Faculty Bibliography

Introduction: Although often characterized as a disease of white matter, gray matter (GM) pathology has been shown to play an important role in multiple sclerosis (MS).
Objective(s): We used diffusion kurtosis imaging (DKI), a clinically feasible extension of diffusion tensor imaging (DTI) to characterize pathology in cortical and subcortical GM regions in MS patients compared to controls and study how selected DKI parameters correlate with disease severity in comparison to traditional volumetric approaches.
Method(s): 36 MS patients and 24 age and gender matched controls were enrolled in the study. MS patients completed a Patient Determined Disease Steps Score (PDDS). All patients received MRI on a 3T MR Scanner (Siemens, Skyra, or Prisma), which included whole brain 3D magnetization-prepared rapid gradientecho (MPRAGE) (1 mm³ isotropic resolution) for extracting volumetrics and monopolar diffusion-weighted echo-planar imaging (EPI) (voxel size = 1.7 x 1.7 x 3 mm³, b=0, 250, 1000, and 2000 s/m² along 84 directions, TE/TR = 100/3500 ms, GRAPPA with acceleration 2, and multiband 2) for deriving diffusion metrics. Volume metrics from automatic segmentation from MPRAGE and diffusion metrics which included mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA) were derived for 7 subcortical and 5 cortical GM regions. We determined the partial correlations between PDDS and either GM volume or diffusion parameters covarying for gender and age. We also determined the differences in volume and diffusion metrics between MS patients and controls using ANCOVA with age as the covariate.
Result(s): We observed statistically significant differences in volumes between MS patients and controls for the amygdala, caudate, putamen, nucleus accumbens, cingulate lobe, and subcortical gray volumes with p-values ranging from 0.001 to 0.044. Statistically significant group differences were observed in a majority of the ROI for FA, MD, and MK. Overall, FA was increased, MD was increased, and MK was decreased for most ROI in MS patients compared to controls. There was an increased number of significant partial correlations between PDDS and diffusion metrics compared to PDDS and volume metrics, specifically positive correlations for occipital lobe MD and FA and negative correlations for hippocampal FA.
Conclusion(s): Our results suggest that DKI metrics are sensitive to changes in GM and complimentary to GM volumetrics as an index of GM pathology

Objective: To use quantitative metrics from a large heterogenous population of MS PATHS (Partners Advancing Technology for Health Solutions) patients to derive an integrated view of MS disease course.
Background(s): A commonly used diagram to describe MS disease course shows how various measures change over time. The curves are derived hypothetically, and the best fit patterns, e.g. linear, accelerating, are uncertain. It is also unknown whether the diagrams reflect the current era of disease modifying therapies.
Method(s): In MS PATHS, 2 standardized MRI acquisition sequences (3D FLAIR and 3D T1 on Siemens 3T scanners) were incorporated into routine MS MRI protocols at all participating institutions. A software prototype (MSPie) was developed for automated calculation of brain parenchymal fraction (BPF), total T2 lesion volume (T2LV), and new T2 lesion counts (newT2). The Multiple Sclerosis Performance Test (MSPT) was used to complete neuroperformance tests and questionnaires, including Patient Determined Disease Steps (PDDS) and self-reported relapses. Serum was collected as part of an MS PATHS biomarker sub-study and analyzed by SIMOA kit assay to measure serum neurofilament light (sNfL). Cross-sectional data from patients with MRI metrics were analyzed using linear regression to calculate slopes, and tests for quadratic terms to test linearity, for each measure vs disease duration.
Result(s): 5215 unique patients (mean[sd] age=45.9[11.9]; disease duration=11.9[8.8] years) had MRI metrics. Over nearly 4 decades of MS, BPF showed a linear decrease (slope=-0.16%/year) while PDDS and T2LV showed a linear increase, with annual slopes of 0.076/year and 0.51ml/year, respectively. Linear terms (slopes) were highly significant (p< 10^-15); whereas quadratic terms were weak (p< 0.05). Markers of inflammatory activity, including newT2 and relapses, stayed constant/decreased over the course of MS, with annual slopes of -0.01 (p=0.174) and -0.01 (p< 10^-6), respectively. Log(sNfL) increased linearly (slope= 0.015/year, p< 10^-14).
Conclusion(s): Standardization of MRIs across an international network is feasible, enabling high quality MRI-based metrics and systematic learning from routine patient care. Although limited by the cross-sectional nature of the analyses, these results show strong linearity observed for various measures of disease progression, suggesting that MS neither stabilizes nor accelerates in later stages, unlike some hypothetical diagrams of disease evolution

PURPOSE/OBJECTIVE:Familial dysautonomia (FD) is a rare autosomal recessive disease that affects the development of sensory and autonomic neurons, including those in the cranial nerves. We aimed to determine whether conventional brain magnetic resonance imaging (MRI) could detect morphologic changes in the trigeminal nerves of these patients. METHODS:Cross-sectional analysis of brain MRI of patients with genetically confirmed FD and age- and sex-matched controls. High-resolution 3D gradient-echo T1-weighted sequences were used to obtain measurements of the cisternal segment of the trigeminal nerves. Measurements were obtained using a two-reader consensus. RESULTS:in controls (P < 0.001). No association between trigeminal nerve area and age was found in patients or controls. CONCLUSIONS:Using conventional MRI, the caliber of the trigeminal nerves was significantly reduced bilaterally in patients with FD compared to controls, a finding that appears to be highly characteristic of this disorder. The lack of correlation between age and trigeminal nerve size supports arrested neuronal development rather than progressive atrophy.

Facial transplant (FT) is a viable option for patients with severe craniomaxillofacial deformities. Transplant imaging requires coordination between radiologists and surgeons and an understanding of the merits and limitations of imaging modalities. Digital subtraction angiography and CT angiography are critical to mapping vascular anatomy, while volume-rendered CT allows evaluation of osseous defects and landmarks used for surgical cutting guides. This article highlights the components of successful FT imaging at two institutions and in two index cases. A deliberate stepwise approach to performance and interpretation of preoperative FT imaging, which consists of the modalities and protocols described here, is essential to seamless integration of the multidisciplinary FT team. ^©RSNA, 2019 See discussion on this article by Lincoln .

BACKGROUND AND PURPOSE/OBJECTIVE:The effects of multiple head impacts, even without detectable primary injury, on subsequent behavioral impairment and structural abnormality is yet well explored. Our aim was to uncover the dynamic changes and long-term effects of single and repetitive head injury without focal contusion on tissue microstructure and macrostructure. MATERIALS AND METHODS/METHODS:tests were used to evaluate the outcome change after injury and the cumulative effects of impact load, respectively. RESULTS:< .05) in neurologic outcome, balance, and locomotor activity were also aggravated after double injury. Histologic analysis showed astrogliosis 24 hours after injury, which persisted throughout the study period. CONCLUSIONS:There are measurable and dynamic changes in microstructure, cortical volume, behavior, and histopathology after both single and double injury, with more severe effects seen after double injury. This work bridges cross-sectional evidence from human subject and pathologic studies using animal models with a multi-time point, longitudinal research paradigm.

OBJECTIVE:Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION/CONCLUSIONS:We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background

We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications