Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images.

OBJECTIVE: The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net model. MATERIALS AND METHODS: Thirty-four rats fed different diets or housed at different temperatures underwent successive MR scans before and after NE injection. The iBAT were labelled automatically by identifying the regions with obvious FF change in response to the NE stimulation. Further, these FF images along with the recognized iBAT mask images were used to develop a deep learning network to accomplish the robust segmentation of iBAT in various rat models, even without NE stimulation. The trained model was then validated in rats fed with high-fat diet (HFD) in comparison with normal diet (ND). RESULT: A total of 6510 FF images were collected using a clinical 3.0 T MR scanner. The dice similarity coefficient (DSC) between the automatic and manual labelled results was 0.895 ± 0.022. For the network training, the DSC, precision rate, and recall rate were found to be 0.897 ± 0.061, 0.901 ± 0.068 and 0.899 ± 0.086, respectively. The volumes and FF values of iBAT in HFD rats were higher than ND rats, while the FF decrease was larger in ND rats after NE injection. CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.

A 5-channel local B0 shimming coil combined with a 3-channel RF receiver coil for rat brain imaging at 3 T.

PURPOSE: We aimed to improve B0 magnetic field homogeneity and minimize the interference between RF coils and local B0 shimming coils with few channel numbers. METHODS: To design and construct the prototype for B0 shimming of the rat brain, we first evaluated the interferences of single shimming loops on RF receiver loops. Then, B0 shimming of the whole rat brain was implemented using an optimization procedure. The positions and currents of the local shimming coils with channel numbers from 3 to 6 were optimized to improve shimming performance. Based on the simulation results, a 5-channel local shimming coil, combined with a 3-channel RF receiver coil, was constructed and evaluated by animal experiments. RESULTS: There was marginal SNR loss within 5% after integrating the local shimming coil into the RF receiver coil. With respect to the Siemens standard shims up to second order, the B0 inhomogeneity in one whole rat brain was reduced from 39.6 Hz to 24.7 Hz by using the local shimming coil. A large portion of the EPI distortions was recovered after using the 5-channel local shimming coil. The temporal SNR using the local shimming coil was higher than that using the Siemens standard shims up to second order, with an improvement of more than 24%. CONCLUSIONS: The local shimming coil can improve B0 magnetic field homogeneity despite minor effects on the RF coil and can benefit a variety of applications that are sensitive to B0 inhomogeneity. Nevertheless, EPI for rat brain is still very challenging.

Reducing the ambiguity of field inhomogeneity and chemical shift effect for fat-water separation by field factor.

PURPOSE: To reduce the ambiguity between chemical shift and field inhomogeneity with flexible TE combinations by introducing a variable (field factor). THEORY AND METHODS: The ambiguity between chemical shift and field inhomogeneity can be eliminated directly from the multiple in-phase images acquired at different TEs; however, it is only applicable to few echo combinations. In this study, we accommodated such an implementation in flexible TE combinations by introducing a new variable (field factor). The effects of the chemical shift were removed from the field inhomogeneity in the candidate solutions, thus reducing the ambiguity problem. To validate this concept, multi-echo MRI data acquired from various anatomies with different imaging parameters were tested. The derived fat and water images were compared with those of the state-of-the-art fat-water separation algorithms. RESULTS: Robust fat-water separation was achieved with the accurate solution of field inhomogeneity, and no apparent fat-water swap was observed. In addition to the good performance, the proposed method is applicable to various fat-water separation applications, including different sequence types and flexible TE choices. CONCLUSION: We propose an algorithm to reduce the ambiguity of chemical shift and field inhomogeneity and achieved robust fat-water separation in various applications.

Fat distribution measurements by chemical shift-encoded transition region extraction predict the risk of hyperglycaemia, dyslipidaemia and metabolic syndrome in mice.

Metabolically healthy or unhealthy obesity is closely related to metabolic syndrome (MetS). To validate a more accurate diagnostic method for obesity that reflects the risk of metabolic disorders in a pre-clinical mouse model, C57BL/6J mice were fed high-sucrose-high-fat and chow diets for 12 weeks to induce obesity. MRI was performed and analysed by chemical shift-encoded fat-water separation based on the transition region extraction method. Abdominal fat was divided into upper and lower abdominal regions at the horizontal lower border of the liver. Blood samples were collected, and the glucose level, lipid profile, liver function, HbA1c and insulin were tested. k-means clustering and stepwise logistic regression were applied to validate the diagnosis of hyperglycaemia, dyslipidaemia and MetS, and to ascertain the predictive effect of MRI-derived parameters to the metabolic disorders. Pearson or Spearman correlation was used to assess the relationship between MRI-derived parameters and metabolic traits. The receiver-operating characteristic curve was used to evaluate the diagnostic effect of each logistic regression model. A two-sided p value less than 0.05 was considered to indicate statistical significance for all tests. We made the precise diagnosis of obesity, dyslipidaemia, hyperglycaemia and MetS in mice. In all, 14 mice could be diagnosed as having MetS, and the levels of body weight, HbA1c, triglyceride, total cholesterol and low-density lipoprotein cholesterol were significantly higher than in the normal group. Upper abdominal fat better predicted dyslipidaemia (odds ratio, OR = 2.673; area under the receiver-operating characteristic curve, AUCROC = 0.9153) and hyperglycaemia (OR = 2.456; AUCROC = 0.9454), and the abdominal visceral adipose tissue (VAT) was better for predicting MetS risk (OR = 1.187; AUCROC = 0.9619). We identified the predictive effect of fat volume and distribution in dyslipidaemia, hyperglycaemia and MetS. The upper abdominal fat played a better predictive role for the risk of dyslipidaemia and hyperglycaemia, and the abdominal VAT played a better predictive role for the risk of MetS.

Fatty Acids Composition of the Sacroiliac Joint in Axial Spondyloarthritis: Analysis Using 3.0 T Chemical Shift-Encoded MRI.

BACKGROUND: Axial spondyloarthritis (axSpA) is a group of inflammatory diseases that may lead to ankylosis of the sacroiliac joint and spine. Fat lesion in the sacroiliac joint is an important feature in diagnosis and disease progression of axSpA. However, whether there is alteration of fatty acids (FAs) composition has not been investigated using MRI. PURPOSE: To investigate bone marrow FA composition of the sacroiliac joint in patients with axSpA compared to controls. STUDY TYPE: Prospective. SUBJECTS: Eighty five participants (mean age, 32.3 ± 6.1 years): 48 axSpA (25 male, 23 female) and 37 non-SpA controls (18 male, 19 female). FIELD STRENGTH/SEQUENCE: 3.0 T/Two multiple gradient-echo chemical shift-encoded (CSE) MRI which differed only in echo times (TEs) were scanned consecutively. ASSESSMENT: Axial multi-echo CSE MRI was performed in the sacroiliac joints in vivo. Regions of interest (ROIs) were manually placed on subchondral bone with and without fat lesion in axSpA patients, and on subchondral bone without fat lesion in controls. FA composition was computed within the ROIs using a nonlinear least square method from literature. STATISTICAL TESTS: Intergroup comparisons were performed using t tests. RESULTS: In axSpA, male patients had significantly higher monounsaturated FA compared to controls in areas with fat lesion in the sacrum (+12%) and in the ilium (+9%), and in areas without fat lesion in the sacrum (+10%). Significantly lower polyunsaturated FAs were found in areas with fat lesion in the sacrum (-10%) and ilium (-11%), and lower saturated FAs were found in areas without fat lesion in the sacrum (-6%). In female, patients with axSpA had significantly higher saturated FAs in areas with fat lesion in the ilium (+7%) in comparison to controls. DATA CONCLUSION: FA composition of the sacroiliac joint alters in patients with axSpA, and it can be detected using CSE MRI based analysis.

Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging.

OBJECTIVE: To achieve efficient segmentation of human supraclavicular adipose tissue (sclavAT) using high-resolution T2-weighted magnetic resonance images. METHODS: High-resolution 1.0 mm isotropic 3D T2-weighted images covering human supraclavicular area were acquired in transverse or coronary plane from 29 volunteers using a 3.0 T MRI scanner. There were typically 144/288 slices for the transverse/coronary scans for each subject, which amounts to a total of 6816 images in 29 volunteers. A U-NET network was trained to segment the supraclavicular adipose tissue (sclavAT). The performance of the automatic segmentation method was evaluated by comparing the output results with the manual labels using the quantitative indices of dice similarity coefficient (DSC), precision rate (PR), and recall rate (RR). The auto-segmented images were used to calculate the sclavAT volumes and registered to the MR fat fraction (FF) images to quantify the fat component of the sclavAT area. The relationship between body mass index (BMI), the volume and FF of sclavAT area was evaluated for all subjects. RESULTS: The DSC, PR and RR of the automatic sclavAT segmentation method on the testing datasets were 0.920 ± 0.048, 0.915 ± 0.070 and 0.930 ± 0.058. The volume and the mean FF of sclavAT were both found to be strongly correlated to BMI, with the correlation coefficient of 0.703 and 0.625 (p < 0.05), respectively. The averaged computation time of the automatic segmentation method was approximately 0.06 s per slice, compared to more than 5 min for manual labeling. CONCLUSION: The present study demonstrates that the proposed automatic segmentation method using U-Net network is able to identify human sclavAT efficiently and accurately.

Fast multi-parametric imaging in abdomen by B 1 + corrected dual-flip angle sequence with interleaved echo acquisition.

PURPOSE: To achieve simultaneous T1, w /proton density fat fraction (PDFF)/ R 2 ∗ mapping in abdomen within a single breadth-hold, and validate the accuracy using state-of-art measurement. THEORY AND METHODS: An optimized multiple echo gradient echo (GRE) sequence with dual flip-angle acquisition was used to realize simultaneous water T1 (T1, w )/PDFF/ R 2 ∗ quantification. A new method, referred to as "solving the fat-water ambiguity based on their T1 difference" (SORT), was proposed to address the fat-water separation problem. This method was based on the finding that compared to the true solution, the wrong (or aliased) solution to fat-water separation problem showed extra dependency on the applied flip angle due to the T1 difference between fat and water. The B 1 + measurement sequence was applied to correct the B 1 + inhomogeneity for T1, w relaxometry. The 2D parallel imaging was incorporated to enable the acquisition within a single breath-hold in abdomen. RESULTS: The multi-parametric quantification results of the proposed method were consistent with the results of reference methods in phantom experiments (PDFF quantification: R2  = 0.993, mean error 0.73%; T1, w quantification: R2  = 0.999, mean error 4.3%; R 2 ∗ quantification: R2  = 0.949, mean error 4.07 s-1 ). For volunteer studies, robust fat-water separation was achieved without evident fat-water swaps. Based on the accurate fat-water separation, simultaneous T1, w /PDFF/ R 2 ∗ quantification was realized for whole liver within a single breath-hold. CONCLUSION: The proposed method accurately quantified T1, w /PDFF/ R 2 ∗ for the whole liver within a single breath-hold. This technique serves as a quantitative tool for disease management in patients with hepatic steatosis.

Water Specific MRI T1 Mapping for Evaluating Liver Inflammation Activity Grades in Rats With Methionine-Choline-Deficient Diet-Induced Nonalcoholic Fatty Liver Disease.

