Faculty Bibliography

Myelin insulates neuronal axons and enables fast signal transmission, constituting a key component of brain development, aging and disease. Yet, myelin-specific imaging of macroscopic samples remains a challenge. Here, we exploit myelin's nanostructural periodicity, and use small-angle X-ray scattering tensor tomography (SAXS-TT) to simultaneously quantify myelin levels, nanostructural integrity and axon orientations in nervous tissue. Proof-of-principle is demonstrated in whole mouse brain, mouse spinal cord and human white and gray matter samples. Outcomes are validated by 2D/3D histology and compared to MRI measurements sensitive to myelin and axon orientations. Specificity to nanostructure is exemplified by concomitantly imaging different myelin types with distinct periodicities. Finally, we illustrate the method's sensitivity towards myelin-related diseases by quantifying myelin alterations in dysmyelinated mouse brain. This non-destructive, stain-free molecular imaging approach enables quantitative studies of myelination within and across samples during development, aging, disease and treatment, and is applicable to other ordered biomolecules or nanostructures.

Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty-one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN-based and manual labels. A leave-one-out cross validation scheme was used. CNN-based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN-based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN-based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN-based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub-mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.

Recurrent episodes of neurological dysfunction and white matter lesions in a young adult raise suspicion for multiple sclerosis (MS). However, occlusive retinopathy, hearing loss and absence of CSF oligoclonal bands are atypical for MS and should make the clinician consider an alternative diagnosis. We describe a man with hearing loss, visual signs and symptoms, and an accumulating burden of brain lesions, who was treated for a clinical diagnosis of MS for nearly two decades. Genetic testing revealed a unifying diagnosis.

Background Functional MRI improves preoperative planning in patients with brain tumors, but task-correlated signal intensity changes are only 2%-3% above baseline. This makes accurate functional mapping challenging. Marchenko-Pastur principal component analysis (MP-PCA) provides a novel strategy to separate functional MRI signal from noise without requiring user input or prior data representation. Purpose To determine whether MP-PCA denoising improves activation magnitude for task-based functional MRI language mapping in patients with brain tumors. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant study, MP-PCA performance was first evaluated by using simulated functional MRI data with a known ground truth. Right-handed, left-language-dominant patients with brain tumors who successfully performed verb generation, sentence completion, and finger tapping functional MRI tasks were retrospectively identified between January 2017 and August 2018. On the group level, for each task, histograms of z scores for original and MP-PCA denoised data were extracted from relevant regions and contralateral homologs were seeded by a neuroradiologist blinded to functional MRI findings. Z scores were compared with paired two-sided t tests, and distributions were compared with effect size measurements and the Kolmogorov-Smirnov test. The number of voxels with a z score greater than 3 was used to measure task sensitivity relative to task duration. Results Twenty-three patients (mean age ± standard deviation, 43 years ± 18; 13 women) were evaluated. MP-PCA denoising led to a higher median z score of task-based functional MRI voxel activation in left hemisphere cortical regions for verb generation (from 3.8 ± 1.0 to 4.5 ± 1.4; P < .001), sentence completion (from 3.7 ± 1.0 to 4.3 ± 1.4; P < .001), and finger tapping (from 6.9 ± 2.4 to 7.9 ± 2.9; P < .001). Median z scores did not improve in contralateral homolog regions for verb generation (from -2.7 ± 0.54 to -2.5 ± 0.40; P = .90), sentence completion (from -2.3 ± 0.21 to -2.4 ± 0.37; P = .39), or finger tapping (from -2.3 ± 1.20 to -2.7 ± 1.40; P = .07). Individual functional MRI task durations could be truncated by at least 40% after MP-PCA without degradation of clinically relevant correlations between functional cortex and functional MRI tasks. Conclusion Denoising with Marchenko-Pastur principal component analysis led to higher task correlations in relevant cortical regions during functional MRI language mapping in patients with brain tumors. © RSNA, 2020 Online supplemental material is available for this article.

