Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Automated Measurement of Pancreatic Fat and Iron Concentration Using Multi-Echo and T1-Weghted MRI Data

We present an automated method for estimation of proton density fat fraction and iron concentration in the pancreas using both structural and quantitative imaging data present in the UK Biobank abdominal MRI acquisition protocol. Our method relies on automatic segmentation of 3D T1-weighted MRI data using a convolutional neural network and extracting the location of the multi-echo slice through the segmented volume. We finally estimate the fat and iron content in the pancreas using the extracted segmentation as a mask on the multi-echo data. Our segmentation model achieves a mean dice similarity coefficient of 0.842±0.071 on unseen data, which is comparable to the current state of the art for 3D segmentation of the pancreas. The proposed method is efficient and robust and enables an enhanced analysis of spatial distribution of proton density fat fraction and iron concentration over the current practice of manually placing regions of interest on often ambiguous multi-echo data

Comparison of magnetic resonance spectroscopy, proton density fat fraction and histological analysis in the quantification of liver steatosis in children and adolescents

AIM: To establish a threshold value for liver fat content between healthy children and those with non-alcoholic fatty liver disease (NAFLD) by using magnetic resonance imaging (MRI), with liver biopsy serving as a reference standard. METHODS: The study was approved by the local ethics committee, and written informed consent was obtained from all participants and their legal guardians before the study began. Twenty-seven children with NAFLD underwent liver biopsy to assess the presence of nonalcoholic steatohepatitis. The assessment of liver fat fraction was performed using MRI, with a high field magnet and 2D gradient-echo and multiple-echo T1-weighted sequence with low flip angle and single-voxel point-resolved ¹H MR-Spectroscopy (¹H-MRS), corrected for T1 and T2* decays. Receiver operating characteristic curve analysis was used to determine the best cut-off value. Lin coefficient test was used to evaluate the correlation between histology, MRS and MRI-PDFF. A Mann-Whitney U-test and multivariate analysis were performed to analyze the continuous variables. RESULTS: According to MRS, the threshold value between healthy children and those with NAFLD is 6%; using MRI-PDFF, a cut-off value of 3.5% is suggested. The Lin analysis revealed a good fit between the histology and MRS as well as MRI-PDFF. CONCLUSION: MRS is an accurate and precise method for detecting NAFLD in children

Respiratory organ motion in interventional MRI : tracking, guiding and modeling

Respiratory organ motion is one of the major challenges in interventional MRI, particularly in interventions with therapeutic ultrasound in the abdominal region. High-intensity focused ultrasound found an application in interventional MRI for noninvasive treatments of different abnormalities. In order to guide surgical and treatment interventions, organ motion imaging and modeling is commonly required before a treatment start. Accurate tracking of organ motion during various interventional MRI procedures is prerequisite for a successful outcome and safe therapy. In this thesis, an attempt has been made to develop approaches using focused ultrasound which could be used in future clinically for the treatment of abdominal organs, such as the liver and the kidney. Two distinct methods have been presented with its ex vivo and in vivo treatment results. In the first method, an MR-based pencil-beam navigator has been used to track organ motion and provide the motion information for acoustic focal point steering, while in the second approach a hybrid imaging using both ultrasound and magnetic resonance imaging was combined for advanced guiding capabilities. Organ motion modeling and four-dimensional imaging of organ motion is increasingly required before the surgical interventions. However, due to the current safety limitations and hardware restrictions, the MR acquisition of a time-resolved sequence of volumetric images is not possible with high temporal and spatial resolution. A novel multislice acquisition scheme that is based on a two-dimensional navigator, instead of a commonly used pencil-beam navigator, was devised to acquire the data slices and the corresponding navigator simultaneously using a CAIPIRINHA parallel imaging method. The acquisition duration for four-dimensional dataset sampling is reduced compared to the existing approaches, while the image contrast and quality are improved as well. Tracking respiratory organ motion is required in interventional procedures and during MR imaging of moving organs. An MR-based navigator is commonly used, however, it is usually associated with image artifacts, such as signal voids. Spectrally selective navigators can come in handy in cases where the imaging organ is surrounding with an adipose tissue, because it can provide an indirect measure of organ motion. A novel spectrally selective navigator based on a crossed-pair navigator has been developed. Experiments show the advantages of the application of this novel navigator for the volumetric imaging of the liver in vivo, where this navigator was used to gate the gradient-recalled echo sequence

Feasibility of automated 3-dimensional magnetic resonance imaging pancreas segmentation.