BACKGROUND: Early detection and grading of liver inflammation are important for the management of nonalcoholic fatty liver disease (NAFLD) patients. There is still lack of a noninvasive way for the inflammation characterization in NAFLD. PURPOSE: To assess liver inflammation grades by water specific T1 (wT1) in a rat model. STUDY TYPE: Prospective. ANIMAL MODEL: A total of 65 male rats with methionine-choline-deficient diet-induced NAFLD and 15 male normal rats as control. FIELD STRENGTH/SEQUENCE: A 3 T; multiecho variable flip angle gradient echo sequence. ASSESSMENT: The wT1 and proton density fat fraction were quantified. Inflammation and fibrosis were assessed histologically with H&E and Sirius red stained slices according to the nonalcoholic steatohepatitis scoring system. Inflammation grade was scored with G0/G1/G2/G3 as none/mild/moderate/severe inflammation in NALFD rats. G0 + G1 and G2 + G3 were combined as none-to-mild grade (GL) and moderate-to-severe grade (GH) inflammation groups. STATISTICAL TESTS: Analysis of variance (ANOVA), Mann-Whitney U test, Spearman's correlation, and receiver operating characteristic (ROC) analysis were performed. The areas under ROC (AUROC) was used for the diagnostic performance of wT1 in discriminating GH and GL. A P value < 0.01 was considered statistically significant. RESULTS: Seventy-six rats were included in the analysis. The numbers in G0-G3 groups were 5, 16, 13, and 27. wT1 of G0-G3 was 568.55 ± 63.93 msec, 582.53 ± 62.98 msec, 521.21 ± 67.31 msec, and 508.79 ± 60.53 msec. A moderate but significant negative correlation between wT1 and histopathological inflammation grades was observed (rs  = -0.42). The wT1 of GH (512.80 ± 62.22 msec) was significantly lower than GL (579.20 ± 61.89 msec). The AUROC of wT1 was 0.79, and the optimal cut-off of wT1 was 562.64 msec (sensitivity: 90%, specificity: 76%), for the discrimination of GL and GH. DATA CONCLUSIONS: wT1 could differentiate none-to-mild inflammation from moderate-to-severe inflammation in the early stage of the NAFLD rat model. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Automatic segmentation of whole-body adipose tissue from magnetic resonance fat fraction images based on machine learning.

OBJECTIVE: To propose a fully automated algorithm, which is implemented to segment subcutaneous adipose tissue (SAT) and internal adipose tissue (IAT) from the total adipose tissue for whole-body fat distribution analysis using proton density fat fraction (PDFF) magnetic resonance images. MATERIALS AND METHODS: Adipose tissue segmentation was implemented using the U-Net deep neural network model. All datasets were collected using a 3.0 T magnetic resonance imaging (MRI) scanner for whole-body scan of 20 volunteers covering from neck to knee with about 160 images for each volunteer. PDFF images were reconstructed based on chemical-shift-encoded fat-water imaging. After selecting the representative PDFF images (total 906 images), the manual labeling of the SAT area was used for model training (504 images), validation (168 images), and testing (234 images). RESULTS: The automatic segmentation model was validated through three indices using the validation and test sets. The dice similarity coefficient, precision rate, and recall rate were 0.976 ± 0.048, 0.978 ± 0.048, and 0.978 ± 0.050, respectively, in both validation and test sets. CONCLUSION: The proposed algorithm can reliably and automatically segment SAT and IAT from whole-body MRI PDFF images. The proposed method provides a simple and automatic tool for whole-body fat distribution analysis to explore the relationship between fat deposition and metabolic-related chronic diseases.

The PHU-NET: A robust phase unwrapping method for MRI based on deep learning.

PURPOSE: This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods. METHODS: A deep learning network called PHU-NET was designed for MR phase unwrapping. In this network, a novel training data generation method was proposed to simulate the wrapping patterns in MR phase images. The wrapping boundary and wrapping counts were explicitly estimated and used for network training. The proposed method was quantitatively evaluated and compared to other methods using a number of simulated datasets with varying signal-to-noise ratio (SNR) and MR phase images from various parts of the human body. RESULTS: The results showed that our method performed better in the simulated data even under an extremely low SNR. The proposed method had less residual wrapping in the images from various parts of human body and worked well in the presence of severe anatomical discontinuity. Our method was also advantageous in terms of computational efficiency compared to the traditional methods. CONCLUSION: This work proposed a robust and computationally efficient MR phase unwrapping method based on a deep learning network, which has promising performance in applications using MR phase information.

