Faculty Bibliography

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

BACKGROUND AND PURPOSE/OBJECTIVE:Na) MR imaging. MATERIALS AND METHODS/METHODS:Na) MR imaging using a 3T scanner. Total sodium concentration was measured in the genu, body, and splenium of the corpus callosum with 5-mm ROIs; total sodium concentration of the genu-to-splenium ratio was calculated and compared between patients and controls. RESULTS:= .001). CONCLUSIONS:Complex differences are seen in callosal total sodium concentration in symptomatic patients with mild traumatic brain injury, supporting the notion of ionic dysfunction in the pathogenesis of mild traumatic brain injury. The total sodium concentration appears to be altered beyond the immediate postinjury phase, and further work is needed to understand the relationship to persistent symptoms and outcome.