Faculty Bibliography

The thalamus consists of several functionally distinct nuclei, some of which serve as targets for functional neurosurgery. Visualization of such nuclei is a major challenge due to their low signal contrast on conventional imaging. We introduce MR susceptibility imaging with a short TE, leveraging susceptibility differences among thalamic nuclei, to automatically delineate 15 thalamic subregions. The technique has the potential to enable direct targeting of thalamic nuclei for functional neurosurgical guidance.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.

Coronavirus disease 2019 (COVID-19) has been reported in association with a variety of brain imaging findings such as ischemic infarct, hemorrhage, and acute hemorrhagic necrotizing encephalopathy. Here, we report brain imaging features in 11 critically ill COVID-19 patients with persistently depressed mental status who underwent MRI between April 5-25, 2020 at our institution. These features include, 1) Confluent T2 hyperintensity and mild restricted diffusion in bilateral supratentorial deep and subcortical white matter (in 10 of 11 patients), and 2) multiple punctate microhemorrhages in juxtacortical and callosal white matter (in 7 of 11 patients). We also discuss potential pathogeneses.

INTRODUCTION/BACKGROUND:In addition to neurogenic orthostatic hypotension (nOH), patients with synucleinopathies frequently have hypertension when supine. The long-term consequences of both abnormalities are difficult to disentangle. We aimed to determine if supine hypertension is associated with target organ damage and worse survival in patients with nOH. METHODS:Patients with nOH due to multiple system atrophy (MSA), Parkinson disease (PD), or pure autonomic failure (PAF) were classified into those with or without supine hypertension (systolic BP of at least 140 mmHg or diastolic BP of at least 90 mmHg). Organ damage was assessed by measuring cerebral white matter hyperintensities (WMH), left ventricular hypertrophy (LVH), and renal function. We prospectively followed patients for 30 months (range: 12-66 months) and recorded incident cardiovascular events and all-cause mortality. RESULTS:Fifty-seven patients (35 with probable MSA, 14 with PD and 8 with PAF) completed all evaluations. In addition to nOH (average fall 35 ± 21/17 ± 14 mmHg, systolic/diastolic, mean ± SD), 38 patients (67%) had supine hypertension (systolic BP > 140 mmHg). Compared to those without hypertension, patients with hypertension had higher blood urea nitrogen levels (P = 0.005), lower estimated glomerular filtration rate (P = 0.008), higher prevalence of LVH (P = 0.040), and higher WMH volume (P = 0.019). Longitudinal follow-up of patients for over 2 years (27.1 ± 14.5 months) showed that supine hypertension was independently associated with earlier incidence of cardiovascular events and death (HR = 0.25; P = 0.039). CONCLUSIONS:Supine hypertension in patients with nOH was associated with an increased risk for target organ damage, cardiovascular events, and premature death. Defining management strategies and safe blood pressure ranges in patients with nOH remains an important research question.

A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.

Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturation and aging. Despite the availability of open-source brain segmentation software, widespread clinical adoption of volumetric analysis has been hindered due to processing times and reliance on manual corrections. Here, we extend the use of deep learning models from proof-of-concept, as previously reported, to present a comprehensive segmentation of cortical and deep gray matter brain structures matching the standard regions of aseg+ aparc included in the commonly used open-source tool, Freesurfer. The work presented here provides a real-life, rapid deep learning-based brain segmentation tool to enable clinical translation as well as research application of quantitative brain segmentation. The advantages of the presented tool include short (~ 1 minute) processing time and improved segmentation quality. This is the first study to perform quick and accurate segmentation of 102 brain regions based on the surface-based protocol (DMK protocol), widely used by experts in the field. This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model. We show the superior performance of our deep-learning-based models over the traditional segmentation tool, Freesurfer. We refer to the proposed deep learning-based tool as DARTS (DenseUnet-based Automatic Rapid Tool for brain Segmentation)

In this work, we propose bag of adversarial features (BAF) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRI) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MR images. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-word approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex matched healthy controls), and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions.

BACKGROUND:H MRS with the ability to separate tissue-type partial volume contribution(s). PURPOSE/OBJECTIVE:H MRSI voxel averaging is sensitive to regional WM metabolic abnormalities. STUDY TYPE/METHODS:Retrospective cross-sectional cohort study. POPULATION/METHODS:Twenty-seven subjects: 15 symptomatic mTBI patients, 12 matched controls. FIELD STRENGTH/SEQUENCE/UNASSIGNED:. ASSESSMENT/RESULTS:N-acetyl-aspartate (NAA), creatine, choline, and myo-inositol concentrations estimated in predominantly WM regions: body, genu, and splenium of the corpus callosum, corona radiata, frontal, and occipital WM. STATISTICAL TESTS/UNASSIGNED:Analysis of covariance (ANCOVA) to compare patients with controls in terms of regional concentrations. The effect sizes (Cohen's d) of the mean differences were compared across regions and with previously published global data obtained with linear regression of the WM over the entire VOI in the same dataset. RESULTS:Despite patients' global VOI WM NAA being significantly lower than the controls', no regional differences were observed for any metabolite. Regional NAA comparisons, however, were all unidirectional (patients' NAA concentrations < controls') within a narrow range: 0.3 ≤ Cohen's d ≤ 0.6. DATA CONCLUSION/UNASSIGNED:H MRS studies, given that these results are confirmed in other cohorts. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019.

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm² versus at b = 250 and 1000 s/mm². The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ = 0.35, p = 3*10^-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10^-4/year and 3.61*10^-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.