = Fontan associated liver disease, HS = participants with evidence of hepatic steatosis, nonHS = controls and participants with other liver diseases, MASLD = metabolic associated steatotic liver disease, \u0026nbsp;MASH = metabolic dysfunction associated steatohepatitis, PSC = primary sclerosing cholangitis, T2 = estimated T2 values without fat suppression, and wT2 = estimated water-specific T2 values with fat suppression.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4887537/v1/6100b77b5d74f36569c33197.jpg"},{"id":66597428,"identity":"129413a6-0099-45c0-975f-3d2dc302b999","added_by":"auto","created_at":"2024-10-14 16:10:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":688094,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4887537/v1/3143508c-62c0-481d-9ccc-124e58916edf.pdf"}],"financialInterests":"Competing interest reported. JK: No disclosures \nMKM: No disclosures\nJT: No disclosures\nAP: No disclosures \nAT:Consulted for GE Healthcare; has received research support from GE Healthcare, Siemens Healthineers, and Perspectum Inc. No support was received for the current work.\nJD: Received unrelated in-kind research support from Perspectum, Philips Healthcare, GE HealthCare, Motilent, Guerbet, and Bracco Imaging. No support was received for the current work.\nMKM: No disclosures \nASP: No disclosures","formattedTitle":"Influence of Fat on Hepatic T2 Relaxation Time Estimation: A Preliminary Investigation","fulltext":[{"header":"Introduction ","content":"\u003cp\u003eNon-invasive quantitative magnetic resonance imaging (MRI) can help to objectively stage disease and subsequent progression based on pathological alteration in tissue composition and architecture consequent to disease processes and/or responses to therapies and interventions. In light of the clinical impact of quantitative MRI for the myocardium\u0026nbsp;(1, 2), clinical interest in quantitative MRI has expanded to other organs\u0026nbsp;(3). Accordingly, the clinical efficacy of quantitative MRI is increasingly being evaluated for the comprehensive assessment of hepatic parenchyma in diffuse liver diseases. Early detection and management of liver inflammation and fibrosis are crucial to prevent the progression to cirrhosis and reduce the risk of complications such as portal hypertension, ascites, varices, and liver failure\u0026nbsp;(4). Robust estimation of liver stiffness, proton density fat fraction, and iron quantification with MRI has successfully translated from research into clinical practice\u0026nbsp;(5, 6). The potential clinical utility of relaxation time mapping, including T2 mapping, is being explored in several liver diseases, including steatosis, cirrhosis, and fibrosis\u0026nbsp;(3, 7-11). However, relaxation processes are known to be\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003einfluenced by various factors including iron deposition, fat deposition, inflammation, and fibrosis\u0026nbsp;(5, 7, 10), complicating the precise identification of the underlying causes of alterations in relaxation times.\u003c/p\u003e\n\u003cp\u003eIn the setting of chronic liver disease, patients can often have overlapping histologic features including intra- and/or extracellular edema, extracellular collagen deposition, intracellular iron, and intracellular lipid deposition. For example, in patients with metabolic dysfunction-associated steatotic liver disease (MASLD), histologic features include macro-vesicular steatosis, lobular inflammation, and often some degree of fibrosis\u0026nbsp;(12). These processes have varying effects on the estimation of hepatic T2, depending on the technique utilized for obtaining T2 estimates\u0026nbsp;(13). Hepatic T2 estimates have been reported to increase in association with the advancing histologic degree of steatosis and/or fibrosis and coexistent inflammation\u0026nbsp;(14, 15). Therefore, the resultant changes in T2 estimates in these patients are non-specific and can be difficult to interpret. When evaluating for edema as a manifestation of inflammation or congestion, the presence of steatosis and/or fibrosis likely confounds T2 estimates. T2 mapping sequences with and without fat suppression could potentially be beneficial to identify and eliminate the influence of hepatic steatosis as a contributing factor when attempting to quantify changes in tissue water content.