Newly identified peptide hormone inhibits intestinal fat absorption and improves NAFLD through its receptor GPRC6A.

BACKGROUND & AIMS: Circulating peptides and G protein-coupled receptors (GPCRs) have gained much attention because of their biofunctions in metabolic disorders including obesity and non-alcoholic fatty liver disease (NAFLD). Herein, we aimed to characterize the role and therapeutic potential of a newly identified peptide hormone in NAFLD. METHODS: Using bioinformatics, we identified a murine circulating pentadecapeptide flanked by potential convertase cleavage sites of osteocalcin (OCN), which we named 'metabolitin (MTL)'. We used ligand-receptor binding, receptor internalization, bioluminescence resonance energy transfer and Nano isothermal titration calorimetry assays to study the binding relationship between MTL and GPRC6A. For in vivo biological studies, wild-type mice kept on a high-fat diet (HFD) were injected or gavaged with MTL to study its function in NAFLD. RESULTS: We confirmed that MTL binds to GPRC6A and OCN interacts with GPRC6A using in vitro biological studies. Both intraperitoneal and oral administration of MTL greatly improved NAFLD and insulin resistance in a mouse model. Interacting with GPRC6A expressed in intestines, MTL can significantly inhibit intestinal neurotensin secretion, which in turn inhibits triglyceride but not cholesterol gut absorption, mediated by the 5'AMP-activated protein kinase pathway. In addition, glucagon like peptide-1 secretion was induced by MTL treatment. CONCLUSIONS: Oral or intraperitoneal MTL significantly improves the symptoms of NAFLD by inhibiting lipid absorption and insulin resistance. MTL could be a potential therapeutic candidate for the treatment of NAFLD. LAY SUMMARY: A novel murine peptide hormone, herein named 'metabolitin', inhibits fatty acid absorption and improves systemic insulin resistance in a murine model of obesity and non-alcoholic fatty liver disease. Thus, metabolitin has therapeutic potential for the treatment of patients with non-alcoholic fatty liver disease.

Dual-step iterative temperature estimation method for accurate and precise fat-referenced PRFS temperature imaging.

PURPOSE: The aim of this study was to propose dual-step iterative temperature estimation (DITE) of a fat-referenced proton resonance frequency shift (PRFS) method to improve both the accuracy and precision of temperature estimations in fat-containing tissues. METHODS: A fat-water signal model with multiple fat peaks was used to simultaneously estimate the temperature, fat/water intensity and T 2 ∗ , and field offset. In DITE, model fitting was implemented with alternating 2-step minimizations. The estimated temperature map was smoothed between the 2-step minimizations, which is considered to be the most important step for improving the temperature precision. The performance of DITE was evaluated with a Monte Carlo simulation, fat/water phantoms, and ex vivo brown adipose tissue experiments and then compared with the performance of previous fat-referenced proton resonance frequency shift methods. RESULTS: In fat/water phantom experiment with a smooth temperature profile, the temperatures estimated by DITE are consistent with the thermometer results and present a better accuracy and precision than those of previous fat-referenced proton resonance frequency shift methods. In the brown adipose tissue heating experiment, the average mean error, SD, and RMS error were -0.08ºC, 0.46ºC, and 0.56ºC, respectively, over all of the measurements within the region of interest. CONCLUSION: Our proposed DITE method improves both the accuracy and precision of temperature measurements in tissues with fat fractions between 20% and 80% under smooth distribution of the temperature profile and represents a simple fat-referenced thermometry method.

Fat-water separation based on Transition REgion Extraction (TREE).

