Hepatic magnetic resonance T1-mapping and extracellular volume fraction compared to shear-wave elastography in pediatric Fontan-associated liver disease

Abstract Background Children with Fontan circulation are at risk of developing hepatic fibrosis/cirrhosis. Reliable noninvasive monitoring techniques are lacking or under development. Objective To investigate surrogate indicators of hepatic fibrosis in adolescents with Fontan circulation by evaluating hepatic magnetic resonance (MR) T1 mapping and extracellular volume fraction measurements compared to US shear-wave elastography. Materials and methods We analyzed hepatic native T1 times and extracellular volume fractions with modified Look-Locker inversion recovery. Liver stiffness was analyzed with shear-wave elastography. We compared results between 45 pediatric patients ages 16.7±0.6 years with Fontan circulation and 15 healthy controls ages 19.2±1.2 years. Measurements were correlated to clinical and hemodynamic data from cardiac catheterization. Results MR mapping was successful in 35/45 patients, revealing higher hepatic T1 times (774±44 ms) than in controls (632±52 ms; P <0.001) and higher extracellular volume fractions (47.4±5.0%) than in controls (34.6±3.8%; P <0.001). Liver stiffness was 1.91±0.13 m/s in patients vs. 1.20±0.10 m/s in controls ( P <0.001). Native T1 times correlated with central venous pressures (r=0.5, P =0.007). Native T1 was not correlated with elastography in patients (r=0.2, P =0.1) or controls (r = −0.3, P =0.3). Extracellular volume fraction was correlated with elastography in patients (r=0.5, P =0.005) but not in controls (r=0.2, P =0.6). Conclusion Increased hepatic MR relaxometry and shear-wave elastography values in adolescents with Fontan circulation suggested the presence of hepatic fibrosis or congestion. Central venous pressure was related to T1 times. Changes were detected differently with MR relaxometry and elastography; thus, these techniques should not be used interchangeably in monitoring hepatic fibrosis

Automated segmentation of magnetic resonance bone marrow signal: a feasibility study

Background - Manual assessment of bone marrow signal is time-consuming and requires meticulous standardisation to secure adequate precision of findings. Objective - We examined the feasibility of using deep learning for automated segmentation of bone marrow signal in children and adolescents. Materials and methods - We selected knee images from 95 whole-body MRI examinations of healthy individuals and of children with chronic non-bacterial osteomyelitis, ages 6–18 years, in a longitudinal prospective multi-centre study cohort. Bone marrow signal on T2-weighted Dixon water-only images was divided into three color-coded intensity-levels: 1 = slightly increased; 2 = mildly increased; 3 = moderately to highly increased, up to fluid-like signal. We trained a convolutional neural network on 85 examinations to perform bone marrow segmentation. Four readers manually segmented a test set of 10 examinations and calculated ground truth using simultaneous truth and performance level estimation (STAPLE). We evaluated model and rater performance through Dice similarity coefficient and in consensus. Results - Consensus score of model performance showed acceptable results for all but one examination. Model performance and reader agreement had highest scores for level-1 signal (median Dice 0.68) and lowest scores for level-3 signal (median Dice 0.40), particularly in examinations where this signal was sparse. Conclusion - It is feasible to develop a deep-learning-based model for automated segmentation of bone marrow signal in children and adolescents. Our model performed poorest for the highest signal intensity in examinations where this signal was sparse. Further improvement requires training on larger and more balanced datasets and validation against ground truth, which should be established by radiologists from several institutions in consensus