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to examine the influence of fat content on the hepatic T2 using a fast multi-echo 2D Gradient and Spin Echo (GRASE) sequence (13, 16). Although the echo planar imaging (EPI) readout used in this GRASE acquisition can result in some T2* weighting, the technique allows T2 mapping with sufficient spatial resolution in reasonable breath-hold times, both without (T2) and with fat suppression (wT2).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis HIPAA-compliant study prospectively acquired liver MR images from research participants under Institutional Review Board (IRB) approval (IRB ID 2022-0108). Written informed consent was obtained from participants or from parents/guardian caretakers where applicable. These same patients underwent additional imaging during the same study visit to evaluate different hepatic quantitative mapping sequences for other studies, including T1 mapping and a novel multi-inversion multi-echo spin and gradient echo (MI-SAGE) sequence in the liver. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBoth asymptomatic individuals without known liver disease (controls) and those with known liver disease were recruited for this study. Controls were recruited through hospital-wide emails. A search of Department of Radiology records was used to identify and recruit patients who had previously undergone diagnostic imaging at our institution for fibrotic, autoimmune, or fatty liver disease. Inclusion criteria for the study included age between 8-80 years, the ability to lie still in the MRI scanner and perform breath-holds of at least 15 seconds, and no contraindications to MRI.\u003c/p\u003e\n\u003cp\u003eThe aim was to recruit approximately one-third of participants with known hepatic steatosis (HS; proton density fat fraction [PDFF] \u0026ge;6% on prior MRI) (17), one-third with fibrotic (\u0026gt; 3kPa) liver from various other chronic liver diseases, and the remaining, asymptomatic individuals without known liver disease (controls). Participants were prospectively reassigned to the hepatic steatosis subgroup, as appropriate, if research liver PDFF was \u0026ge;6% (17).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMR Image Acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003eAll MR imaging was performed on a 1.5-T clinical MR scanner (Ingenia; Philips Healthcare; Best, the Netherlands) using a 28-element torso coil for signal reception. The quantitative imaging sequences included a 3D chemical shift-encoded six-echo radiofrequency spoiled gradient echo MRI (mDIXON Quant) acquisition for quantitative PDFF estimation, and a multi-echo 2D GRASE with and without fat suppression for quantitative wT2 and T2 estimation, each performed within a single 15 second end-expiration breath-hold. For wT2 and T2 2D GRASE acquisitions, a single slice was positioned to encompass the widest part of the mid-liver in the transverse plane. The 3D mDIXON Quant transverse slab was positioned so that the location of the central slices of the volume matched that of the T2 and wT2 2D GRASE slices. For all three acquisitions, the in-plane field of view was 440x440 mm\u003csup\u003e2\u003c/sup\u003e with an acquired in-plane spatial resolution of 3x3 mm\u003csup\u003e2\u003c/sup\u003e and a sensitivity encoding (SENSE) acceleration factor of 2. The T2 mapping GRASE acquisition used 9 spin echoes acquired with an EPI factor of 7 (one startup echo, time of the first spin echo =15 ms, spin echo spacing = 7.4 ms) with a repetition time (TR) of 1000 ms and a slice thickness of 10 mm. The wT2 mapping GRASE acquisition used the same parameters but included an addition of a Spectral Presaturation with Inversion Recovery (SPIR) pulse for fat suppression. The 3D mDIXON Quant acquired 6 gradient echoes (first TE=1.2 ms, echo spacing = 0.8 ms) with a flip angle of 5\u003csup\u003e0\u003c/sup\u003e\u003csub\u003e,\u003c/sub\u003e\u003csup\u003e \u003c/sup\u003ea TR of 6.5 ms and a slice thickness of 6 mm covering 24 cm in the caudocephalad direction. The PDFF, wT2 and T2 maps were computed and produced by FDA approved software on the scanner. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImage Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e \u003c/em\u003eAll liver MRI data, including the scanner-generated PDFF, wT2, and T2 estimate maps, were exported in standard DICOM format to a commercial post-processing workstation (IntelliSpace Portal v10.1; ISP, Philips Healthcare; Best, the Netherlands) for image analysis. Manual measurements of PDFF, wT2, and T2 were made by a blinded clinical pediatric radiology fellow \u003cstrong\u003e\u003cem\u003e\u0026lt;Blinded for review\u0026gt;\u003c/em\u003e\u003c/strong\u003e. A single large freehand ROI was drawn on the liver wT2 and T2 maps encompassing as much of the right hepatic lobe as possible. A circular ROI was placed in the right lobe of the liver at the corresponding slice of the PDFF map. The liver capsule, parenchyma edge, visible blood vessels, bile ducts, and areas of visible artifacts were avoided while drawing ROIs \u003cstrong\u003e(Figure 1\u003c/strong\u003e). The mean and standard deviation of the measured values within each ROI were recorded. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eContinuous data were summarized as means and standard deviations, or medians and interquartile ranges, as appropriate. Linear regression, intra-class correlation coefficients (ICC), and Bland-Altman analysis were employed to assess the consistency and agreement of T2 and wT2 estimates. Pearson\u0026rsquo;s correlation was computed to determine the relationship between differences in hepatic T2 and wT2 estimates as the dependent variable and the participant\u0026rsquo;s estimated PDFF as the independent variable. A p-value \u0026lt;0.05 was considered significant for all inference testing, and 95% confidence intervals were calculated as appropriate. ICC values were interpreted as follows: \u0026lt; 0.5, poor; 0.5\u0026ndash;0.75, moderate; 0.75\u0026ndash;0.9, good; and \u0026gt; 0.90, excellent agreement (18). Correlation coefficients were interpreted as follows: 0\u0026ndash;0.19, very weak; 0.2\u0026ndash;0.39, weak; 0.40\u0026ndash;0.59, moderate; 0.60\u0026ndash;0.79, strong; and 0.80\u0026ndash;1.0, very strong (19). All statistical analyses were performed using MATLAB (The MathWorks\u003csup\u003eTM\u003c/sup\u003e Inc., Natick, Massachusetts, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTwenty-one participants (9 males, 12 females; mean age 18.4\u0026plusmn;4.7 years [range: 9\u0026ndash;27 years]) successfully underwent research liver MR imaging. Baseline patient diagnoses included 7 control participants, 6 participants with MASLD, 2 participants with metabolic dysfunction associated steatohepatitis (MASH), 1 participant with steatosis and autoimmune hepatitis, and an additional 5 participants with other non-steatotic hepatobiliary pathologies (9 steatotic participants and 12 non-steatotic participants in total). Patient characteristics are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. \u003cstrong\u003eFigure 1\u003c/strong\u003e presents the quantitative maps for PDFF, wT2, and T2 for four representative study subjects.\u003c/p\u003e\n\u003cp\u003eParticipant liver PDFF ranged from 2% to 34% (median 4%, IQR 7%).\u0026nbsp;There was a moderate agreement (ICC = 0.57 [95% CI:\u0026nbsp;0.19 - 0.80]) between the T2 and wT2 estimates across all participants with\u0026nbsp;the T2 estimates (57\u0026plusmn;7.3 ms (range: 44.5-70.5 ms)) significantly longer\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e=0.0085) compared to the\u0026nbsp;wT2 estimates (52.5\u0026plusmn;8 ms (range: 43-69.7 ms)) for all participants.