Purpose Several recent studies have used a three‐tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion‐weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF‐like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long‐term stability of these metrics have not yet been evaluated. Methods This study examined estimates of whole‐brain microstructure for the three tissue compartments, in three separate test–retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters. Results The CSF‐like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter–like and gray matter–like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3‐month interval cohort to both compartments having ICC > 0.80. Regional CSF‐like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort. Conclusion The three‐tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue‐microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three‐tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

Purpose To describe a nonlinear finite element analysis method by using magnetic resonance (MR) images for the assessment of the mechanical competence of the hip and to demonstrate the reproducibility of the tool. Materials and Methods This prospective study received institutional review board approval and fully complied with HIPAA regulations for patient data. Written informed consent was obtained from all subjects. A nonlinear finite element analysis method was developed to estimate mechanical parameters that relate to hip fracture resistance by using MR images. Twenty-three women (mean age ± standard deviation, 61.7 years ± 13.8) were recruited from a single osteoporosis center. To thoroughly assess the reproducibility of the finite element method, three separate analyses were performed: a test-retest reproducibility analysis, where each of the first 13 subjects underwent MR imaging on three separate occasions to determine longitudinal variability, and an intra- and interoperator reproducibility analysis, where a single examination was performed in each of the next 10 subjects and four operators independently performed the analysis two times in each of the subjects. Reproducibility of parameters that reflect fracture resistance was assessed by using the intraclass correlation coefficient and the coefficient of variation. Results For test-retest reproducibility analysis and inter- and intraoperator analyses for proximal femur stiffness, yield strain, yield load, ultimate strain, ultimate load, resilience, and toughness in both stance and sideways-fall loading configurations each had an individual median coefficient of variation of less than 10%. Additionally, all measures had an intraclass correlation coefficient higher than 0.99. Conclusion This experiment demonstrates that the finite element analysis model can consistently and reliably provide fracture risk information on correctly segmented bone images. RSNA, 2016 Online supplemental material is available for this article.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

Rationale and Objectives Severe progressive multi-focal heterotopic ossification (HO) is a rare occurrence seen predominantly in patients who have fibrodysplasia ossificans progressiva (FOP) and is difficult to quantitate due to patient-, disease-, logistical-, and radiation-related issues. The purpose of this study was to develop and validate a scoring system based on plain radiographs for quantitative assessment of HO lesions in FOP patients. Materials and Methods IRB approval was obtained from the University of Pennsylvania and all data comply with HIPAA regulations. The University of Pennsylvania Institutional Animal Care and Use Committee (IACUC) approved the use of mice in this study. First, we used a mouse model of FOP-like HO to validate a semi-quantitative analogue scale for estimating relative heterotopic bone volume. Second, we used this validated scale to estimate the relative amount of HO from a retrospective analysis of plain radiographs from 63 patients with classic FOP. Finally, the scale was applied to a retrospective analysis of CT images from three FOP patients. Results In the FOP-mouse model, the observed rating on the analogue scale is highly correlated to heterotopic bone volumes measured by micro-computed tomography (R2 = 0.89). The scoring system that was applied to radiographs of FOP patients captured the clinical range of HO typically present at all axial and appendicular sites. Analysis of CT scans of FOP patients found that observed radiograph ratings were highly correlated with HO volume (R2 = 0.80). Conclusion The scoring system described here could enable practical, quantitative assessment of HO in clinical trials to evaluate new treatment modalities, especially for FOP. Clinical Relevance The development of the six-point analogue scale described here provides and validates a much-needed, reproducible and quantifiable method for describing and assessing heterotopic ossification in FOP patients. This scale has the potential to be a key descriptor that can inform FOP patients and clinicians about disease progression and response of heterotopic ossification lesions to interventions and treatments.