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the evaluation, fine-tuning and application of anatomically-guided PET reconstruction in real-world clinical settings. In this work, we demonstrate that anatomically-guided PET reconstruction using the asymmetric Bowsher prior can be well-approximated by a purely shift-invariant convolutional neural network in image space allowing the generation of anatomically-guided PET images in almost real-time. We show that by applying dedicated data augmentation techniques in the training phase, in which 16 [¹⁸F]FDG and 10 [¹⁸F]PE2I data sets were used, lead to a CNN that is robust against the used PET tracer, the noise level of the input PET images and the input MRI contrast. A detailed analysis of our CNN in 36 [¹⁸F]FDG, 18 [¹⁸F]PE2I, and 7 [¹⁸F]FET test data sets demonstrates that the image quality of our trained CNN is very close to the one of the target reconstructions in terms of regional mean recovery and regional structural similarity.

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.

Beta amyloid (Aβ) accumulation is the earliest pathological marker of Alzheimer's disease (AD), but early AD pathology also affects white matter (WM) integrity. We performed a cross-sectional study including 44 subjects (23 healthy controls and 21 mild cognitive impairment or early AD patients) who underwent simultaneous PET-MR using 18F-Florbetapir, and were categorized into 3 groups based on Aβ burden: Aβ- [mean mSUVr ≤1.00], Aβi [1.00 < mSUVr <1.17], Aβ+ [mSUVr ≥1.17]. Intergroup comparisons of diffusion MRI metrics revealed significant differences across multiple WM tracts. Aβi group displayed more restricted diffusion (higher fractional anisotropy, radial kurtosis, axonal water fraction, and lower radial diffusivity) than both Aβ- and Aβ+ groups. This nonmonotonic trend was confirmed by significant continuous correlations between mSUVr and diffusion metrics going in opposite direction for 2 cohorts: pooled Aβ-/Aβi and pooled Aβi/Aβ+. The transient period of increased diffusion restriction may be due to inflammation that accompanies rising Aβ burden. In the later stages of Aβ accumulation, neurodegeneration is the predominant factor affecting diffusion.

BACKGROUND AND PURPOSE/OBJECTIVE:The brain stem is a complex configuration of small nuclei and pathways for motor, sensory, and autonomic control that are essential for life, yet internal brain stem anatomy is difficult to characterize in living subjects. We hypothesized that the 3D fast gray matter acquisition T1 inversion recovery sequence, which uses a short inversion time to suppress signal from white matter, could improve contrast resolution of brain stem pathways and nuclei with 3T MR imaging. MATERIALS AND METHODS/METHODS:-space to reduce motion; total scan time = 58 minutes). One subject returned for an additional 5-average study that was combined with a previous session to create a highest quality atlas for anatomic assignments. A 1-mm isotropic resolution, 12-minute version, proved successful in a patient with a prior infarct. RESULTS:The fast gray matter acquisition T1 inversion recovery sequence generated excellent contrast resolution of small brain stem pathways in all 3 planes for all 10 subjects. Several nuclei could be resolved directly by image contrast alone or indirectly located due to bordering visualized structures (eg, locus coeruleus and pedunculopontine nucleus). CONCLUSIONS:The fast gray matter acquisition T1 inversion recovery sequence has the potential to provide imaging correlates to clinical conditions that affect the brain stem, improve neurosurgical navigation, validate diffusion tractography of the brain stem, and generate a 3D atlas for automatic parcellation of specific brain stem structures.

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

Sudden unexplained death in childhood (SUDC) affects children >1-year-old whose cause of death remains unexplained following comprehensive case investigation and is often associated with hippocampal abnormalities. We prospectively performed systematic neuropathologic investigation in 20 SUDC cases, including (i) autopsy data and comprehensive ancillary testing, including molecular studies, (ii) ex vivo 3T MRI and extensive histologic brain samples, and (iii) blinded neuropathology review by 2 board-certified neuropathologists. There were 12 girls and 8 boys; median age at death was 33.3 months. Twelve had a history of febrile seizures, 85% died during apparent sleep and 80% in prone position. Molecular testing possibly explained 3 deaths and identified genetic mutations in TNNI3, RYR2, and multiple chromosomal aberrations. Hippocampal abnormalities most often affected the dentate gyrus (altered thickness, irregular configuration, and focal lack of granule cells), and had highest concordance between reviewers. Findings were identified with similar frequencies in cases with and without molecular findings. Number of seizures did not correlate with hippocampal findings. Hippocampal alterations were the most common finding on histological review but were also found in possibly explained deaths. The significance and specificity of hippocampal findings is unclear as they may result from seizures, contribute to seizure pathogenesis, or be an unrelated phenomenon.