\u003c/p\u003e\n\u003cp\u003eIn the participants without hepatic steatosis (controls and participants with other liver diseases), liver PDFF ranged from 2% to 5% (3.4\u0026plusmn;0.8%), whereas in participants with hepatic steatosis, liver PDFF ranged from 7% to 34% (median 12%, IQR 14.8% 16.1\u0026plusmn;9.4 %). The distributions of estimated T2 and wT2 in the controls, participants with hepatic steatosis, and participants with other liver diseases are depicted in \u003cstrong\u003eFigure 2\u003c/strong\u003e. T2 estimates were significantly (\u003cem\u003ep\u003c/em\u003e=0.0063) longer than wT2 estimates in participants with hepatic steatosis, while these values were not significantly different in controls (\u003cem\u003ep\u003c/em\u003e=0.3577) and participants with other liver diseases (\u003cem\u003ep\u003c/em\u003e=0.3954). Therefore, for further analysis, controls and participants with other liver diseases were grouped together as a non-steatotic group.\u003c/p\u003e\n\u003cp\u003eThe linear regression and Bland-Altman plots for T2 and wT2 estimates for participants with and without hepatic steatosis are depicted in \u003cstrong\u003eFigure 3\u003c/strong\u003e. There was excellent agreement (ICC = 0.99\u0026nbsp;[95% CI: 0.96 \u0026ndash; 0.99]) between\u0026nbsp;the T2 and wT2 estimates in the non-steatotic group with\u0026nbsp;the negligible mean bias of 0.45 ms (95% limits of agreement; -1.7 to 2.6 ms).\u0026nbsp;However,\u0026nbsp;there was poor agreement (ICC = 0.06\u0026nbsp;[95% CI: -0.59 \u0026ndash; 0.67]) between estimates in participants with hepatic steatosis. In participants with hepatic steatosis, a non-significant (\u003cem\u003ep\u003c/em\u003e=0.133) proportional bias was observed with relative overestimation of T2 compared to wT2.\u0026nbsp;\u003cstrong\u003eFigure 4\u003c/strong\u003e depicts linear regression plots for T2 and wT2 estimates against PDFF for participants with and without hepatic steatosis. In participants with hepatic steatosis, there was a strong correlation (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.7) of\u0026nbsp;estimated\u0026nbsp;T2 with PDFF with 0.66ms increase in estimated T2 for every 1% rise in PDFF. In the non-steatotic group there was a very weak (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.18) correlation of estimated wT2 with PDFF. \u003cstrong\u003eFigure 5\u003c/strong\u003e illustrates the linear regression plots for overestimation of T2 compared to wT2 against PDFF for all participants. The overestimation of T2 relative to wT2 across all participants was very strongly (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.95) correlated with PDFF, showing an increase of 0.8 ms (1.22%) for every 1% rise in PDFF.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study compared hepatic T2 estimates obtained without fat suppression (T2) to water-specific T2 (wT2) estimates obtained with fat suppression using multi-echo 2D GRASE sequence in a sample of participants exhibiting a range of liver PDFF similar to those observed in routine clinical practice. Across all participants, we found moderate agreement between T2 and wT2 estimates, with the T2 estimates being significantly longer compared to wT2 estimates. In the subgroup of participants without hepatic steatosis, there was excellent agreement between the T2 and wT2 estimates. In contrast, among participants with hepatic steatosis, there was poor agreement between T2 and wT2 estimates, with a strong correlation between liver PDFF and the extent of overestimation of T2 compared to wT2. Specifically, we observed an increase of 0.8 ms for every 1% increase in PDFF across all participants. These results highlight the potential contributing effect of hepatic fat on T2 estimates when characterizing tissue water accumulation as a manifestation of diffuse liver disease. Our findings are concordant with those of Idilman et al. who demonstrated a stepwise increase in T2 estimates as the histologic degree of hepatic steatosis increased (15).