PURPOSE: To develop a method based on fat-water transition region extraction (TREE) for robust fat-water separation and quantification in challenging scenarios, including low signal-to-noise ratio (SNR), fast changing B0 field, and disjointed anatomies. THEORY AND METHODS: In TREE method, the phasor solutions of each pixel were categorized into fat-dominant and water-dominant groups. The fat-water transition region was then extracted by detecting sudden changes in the phasor maps. The phasor solutions of the pixels in the transition region were solved by choosing the smoothest phasor combinations. For the remaining subregions, the phasor solution was then determined by all the surrounding transition region pixels. The proposed method was validated using various datasets, including some from the International Society for Magnetic Resonance in Medicine (ISMRM) 2012 Challenge. RESULTS: Quantitative score of proposed method (9936.8 of 10,000) is comparable to the winner (9951.9) of ISMRM 2012 Challenge. The total processing time was 179.3 s for 15 datasets. Sagittal spine data with ~400 mm field of view in head-foot direction were used to compare TREE with several representative region-growing methods. Results showed that the proposed method was robust under fast changing B0 field, disjointed anatomies and low SNR area. No apparent fat-water swap was observed in the low SNR (SNR ~ 10) dataset. Accurate proton density fat fraction results were also produced from the proposed method. CONCLUSION: A method based on fat-water transition region extraction was proposed for robust water-fat separation and fat fraction quantification. The method worked well in spatially disjointed objects, fast changing B0 field, and low SNR application.

Fast MR thermometry using an echo-shifted sequence with simultaneous multi-slice imaging.

PURPOSE: Real-time monitoring is important for the safety and effectiveness of high-intensity focused ultrasound (HIFU) therapy. Magnetic resonance imaging is the preferred imaging modality for HIFU monitoring, with its unique capability of temperature imaging. For real-time temperature imaging, higher temporal resolution and larger spatial coverage are needed. In this study, a sequence based on the echo-shifted RF-spoiled gradient echo (GRE) with simultaneous multi-slice (SMS) imaging was designed for fast temperature imaging. METHODS: A phantom experiment was conducted to evaluate the accuracy of the echo-shifted sequence using a fluorescent fiber thermometer as reference. The temperature uncertainty of the echo-shifted sequence was compared with the traditional GRE sequence at room temperature through the ex vivo porcine muscle. Finally, the ex vivo porcine liver tissue experiment using HIFU heating was performed to demonstrate that the spatial coverage was increased without decreasing temporal resolution. RESULTS: The echo-shifted sequence had a better temperature uncertainty performance compared with the traditional GRE sequence with the same temporal resolution. The ex vivo heating experiment confirmed that by combining the SMS technique and echo-shifted sequence, the spatial coverage was increased without decreasing the temporal resolution while maintaining high temperature measurement precision. CONCLUSION: The proposed technique was validated as an effective real-time method for monitoring HIFU therapy.

Fat-water separation using a region-growing algorithm with self-feeding phasor estimation.

PURPOSE: To develop a novel region-growing algorithm with self-feeding phasor estimation for robust fat-water separation. THEORY AND METHODS: The proposed seed pixel identification and region-growing methods were performed independently at different resolutions. Multiple phasor maps were obtained at lower resolutions and then merged into a new seed map, which was used to generate the final phasor map at the finest resolution. The final fat and water images were reconstructed based on this phasor map. The proposed method was compared with traditional region-growing methods, multiresolution methods, and graph-cut methods using data from the ISMRM 2012 Challenge. All methods were scored on a scale of 0 to 10000. RESULTS: The average score of all 17 data sets from the ISMRM 2012 Challenge was 9928, with 13 of the 17 scores surpassing 9900. The lowest score was 9697 from data set #12; there was no apparent fat-water swap observed throughout these data sets. CONCLUSIONS: The self-feeding mechanism of phasor estimation ensures the reliability of seed pixel selection at the finest resolution. Compared with traditional multiple resolution methods and region-growing methods, the proposed method is shown to be more robust when applied to disjoint areas and to regions with strong field inhomogeneity. Magn Reson Med 77:2390-2401, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Comparative analysis of hepatic fat quantification across 5 T, 3 T and 1.5 T: A study on consistency and feasibility.