PurposeWith the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods.Methods and materialT2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT).ResultsAll VIBE images were successfully segmented by at least one of the auto-segmentation method with DI >0.83 and SHIFT ≤2 mm using the best automated segmentation method. The automated segmentation error of HASTE images was significantly greater. DL is statistically superior to the other methods in Dice's overlapping index. For the Hausdorff distance and SHIFT measurement, DRLS and DL performed slightly superior to the GC method, and substantially superior to MSM. DL required least human supervision and was faster to compute.ConclusionOur study demonstrated potential feasibility of automated segmentation of the pancreas on MRI images with minimal human supervision at the beginning of imaging acquisition. The achieved accuracy is promising for organ localization

Deficits in trabecular bone microarchitecture in young women with Type 1 diabetes mellitus

Context: The pathophysiological mechanism of increased fractures in young adults with Type 1 Diabetes Mellitus (T1DM) is unclear. Objective: Case:control study of trabecular bone microarchitecture and vertebral marrow adiposity in young women with T1DM. Patients and Settings: 30 women with T1DM with a median (range) age of 22.0yrs (16.9, 36.1) attending one outpatient clinic with a median age at diagnosis of 9.7yrs (0.46, 14.8) were compared to 28 age-matched healthy women who acted as controls. Methods and Main Outcome Measures: Measurements included MRI-based assessment of proximal tibial bone volume/total volume (appBV/TV), trabecular separation (appTb.Sp), vertebral bone marrow adiposity (BMA) and abdominal adipose tissue and biochemical markers of GH/IGF-1 axis (IGF-1, IGFBP3, ALS) and bone turnover. Results: Median appBV/TV in cases and controls was 0.3 (0.22, 0.37) and 0.33 (0.26, 0.4), respectively (p = 0.018) and median appTb.Sp in T1DM was 2.59 (2.24, 3.38) and 2.32 (2.03, 2.97), respectively (p = 0.012). The median appBV/TV was 0.28 (0.22, 0.33) in those cases with retinopathy (n,15) compared to 0.33 (0.25, 0.37) in those without retinopathy (p = 0.02). Although median visceral adipose tissue in cases was higher than in controls at 5,733mm3 (2030, 11,144) and 3,460mm3 (1,808, 6,832), respectively (p = 0.012), there was no difference in median BMA which was 31.1% (9.9, 59.9) and 26.3% (8.5, 49.8) in cases and controls, respectively (p = 0.2). Serum IGF-1 and ALS were also lower in cases and the latter showed an inverse association to appTbSp (r = -0.30, p = 0.04). Conclusion: Detailed MRI studies in young women with childhood-onset T1DM have shown clear deficits in trabecular microarchitecture of the tibia. Underlying pathophysiological mechanisms may include a microvasculopathy

The first joint ESGAR/ ESPR consensus statement on the technical performance of cross-sectional small bowel and colonic imaging

Objectives: To develop guidelines describing a standardised approach to patient preparation and acquisition protocols for magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) of the small bowel and colon, with an emphasis on imaging inflammatory bowel disease. Methods: An expert consensus committee of 13 members from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) and European Society of Paediatric Radiology (ESPR) undertook a six-stage modified Delphi process, including a detailed literature review, to create a series of consensus statements concerning patient preparation, imaging hardware and image acquisition protocols. Results: One hundred and fifty-seven statements were scored for agreement by the panel of which 129 statements (82 %) achieved immediate consensus with a further 19 (12 %) achieving consensus after appropriate modification. Nine (6 %) statements were rejected as consensus could not be reached. Conclusions: These expert consensus recommendations can be used to help guide cross-sectional radiological practice for imaging the small bowel and colon. Key points: • Cross-sectional imaging is increasingly used to evaluate the bowel • Image quality is paramount to achieving high diagnostic accuracy • Guidelines concerning patient preparation and image acquisition protocols are provided

An Automatic Technique for MRI Based Murine Abdominal Fat Measurement

Because of the well-known relationship between obesity and high incidence of diseases, fat related research using mice models is being widely investigated in preclinical experiments. In the present study, we developed a technique to automatically measure mice abdominal adipose volume and determine the depot locations using Magnetic Resonance Imaging (MRI). Our technique includes an innovative method to detect fat tissues from MR images which not only utilizes the T1 weighted intensity information, but also takes advantage of the transverse relaxation time(T2) calculated from the multiple echo data. The technique contains both a fat optimized MRI imaging acquisition protocol that works well at 7T and a newly designed post processing methodology that can automatically accomplish the fat extraction and depot recognition without user intervention in the segmentation procedure. The post processing methodology has been integrated into easy-to-use software that we have made available via free download. The method was validated by comparing automated results with two independent manual analyses in 26 mice exhibiting different fat ratios from the obesity research project. The comparison confirms a close agreement between the results in total adipose tissue size and voxel-by-voxel overlaps

Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies

Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such asMRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat–muscle quantitation system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRATM Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variationwas 1.4%for IAAT and 1.2%for ASAT, the intra-observer correlationwas 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies