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

비알코올성 지방간 환자에서 정량적 초음파 영상 지표의 개발 및 지방간 진단능 평가

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의학과, 2021. 2. 이정민.Purpose: To investigate the diagnostic performance of quantitative ultrasound (QUS) parameters for the assessment of hepatic steatosis in patients with nonalcoholic fatty liver disease (NAFLD) using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the reference standard. Materials and methods: In this single-center prospective study, 120 patients with clinically suspected NAFLD were enrolled between March 2019 and January 2020. Participants underwent ultrasound (US) examination for radiofrequency (RF) data acquisition and chemical shift-encoded liver MRI for PDFF measurement. Using the RF data analysis, attenuation coefficient (AC) at tissue attenuation imaging (TAI) and scatter-distribution coefficient (SC) at tissue scatter-distribution imaging (TSI) were measured. Correlation between the QUS parameters (AC and SC) and MRI-PDFF was evaluated using Pearson correlation coefficients. Diagnostic performance of AC at TAI and SC at TSI for detecting hepatic steatosis (MRI-PDFF ≥5%) and hepatic fat content ≥10% (MRI-PDFF ≥10%) were assessed by receiver operating characteristic (ROC) analysis. Significant clinical or imaging factors associated with AC and SC were analyzed using linear regression analysis. Results: Participants were classified with MRI-PDFF <5% (n=38), 5-10% (n=23), and ≥10% (n=59). AC at TAI and SC at TSI were significantly correlated with MRI-PDFF (r=0.659 and 0.727, P<0.001 for both). For detecting hepatic steatosis and hepatic fat content ≥10%, the area under the ROC curves (AUCs) of AC at TAI were 0.861 (95% confidence interval [CI]: 0.786-0.918) and 0.835 (95% CI: 0.757-0.897), and of SC at TSI were 0.964 (95% CI: 0.913-0.989) and 0.935 (95% CI: 0.875-0.972), respectively. In multivariate linear regression analysis, MRI-PDFF was an independent determinant of AC at TAI and SC at TSI. Conclusion: AC at TAI and SC at TSI derived from quantitative US RF data analysis yielded a good correlation with MRI-PDFF and provided good performance for detecting hepatic steatosis and assessing its severity in NAFLD.배경 및 목적: 본 연구에서는 비알코올성 지방간 환자에서 지방간 정도를 평가하기 위한 정량적 초음파 지표를 개발하고, 자기공명영상 양성자밀도 지방분율을 기준으로 하여 정량적 초음파 지표의 지방간 진단능을 평가하고자 한다. 재료 및 방법: 본 단일센터 전향적 연구에서는 2019년 3월부터 2020년 1월까지 임상적으로 비알코올성 지방간이 의심되는 환자와 간이식 공여자를 포함한 총 120명의 참가자가 등록되었다. 참가자들은 무선주파수 (radiofrequency, RF) 데이터를 얻기 위한 초음파 검사와 자기공명영상 양성자밀도 지방분율(Magnetic resonance imaging proton density fat fraction, MRI-PDFF) 검사를 시행하였다. 초음파 RF 데이터를 분석하여, 조직감쇠영상(tissue attenuation imaging, TAI)에서의 감쇠계수 (attenuation coefficient, AC)와 조직 산란분포 영상(tissue scatter-distribution imaging, TSI)에서의 산란분포계수 (scatter-distribution coefficient, SC)를 획득하였다. 이 두 정량적 초음파 지표 (AC, SC)와 자기공명영상 양성자밀도 지방분율(MRI-PDFF) 사이의 연관성을 피어슨 상관계수를 통해 분석하였다. 정량적 초음파 지표들이 MRI-PDFF ≥5% 와 MRI-PDFF ≥10%의 지방간을 진단하는 진단능을 Receiver operating characteristics (ROC) 분석을 통해 확인하였다. 또한, 다변량 회귀분석(multivariate linear regression analysis)을 통해, 두 정량적 초음파 지표에 영향을 주는 임상 또는 영상적 지표를 확인하였다. 결과: 참가자는 지방간 정도에 따라 세 단계로 구분되었다 (MRI-PDFF <5% (n=38), 5-10% (n=23), and ≥10% (n=59)). 감쇠계수 (AC at TAI)와 산란분포계수 (SC at TSI)는 자기공명영상 양성자밀도 지방분율과 강한 상관관게를 보였다 (r=0.659 and 0.727, P<0.001 for both). 지방간 유무 진단 (MRI-PDFF ≥5%)과 MRI-PDFF ≥10%의 지방간진단에 있어 감쇠계수의 진단능은 0.861 (95% confidence interval [CI]: 0.786-0.918) 과 0.835 (95% CI: 0.757-0.897)이었고, 산란분포계수의 진단능은 0.964 (95% CI: 0.913-0.989) and 0.935 (95% CI: 0.875-0.972) 이었다. 다변량회귀분석에서 지방분율이 정량적 초음파 지표와 연관성을 보이는 유일한 독립적인 인자로 확인되었다. 결론: 본 연구에서 감쇠계수 (AC at TAI)와 산란분포계수 (SC at TSI)는 자기공명영상 양성자 지방분율과 높은 상관성을 보였고, 지방간의 진단과 그 정도를 확인하는데 있어 높은 진단능을 보였다.Abstract -----------------------1 Contents -----------------------3 List of Tables -----------------4 List of Figures -----------------5 Introduction -----------------6 I. Pilot study -----------------8 Materials and Methods ---- 8 Results ----------------------13 II. Main study ----------------15 Materials and Methods --- 15 Results ----------------------22 Discussion ----------------25 References ----------------30 Tables ----------------------35 Figures ----------------------43 Appendix --------------- 46 Abstract in Korean --------- 49Docto