\u003c/p\u003e\n\u003cp\u003eAlthough we demonstrated that fat content contributes to elevated T2 estimates, several other tissue characteristics also elevate T2 estimates. This is evident in estimates for several participants without steatosis where prolonged T2 estimates were identified regardless of whether fat suppression was applied or not. For instance, the two participants with FALD who were recruited for this study exhibited PDFF values of 2 to 3%. However, the T2 estimates for both patients were significantly elevated (\u0026gt;65 msec) irrespective of whether fat suppression was applied or not (as shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e). This likely reflects venous and lymphatic congestion consequent to the shunting of the blood from inferior vena cava to the pulmonary circulation after the Fontan procedure, with associated elevation of central venous pressure and eventual development of liver fibrosis (20). Fibrosis was also shown by Guimaraes et al. to be associated with a stepwise increase in T2 estimates as the histologic degree of fibrosis (Ishak classification) increased (14). This suggests that while water-specific hepatic T2 estimates may contribute to the differential diagnosis of diffuse liver diseases, multiple factors likely contribute to T2 prolongation, including inflammation and/or fibrosis.\u003c/p\u003e\n\u003cp\u003eWater, fat, and iron content may change simultaneously during the progression of liver disease. An increase in fat fraction or intra- and extracellular water can concurrently prolong hepatic T2, while an increase in iron content can lead to T2 underestimation. Therefore, minimizing, correcting, and interpreting these individual parameters in conjunction with each other should enhance the differential diagnosis of liver diseases and improve treatment monitoring over time. When evaluating estimated T2 values, it is advisable to obtain both a T2 and a water-sensitive T2 map to assess the contributing effect of fat content. This approach can potentially help differentiate the alteration in water and fat content due to various diffuse disease processes. Overall, estimated hepatic T2 values should be interpreted in conjunction with estimates of other individual parameters, including PDFF, T1, and T2*.\u003c/p\u003e\n\u003cp\u003eThe findings of this study emphasize the importance of considering the combined influence of fat and water content on hepatic T2 alterations. We utilized the GRASE acquisition technique because it enables T2 mapping with adequate spatial resolution, reasonable breath-hold times, and ability to suppress fat, thereby allowing for comparison between T2 and wT2. Although the GRASE technique is sensitive to relatively short T1 and T2* values due to the EPI readout (13), it provides valuable insights into the contributions of water and fat components to the T2 estimation. Apart from the SPIR fat suppression applied for wT2 estimation, identical methods\u0026mdash;including the pulse sequence, imaging parameters, and fitting to exponential decay model\u0026mdash;were used for both T2 and wT2 estimation. This ensures that other confounding factors, including T1 and T2* effects, remain consistent for both estimates, making the observed differences in T2 and wT2 estimates primarily attributable to fat content. However, other effects such as magnetization transfer of the SPIR pulse and variations in fat suppression across the field of view may still be present and result in small contributions to the differences in T2 and wT2 estimates. The influence of fat content on hepatic T2 estimation will likely vary across different T2 mapping approaches, depending on the acquisition technique (e.g., spin echo, fast spin echo, GRASE, T2-prepared; MR fingerprinting), the sequence parameters (e.g., number of echoes, time between echoes, T2-prep durations), and the algorithms used (e.g., fitting exponential decay model, dictionary matching).\u003c/p\u003e\n\u003cp\u003eThere are a few limitations to this study, including its single-center, single-field strength, single-scanner, single-vendor design, and the evaluation of only 21 research participants. Additionally, although there was a representative, balanced mixture of individuals with healthy, steatotic, and other diseased livers, there was no histological validation in these participants to determine the underlying factors driving alterations in estimated T2 values. Lastly, the influence of fat on T2 estimation was evaluated only using the GRASE technique. The influence of fat on T2 estimates using other T2 mapping approaches will likely differ and should be similarly evaluated. Nonetheless, the study findings provide sufficient evidence to warrant further prospective clinical evaluations of the influence of fat on hepatic T2 estimation in larger samples of patients that encompass a wide range and variety of diseases. \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLiver fat content proportionally increases estimated T2 potentially confounding quantification of changes in T2 that can be attributed to alteration in tissue water content.