OBJECTIVES: Magnetic resonance imaging (MRI) is a critical noninvasive technique for evaluating liver steatosis, with efficient and precise fat quantification being essential for diagnosing liver diseases. This study leverages 5 T ultra-high-field MRI to demonstrate the clinical significance of liver fat quantification, and explores the consistency and accuracy of the Proton Density Fat Fraction (PDFF) in the liver across different magnetic field strengths and measurement methodologies. METHODS: The study involved phantoms with lipid contents ranging from 0 % to 30 % and 35 participants (21 females, 14 males; average age 30.17 ± 13.98 years, body mass index 25.84 ± 4.76, waist-hip ratio 0.84 ± 0.09). PDFF measurements were conducted using chemical shift encoded (CSE) MRI at 5 T, 3 T, and 1.5 T, alongside magnetic resonance spectroscopy (MRS) at 5 T and 1.5 T for both liver and phantoms, analyzed using jMRUI software. The MRS-derived PDFF values served as the reference standard. Repeatability of 5 T MRI measurements was assessed through correlation analysis, while accuracy was evaluated using linear regression analysis against the reference standards. RESULTS: The CSE-PDFF measurements at 5 T demonstrated strong consistency with those at 3 T and 1.5 T, showing high intraclass correlation coefficients (ICC) of 0.988 and 0.980, respectively (all p < 0.001). There was also significant consistency across ROIs within liver lobes, with ICC values ranging from 0.975 to 0.986 (all p < 0.001). MRS-PDFF measurements for both phantoms and liver at 5 T and 1.5 T exhibited substantial agreement, with ICC values of 0.996 and 0.980, respectively (all p < 0.001). Particularly, ICC values for ROIs in the liver ranged from 0.963 to 0.990 (all p < 0.001). Despite overall agreement, statistically significant differences were noted in specific ROIs within the liver lobes (p = 0.004 and 0.012). The CSE and MRS PDFF measurements at 5 T displayed strong consistency, with an ICC of 0.988 (p < 0.001), and significant agreement was also found between 5 T CSE and 1.5 T MRS PDFF measurements, with an ICC of 0.978 (p < 0.001). Agreement was significant within the ROIs of the liver lobes on the same platform at 5 T, with ICC values ranging from 0.986 to 0.991 (all p < 0.001). CONCLUSION: PDFF measurements at 5 T MR imaging exhibited both accuracy and repeatability, indicating that 5 T imaging provides reliable quantification of liver fat content and shows substantial potential for clinical diagnostic applications.

Successful allogeneic fecal microbiota transplantation for severe diversion colitis: a case report.

Ileostomy diverts the flow of feces, which can result in malnutrition in the distal part of the intestine. The diversity of the gut microbiota consequently decreases, ultimately leading to intestinal dysbiosis and dysfunction. This condition can readily result in diversion colitis (DC). Potential treatment strategies include interventions targeting the gut microbiota. In this case study, we effectively treated a patient with severe DC by ileostomy and allogeneic fecal microbiota transplantation (FMT). A 69-year-old man presented with a perforated malignant tumor in the descending colon and an iliac abscess. He underwent laparoscopic radical sigmoid colon tumor resection and prophylactic ileostomy. Follow-up colonoscopy 3 months postoperatively revealed diffuse intestinal mucosal congestion and edema along with granular inflammatory follicular hyperplasia, leading to a diagnosis of severe DC. After two rounds of allogeneic FMT, both the intestinal mucosal bleeding and edema significantly improved, as did the diversity of the gut microbiota. The positive outcome of allogeneic FMT in this case highlights the potential advantages that this procedure can offer patients with DC. However, few studies have focused on allogeneic FMT, and more in-depth research is needed to gain a better understanding.

MMCA-NET: A Multimodal Cross Attention Transformer Network for Nasopharyngeal Carcinoma Tumor Segmentation Based on a Total-Body PET/CT System.