Measurement of liver fat fraction and iron with MRI and MR spectroscopy techniques.

Diffuse liver disease is a widespread global healthcare burden, and the abnormal accumulation of lipid and/or iron is common to important disease processes. Developing the improved methods for detecting and quantifying liver lipid and iron is an important clinical need. The inherent risk, invasiveness, and sampling error of liver biopsy have prompted the development of noninvasive imaging methods for lipid and iron assessment. Ultrasonography and computed tomography have the ability to detect diffuse liver disease, but with limited accuracy. The purpose of this review is to describe the current state-of-the-art methods for quantifying liver lipid and iron using magnetic resonance imaging and spectroscopy, including their implementation, benefits, and potential pitfalls. Imaging- and spectroscopy-based methods are naturally suited for lipid and iron quantification. Lipid can be detected and decomposed from the inherent chemical shift between lipid and water signals, whereas iron imparts significant paramagnetic susceptibility to tissue, which accelerates proton relaxation. However, measurements of these biomarkers are confounded by technical and biological effects. Current methods must address these factors to allow a precise correlation between the lipid fraction and iron concentration. Although this correlation becomes increasingly challenging in the presence of combined lipid and iron accumulation, advanced techniques show promise for delineating these quantities through multi-lipid peak analysis, T2 water mapping, and fast single-voxel water-lipid spectroscopy

Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop

More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water–fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based “big data” studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work

Simulation of a Virtual Iron-Overload Model and R2* estimation using Multispectral Fat-Water Models for GRE and UTE Acquisitions using MRI

Iron overload is an excessive accumulation of iron in the body and can be either inherited or acquired through chronic blood transfusions. Assessment of hepatic iron concentration (HIC) is important in the management and monitoring of iron overload. Despite liver biopsy being the gold standard method for assessing HIC, it is invasive, painful, unsuitable for repeated measurements, and carries the risk of bleeding and infection. Magnetic Resonance Imaging (MRI) methods based on transverse relaxation rate (R2*) have emerged as a non-invasive alternative to liver biopsy for assessing HIC. Multispectral fat-water-R2* modeling techniques, such as the non-linear square (NLSQ) fitting and autoregressive moving average (ARMA) models, have been proposed to provide more accurate assessments of iron overload by accounting for the presence of fat, which can otherwise confound R2*-based HIC measurements in conditions of co-existing iron overload and steatosis. However, the R2* estimation by these multispectral models has not been systematically investigated for various acquisition methods like the multiecho gradient echo (GRE) and ultrashort echo time (UTE) across the full clinically relevant range of HICs. To address this challenge, a Monte Carlo-based iron overload model based on true iron morphometry and histological data was constructed, and MRI signals were synthesized at 1.5 T and 3 T field strengths. This study compared the accuracy and precision of multispectral NLSQ and ARMA models against the monoexponential model and published in vivo R2*-HIC calibrations in estimating R2*. The results showed that, for GRE acquisitions, ARMA and NLSQ models produced higher slopes compared to the monoexponential model and published in vivo R2*-HIC calibrations. However, for UTE acquisitions for shorter echo spacing (≤ 0.5 ms) and longer maximum echo time, TEmax (≥ 6 ms), both multispectral and monoexponential signal models produced similar R2*-HIC slopes and precision values across the full clinical spectrum of HICs at both 1.5 T and 3 T. The results from the simulation studies were validated using phantoms and patient data. Future work should investigate the performance of multispectral models by simulating liver models in coexisting conditions of iron overload and steatosis to investigate simultaneous and accurate quantification of both R2* and fat