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRao S, Tseng SY, Pednekar A, Siddiqui S, Kocaoglu M, Fares M, Lang SM, Kutty S, Christopher AB, Olivieri LJ, Taylor MD, Alsaied T. Myocardial Parametric Mapping by Cardiac Magnetic Resonance Imaging in Pediatric Cardiology and Congenital Heart Disease. Circ Cardiovasc Imaging 2022;15(1):e012242. doi: 10.1161/circimaging.120.012242\u003c/li\u003e\n\u003cli\u003eAgarwal A, Khandheria BK, Paterick TE, Treiber SC, Bush M, Tajik AJ. Left ventricular noncompaction in patients with bicuspid aortic valve. J Am Soc Echocardiogr 2013;26(11):1306-1313. doi: 10.1016/j.echo.2013.08.003\u003c/li\u003e\n\u003cli\u003eDekkers IA, Lamb HJ. Clinical application and technical considerations of T(1) \u0026amp; T(2)(*) mapping in cardiac, liver, and renal imaging. Br J Radiol 2018;91(1092):20170825. doi: 10.1259/bjr.20170825\u003c/li\u003e\n\u003cli\u003ePerez IC, Bolte FJ, Bigelow W, Dickson Z, Shah NL. Step by Step: Managing the Complications of Cirrhosis. Hepat Med 2021;13:45-57. doi: 10.2147/hmer.S278032\u003c/li\u003e\n\u003cli\u003eSchaapman JJ, Tushuizen ME, Coenraad MJ, Lamb HJ. Multiparametric MRI in Patients With Nonalcoholic Fatty Liver Disease. Journal of Magnetic Resonance Imaging 2021;53(6):1623-1631. doi: https://doi.org/10.1002/jmri.27292\u003c/li\u003e\n\u003cli\u003eZerunian M, Pucciarelli F, Masci B, Siciliano F, Polici M, Bracci B, Guido G, Polidori T, De Santis D, Laghi A, Caruso D. Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. Biomed Res Int 2022;2022:1147111. doi: 10.1155/2022/1147111\u003c/li\u003e\n\u003cli\u003eAhn JH, Yu JS, Park KS, Kang SH, Huh JH, Chang JS, Lee JH, Kim MY, Nickel MD, Kannengiesser S, Kim JY, Koh SB. Effect of hepatic steatosis on native T1 mapping of 3T magnetic resonance imaging in the assessment of T1 values for patients with non-alcoholic fatty liver disease. Magn Reson Imaging 2021;80:1-8. doi: 10.1016/j.mri.2021.03.015\u003c/li\u003e\n\u003cli\u003eBreit HC, Block KT, Winkel DJ, Gehweiler JE, Henkel MJ, Weikert T, Stieltjes B, Boll DT, Heye TJ. Evaluation of liver fibrosis and cirrhosis on the basis of quantitative T1 mapping: Are acute inflammation, age and liver volume confounding factors? Eur J Radiol 2021;141:109789. doi: https://doi.org/10.1016/j.ejrad.2021.109789\u003c/li\u003e\n\u003cli\u003eHoffman DH, Ayoola A, Nickel D, Han F, Chandarana H, Shanbhogue KP. T1 mapping, T2 mapping and MR elastography of the liver for detection and staging of liver fibrosis. Abdominal Radiology 2020;45(3):692-700. doi: 10.1007/s00261-019-02382-9\u003c/li\u003e\n\u003cli\u003eErden A, Kuru \u0026Ouml;z D, Peker E, Kul M, \u0026Ouml;zalp Ateş FS, Erden İ, İdilman R. MRI quantification techniques in fatty liver: the diagnostic performance of hepatic T1, T2, and stiffness measurements in relation to the proton density fat fraction. Diagn Interv Radiol 2021;27(1):7-14. doi: 10.5152/dir.2020.19654\u003c/li\u003e\n\u003cli\u003eMesropyan N, Kupczyk P, Kukuk GM, Dold L, Weismueller T, Endler C, Isaak A, Faron A, Sprinkart AM, Pieper CC, Kuetting D, Strassburg CP, Attenberger UI, Luetkens JA. Diagnostic value of magnetic resonance parametric mapping for non-invasive assessment of liver fibrosis in patients with primary sclerosing cholangitis. BMC Medical Imaging 2021;21(1):65. doi: 10.1186/s12880-021-00598-0\u003c/li\u003e\n\u003cli\u003eBrown GT, Kleiner DE. Histopathology of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Metabolism 2016;65(8):1080-1086. doi: 10.1016/j.metabol.2015.11.008\u003c/li\u003e\n\u003cli\u003eBae\u0026szlig;ler B, Schaarschmidt F, Stehning C, Schnackenburg B, Maintz D, Bunck AC. A systematic evaluation of three different cardiac T2-mapping sequences at 1.5 and 3T in healthy volunteers. Eur J Radiol 2015;84(11):2161-2170. doi: https://doi.org/10.1016/j.ejrad.2015.08.002\u003c/li\u003e\n\u003cli\u003eGuimaraes