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive nature of manual delineation for radiotherapy. In this paper, MMCA-Net, a novel segmentation network for NPC using PET/CT images that incorporates an innovative multimodal cross attention transformer (MCA-Transformer) and a modified U-Net architecture, is introduced to enhance modal fusion by leveraging cross-attention mechanisms between CT and PET data. Our method, tested against ten algorithms via fivefold cross-validation on samples from Sun Yat-sen University Cancer Center and the public HECKTOR dataset, consistently topped all four evaluation metrics with average Dice similarity coefficients of 0.815 and 0.7944, respectively. Furthermore, ablation experiments were conducted to demonstrate the superiority of our method over multiple baseline and variant techniques. The proposed method has promising potential for application in other tasks.

Ability of dynamic gadoxetic acid-enhanced magnetic resonance imaging combined with water-specific T1 mapping to reflect inflammation in a rat model of early-stage nonalcoholic steatohepatitis.

Background: Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA) has shown potential in reflecting the hepatic function alterations in nonalcoholic steatohepatitis (NASH). The purpose of this study was to evaluate whether Gd-EOB-DTPA combined with water-specific T1 (wT1) mapping can be used to detect liver inflammation in the early-stage of NASH in rats. Methods: In this study, 54 rats with methionine- and choline-deficient (MCD) diet-induced NASH and 10 normal control rats were examined. A multiecho variable flip angle gradient echo (VFA-GRE) sequence was performed and repeated 40 times after the injection of Gd-EOB-DTPA. The wT1 of the liver and the reduction rate of wT1 (rrT1) were calculated. All rats were histologically evaluated and grouped according to the NASH Clinical Research Network scoring system. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of Gd-EOB-DTPA transport genes. Analysis of variance and least significant difference tests were used for multiple comparisons of quantitative results between all groups. Multiple regression analysis was applied to identify variables associated with precontrast wT1 (wT1pre), and receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance. Results: The rats were grouped according to inflammatory stage (G0 =4, G1 =15, G2 =12, G3 =23) and fibrosis stage (F0 =26, F1 =19, F2 =9). After the infusion of Gd-EOB-DTPA, the rrT1 showed significant differences between the control and NASH groups (P<0.05) but no difference between the different inflammation and fibrosis groups at any time points. The areas under curve (AUCs) of rrT1 at 10, 20, and 30 minutes were only 0.53, 0.58, and 0.61, respectively, for differentiating between low inflammation grade (G0 + G1) and high inflammation grade (G2 + G3). The MRI findings were verified by qRT-PCR examination, in which the Gd-EOB-DTPA transporter expressions showed no significant differences between any inflammation groups. Conclusions: The wT1 mapping quantitative method combined with Gd-EOB-DTPA was not capable of discerning the inflammation grade in a rat model of early-stage NASH.

MARFit: An Integrated Software for Real-Time MR Guided Focused Ultrasound Neuromodulation System.

MR guided focused ultrasound (MRgFUS) therapy has been a promising treatment modality for many neurological disorders. However, the lack of real-time image processing software platform sets barriers for relevant pre-clinical researches. This work intends to develop an integrated software for MRgFUS therapy. The software contains three functional modules: a communication module, an image post-processing module, and a visualization module. The communication module provides a data interface with an open-source MR image reconstruction platform (Gadgetron) to receive the reconstructed MR images in real-time. The post-processing module contains the algorithms of image coordinate registration, focus localization by MR acoustic radiation force imaging (MR-ARFI), temperature and thermal dose calculations, motion correction, and temperature feedback control. The visualization module displays monitoring information and provides a user-machine interface. The software was tested to be compatible with systems from two different vendors and validated in multiple scenarios for MRgFUS. The software was tested in many ex vivo and in vivo experiments to validate its functions. The in vivo transcranial focus localization experiments were carried out for targeting the focused ultrasound in neuromodulation.