Feasibility of Using Virtual Unenhanced Images to Replace Pre-contrast Images in Multiphase Renal CT Examinations

Multiphase renal CT exams are a commonly used imaging technique for the diagnosis of renal masses. The pre-contrast, or true unenhanced (TUE), image provides a baseline for enhancement measurements which is an important criteria used to characterize renal lesions, consequently it is crucial that CT numbers measured in TUE images be accurate. The purpose of this work is to assess the feasibility of replacing TUE with virtual unenhanced (VUE) images derived from DECT data in renal CT exams. Eliminating TUE image acquisition would reduce patient dose and increase patient throughput, improving clinical efficiency. A retrospective study was conducted for 60 consecutively selected patient exams. VUE and TUE images were compared qualitatively and the differences were tested using a Bayesian Hierarchical model. VUE images were found to be inferior to TUE images for visualization of major vessels and depiction of liver parenchyma. CT numbers were measured in the liver, spleen, spine, aorta, cystic lesions, subcutaneous fat, renal cortex and medulla, and the differences were tested with a Student’s paired t-test. There were significant differences between TUE and VUE measurements ( p-value \u3e 0.05) in the spleen, spine, aorta, renal cortex, subcutaneous fat, and inferior vena cava. However, evaluation of the clinical relevance based on grayscale perceptibly indicated that the difference for the spleen and subcutaneous fat are not clinically meaningful. The rapid kVp-switching GE CT750HD scanner was used to assess enhancement accuracy when using VUE compare to TUE images as the baseline for enhancement calculations across a wide range of clinical scenarios simulated in a phantom study, and the results were analyzed using Bayesian Hierarchical models. For simulation of angiomyolipoma and benign cystic lesions, enhancement values were not significantly different. However, for simulation of Bosniak category II-IV lesions, differences in measured enhancement were found to be significant. Additionally, the effect of ASIR level used in image reconstruction was assessed, and found not to affect measured CT number using a mixed effects model. Differences in measured enhancement values for simulated borderline enhancing renal lesions demonstrate that replacement of TUE with VUE images is not feasible with the current iteration of the algorithm

Analyse der Körperzusammensetzung: Messung der Skelettmuskulatur mit Computertomographie und Implikationen für die Patientenversorgung