AR, Siqueira L, Uppal R, Alford J, Fuchs BC, Yamada S, Tanabe K, Chung RT, Lauwers G, Chew ML, Boland GW, Sahani DV, Vangel M, Hahn PF, Caravan P. T2 relaxation time is related to liver fibrosis severity. Quant Imaging Med Surg 2016;6(2):103-114. doi: 10.21037/qims.2016.03.02\u003c/li\u003e\n\u003cli\u003eIdilman IS, Celik A, Savas B, Idilman R, Karcaaltincaba M. The feasibility of T2 mapping in the assessment of hepatic steatosis, inflammation, and fibrosis in patients with non-alcoholic fatty liver disease: a preliminary study. Clinical radiology 2021;76(9):709.e713-709.e718. doi: 10.1016/j.crad.2021.06.014\u003c/li\u003e\n\u003cli\u003eSprinkart AM, Luetkens JA, Tr\u0026auml;ber F, Doerner J, Gieseke J, Schnackenburg B, Schmitz G, Thomas D, Homsi R, Block W, Schild H, Naehle CP. Gradient Spin Echo (GraSE) imaging for fast myocardial T2 mapping. Journal of Cardiovascular Magnetic Resonance 2015;17(1):12. doi: 10.1186/s12968-015-0127-z\u003c/li\u003e\n\u003cli\u003eShin HJ, Kim HG, Kim MJ, Koh H, Kim HY, Roh YH, Lee MJ. Normal range of hepatic fat fraction on dual- and triple-echo fat quantification MR in children. PloS one 2015;10(2):e0117480. doi: 10.1371/journal.pone.0117480\u003c/li\u003e\n\u003cli\u003eKoo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 2016;15(2):155-163. doi: 10.1016/j.jcm.2016.02.012\u003c/li\u003e\n\u003cli\u003eWuensch K, Evans J. Straightforward Statistics for the Behavioral Sciences. . Journal of the American Statistical Association 1996;91(436). doi: https://doi:10.2307/2291607\u003c/li\u003e\n\u003cli\u003eDillman JR, Trout AT, Alsaied T, Gupta A, Lubert AM. Imaging of Fontan-associated liver disease. Pediatr Radiol 2020;50(11):1528-1541. doi: 10.1007/s00247-020-04776-0\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\" style=\"width: 82.2005%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: Characteristics of the study population\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e18.4\u0026plusmn;4.7 (9-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003eFemale-to-male ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e9:12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026shy;Height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e165.9\u0026plusmn;10.5 (142-188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e81.7\u0026plusmn;35.0 (38-165)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003eBSA (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e1.91\u0026plusmn;0.44 (1.29-2.86)\u003c/p\u003e\n \u003cp\u003e29.1\u0026plusmn;10.2(16.2-51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003eClinical Indications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Asymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;MASLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;MASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Primary sclerosing cholangitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Fontan-associate liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.375601926163725%\" valign=\"top\" style=\"width: 40.5743%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Autoimmune hepatitis\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Alagille syndrome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.89566613162119%\" valign=\"top\" style=\"width: 52.4084%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data are presented as the mean \u0026plusmn; standard deviation (minimum \u0026ndash; maximum) or as the number of subjects. BSA = body surface area, BMI = body mass index, MASLD = metabolic associated steatotic liver disease, MASH = metabolic dysfunction associated steatohepatitis.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Liver, T2 mapping, T2 relaxation, proton density fat fraction","lastPublishedDoi":"10.21203/rs.3.rs-4887537/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4887537/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003ePURPOSE:\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the influence of fat content on hepatic T2 estimation through T2 mapping without and with fat suppression.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eMATERIALS AND METHODS:\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective IRB-approved study included participants without known liver disease (controls), with hepatic steatosis (proton density fat fraction [PDFF] \u0026gt;6%), or with fibrotic (\u0026gt; 3kPa) liver from other chronic liver diseases. Imaging at 1.5T included a quantitative 3D chemical shift encoded six-echo radiofrequency spoiled gradient echo MRI sequence (mDIXON Quant) for PDFF estimation, nine-echo 2D gradient and spin echo (GRASE) for T2 estimation, and nine-echo 2D GRASE with fat suppression for water-specific T2 (wT2) estimation.\u003c/p\u003e\n\u003cp\u003eA single blinded observer traced a large freehand region-of-interest (ROI) in the right hepatic lobe on the T2 and wT2 maps, and a circular ROI on the PDFF map. Consistency and agreement of T2 and wT2 estimates were assessed using linear regression, intra-class correlation coefficients (ICC), and Bland-Altman analysis. Pearson’s correlation evaluated the relationship between differences in hepatic T2 and wT2 estimates and PDFF.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eRESULTS:\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eA total of 21 participants (7 controls, 9 with hepatic steatosis, 5 with other liver diseases) with a mean age of 18.4±4.7 years (range: 9-27 years; 9 males) were included. Estimated liver PDFF ranged from 2% to 34% (median 4%, IQR 7.5%). T2 estimates (57±7.3 ms [range: 44.5-70.5 ms]) were significantly longer (\u003cem\u003ep\u003c/em\u003e=0.0085) compared to wT2 estimates (52.5±8 ms [range: 43-69.7 ms]) with moderate agreement (ICC = 0.57 [95% CI: 0.19 - 0.80]).\u003c/p\u003e\n\u003cp\u003eFor participants with hepatic steatosis, T2 estimates were significantly longer than wT2 estimates (\u003cem\u003ep\u003c/em\u003e=0.0063) with poor agreement (ICC=0.06 [95% CI: -0.59 – 0.67]). Participants without hepatic steatosis showed comparable T2 and wT2 estimates (\u003cem\u003ep\u003c/em\u003e=0.3577, \u003cem\u003ep\u003c/em\u003e=0.3954) with excellent agreement (ICC = 0.99 [95% CI: 0.96 - 0.99]). The relative bias of T2 to wT2 was very strongly correlated with PDFF (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 0.95), increasing by 0.8 ms (1.22%) for every 1% rise in PDFF.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e\u003cstrong\u003eCONCLUSION:\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eLiver fat content proportionally increases estimated T2, potentially confounding the quantification of changes in T2 that can be attributed to alteration in tissue water content.\u003c/p\u003e","manuscriptTitle":"Influence of Fat on Hepatic T2 Relaxation Time Estimation: A Preliminary Investigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 00:53:31","doi":"10.21203/rs.3.rs-4887537/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-29T13:22:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-28T20:15:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-20T10:18:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22421579224416513334335685037735947067","date":"2024-08-12T06:05:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112793717936797428070552634616236204761","date":"2024-08-11T00:05:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-10T04:18:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-09T14:47:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-09T14:46:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2024-08-09T13:45:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a1a6dc2b-0da4-44da-b567-7b72cba8561a","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:06:39+00:00","versionOfRecord":{"articleIdentity":"rs-4887537","link":"https://doi.org/10.1007/s00261-024-04623-y","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2024-10-12 15:58:09","publishedOnDateReadable":"October 12th, 2024"},"versionCreatedAt":"2024-10-08 00:53:31","video":"","vorDoi":"10.1007/s00261-024-04623-y","vorDoiUrl":"https://doi.org/10.1007/s00261-024-04623-y","workflowStages":[]},"version":"v1","identity":"rs-4887537","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4887537","identity":"rs-4887537","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}