Objective: This thesis aims to evaluate the relationship between the skeletal muscle index derived from computed tomography (CT) images and patient outcomes, as well as its implications for patient care. This goal was pursued in five individual studies: Studies A and B evaluated the relationship between the lumbar skeletal muscle index (L3SMI) and patient outcomes in the intensive care unit (ICU) and oncology setting, respectively. Studies C and D evaluated the effect of CT acquisition parameters on body composition measures. Study E proposed a novel technique to automate the segmentation of skeletal muscle using a fully automated deep learning system. Material and methods: In total, 1328 axial CT images were included in the five studies. Patients in studies A and B were part of the clinical trials NCT01967056 and NCT01401907 at Massachusetts General Hospital (MGH), respectively. Body composition indices were computed using semi-automated segmentation. Multivariable regression models with a priori defined covariates were used to analyze clinical outcomes. To evaluate whether CT acquisition parameters influence segmentation, the Bland-Altman approach was used. In study E, a fully convolutional neural network was implemented for deep learning-based automatic segmentation. Results: Study A found lower L3SMI to be a predictor of increased mortality within 30 days of extubation (p = 0.033), increased rate of pneumonia within 30 days of extubation (p = 0.002), increased adverse discharge disposition (p = 0.044), longer hospital stays post-extubation (p = 0.048), and higher total hospital costs (p = 0.043). In study B, low L3SMI was associated with worse quality of life (p = 0.048) and increased depression symptoms (p = 0.005). Threshold-based segmentation of skeletal muscle in study C and adipose tissue compartments in study D were significantly affected by CT acquisition parameters. The proposed deep learning system in study E provided automatic segmentation of skeletal muscle cross-sectional area and achieved a high congruence to segmentations performed by domain experts (average Dice coefficient of 0.93). Conclusion: L3SMI is a useful tool for the assessment of muscle mass in clinical practice. In critically ill patients, L3SMI can provide clinically useful information about patient outcomes at the time of extubation. Patients with advanced cancer who suffered from low muscle mass reported worse quality of life and increased depression symptoms. This highlights the clinical relevance of addressing muscle loss early on as part of a multimodal treatment plan. Importantly, indices utilized in body composition analysis are significantly affected by CT acquisition parameters. These effects should be considered when body composition analysis is used in clinical practice or research studies. Finally, our fully automated deep learning system enabled instantaneous segmentation of skeletal muscle.Zielsetzung: Das Ziel der vorliegenden Dissertation war es, den Einfluss des auf CT-Bildern berechneten Skelettmuskelindexes auf klinische Ergebnisse von Patienten und die daraus resultierenden Implikationen für die Patientenversorgung zu evaluieren. Dieses Ziel wurde in fünf Einzelstudien verfolgt: In den Studien A und B wurde der Einfluss des lumbalen Skelettmuskelindex (L3SMI) auf klinische Endpunkte von Patienten auf der Intensivstation sowie in der Onkologie untersucht. Die Studien C und D evaluierten die Auswirkungen von CT-Akquisitionsparametern auf Indizes der Körperzusammensetzung. Studie E stellte eine neuartige Technik der automatisierten Segmentierung von Skelettmuskulatur vor, die durch maschinelles Lernen ermöglicht wurde. Material und Methoden: Insgesamt wurden 1328 axiale CT-Bilder in die fünf Studien eingeschlossen. Die Patienten der Studien A und B waren Teilnehmer der klinischen Studien NCT01967056 und NCT01401907 am Massachusetts General Hospital. Die Indizes der Körperzusammensetzung wurden mithilfe halbautomatischer Segmentierung berechnet. Die klinischen Endpunkte wurden in multivariablen Regressionsmodellen mit a priori definierten Kovariaten analysiert. Um zu evaluieren, ob CT-Akquisitionsparameter die Segmentierung beeinflussen, wurde der Bland-Altman-Ansatz verwendet. In Studie E wurden ein künstliches neuronales Netzwerk sowie maschinelles Lernen für die automatische Segmentierung eingesetzt. Ergebnisse: In Studie A war ein niedriger L3SMI ein Prädiktor für eine höhere Mortalität (p = 0.033) und Pneumonierate (p = 0.002) innerhalb von 30 Tagen nach der Extubation sowie für mehr ungünstige Entlassungen (p = 0.044) und höhere Behandlungskosten für den gesamten Krankenhausaufenthalt (p = 0.043). Ein niedriger L3SMI war in Studie B mit einer schlechteren Lebensqualität (p = 0.048) und stärkeren depressiven Symptomen (p = 0.005) assoziiert. Die schwellenwertbasierte Segmentierung der Skelettmuskulatur in Studie C und der Fettgewebekompartimente in Studie D wurde durch CT-Akquisitionsparameter signifikant beeinflusst. Das in Studie E vorgestellte vollautomatische Segmentierungssystem erreichte eine hohe Übereinstimmung mit den durch Experten erstellten Segmentationen (durchschnittlicher Dice-Koeffizient von 0.93). Fazit: Der L3SMI ist ein Werkzeug zur Beurteilung von Muskelmasse. Bei Intensivpatienten kann L3SMI zum Zeitpunkt der Extubation nützliche klinische Informationen liefern. Patienten mit fortgeschrittener Krebserkrankung, die zudem eine geringere Muskelmasse hatten, berichteten über eine schlechtere Lebensqualität und stärkere depressive Symptome. Dies unterstreicht die Notwendigkeit, die Muskulatur frühzeitig als Teil eines multimodalen Behandlungskonzeptes zu adressieren. Die Indizes der Körperzusammensetzung werden signifikant von CT-Akquisitionsparametern beeinflusst. Darüber hinaus ermöglichte unser vollautomatisiertes System dank maschinellen Lernens die verzögerungsfreie Segmentierung von Skelettmuskulatur