Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain

In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 ​mm or smaller but degrades at 2 ​mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.Published versio

Validation of dMRI techniques for mapping brain pathways

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 201-222).Diffusion magnetic resonance imaging (dMRI) tractography is the only non-invasive tool for studying the connectional architecture of the brain in vivo. By measuring the diffusion of water molecules dMRI provides unique information about white matter pathways and their integrity, making it an invaluable neuroimaging tool that has improved our understanding of the human brain and how it is affected by disease. A major roadblock to its acceptance into clinical practice has been the difficulty in assessing its anatomical accuracy and reliability. In fact, obtaining a map of brain pathways is a multi-step process with numerous variables, assumptions and approximations that can influence the veracity of the generated pathways. Validation is, thus, necessary and yet challenging because there is no gold standard which dMRI can be compared to, since the configuration of human brain connections is largely unknown. Which aspects of tractography processing have the greatest effect on its performance? How do mapping methods compare? Which one is the most anatomically accurate? We tackle these questions with a multi-modal approach that capitalizes on the complementary strengths of available validation strategies to probe dMRI performance on different scales and across a wide range of acquisition and analysis parameters. The outcome is a multi-layered validation of dMRI tractography that 1) quantifies dMRI tractography accuracy both on the level of brain connections and tissue microstructure; 2) highlights the strengths and weaknesses of different modeling and tractography approaches, offering guidance on the issues that need to be resolved to achieve a more accurate mapping of the human brain.by Giorgia Grisot.Ph. D.Ph.D. Harvard-MIT Program in Health Sciences and Technolog

Systemic inflammation in non-demented elderly human subjects: brain microstructure and cognition.

The purpose of this study was to test the hypothesis that higher levels of systemic inflammation in a community sample of non-demented subjects older than seventy years of age are associated with reduced diffusion anisotropy in brain white matter and lower cognition. Ninety-five older persons without dementia underwent detailed clinical and cognitive evaluation and magnetic resonance imaging, including diffusion tensor imaging. Systemic inflammation was assessed with a composite measure of commonly used circulating inflammatory markers (C-reactive protein and tumor necrosis factor-alpha). Tract-based spatial statistics analyses demonstrated that diffusion anisotropy in the body and isthmus of the corpus callosum was negatively correlated with the composite measure of systemic inflammation, controlling for demographic, clinical and radiologic factors. Visuospatial ability was negatively correlated with systemic inflammation, and diffusion anisotropy in the body and isthmus of the corpus callosum was shown to mediate this association. The findings of the present study suggest that higher levels of systemic inflammation may be associated with lower microstructural integrity in the corpus callosum of non-demented elderly individuals, and this may partially explain the finding of reduced higher-order visual cognition in aging

Association of cognitive performance with systemic inflammation and mean FA from the region of the corpus callosum that showed significant negative correlation of FA with inflammation in the voxel-wise TBSS analysis.

<p>The results from three types of linear regression models are shown. In the first two columns, the composite measure of systemic inflammation and mean FA are included in models separately. In the last column, the two measures are included in the same model. All linear regression models contain as covariates age, sex, level of education, history of hypertension, and use of antihypertensive medication at evaluation. The estimate (β), standard error (SE) and p-value (p) are reported for each case. Significant associations are in bold letters and the p-value is marked with a *.</p

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach

Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. (1)). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-40% (refs. (2,3)). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access(4,5). To address these limitations, there has been much recent interest in applying deep learning to mammography(6-18), and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. Here we present an annotation-efficient deep learning approach that (1) achieves state-of-the-art performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis (DBT; \u273D mammography\u27), (3) detects cancers in clinically negative prior mammograms of patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new \u27maximum suspicion projection\u27 (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide

Demographic and clinical characteristics.

<p>Demographic and clinical characteristics.</p

Regions of the white matter skeleton with a significant negative correlation between FA and the composite measure of systemic inflammation are shown in dark blue (controlling for age, sex, level of education, and presence of WMHs).

<p>(A) Axial (radiological convention), (B) sagittal (left to right) and (C) coronal (posterior to anterior) views are presented for better localization. In order to ensure high contrast, the same dark blue color is assigned to all voxels with p<0.05 after correction for multiple comparisons. Mean FA maps of the IIT Human Brain Atlas (v.3) (grayscale), and the corresponding white matter skeleton (green color) are shown in the background. No part of the white matter skeleton showed a significant positive correlation between FA and the composite measure of inflammation.</p

Results of probabilistic tractography in the HARDI template of the IIT Human Brain Atlas (v.3) for a seed region covering the mid-sagittal section of the corpus callosum cluster that showed significant negative correlation between FA and systemic inflammation.

<p>(A) Location of the seed used for probabilistic tractography, overlaid on the mid-sagittal slice of the mean T₁-weighted template of the atlas. (B–E) Three-dimensional renderings of track-density maps of fibers that cross through the body and isthmus of the corpus callosum, where FA was shown to be significantly negatively correlated with the composite measure of systemic inflammation. (B) View of the lateral side of the left hemisphere. (C) View of the lateral side of the right hemisphere. (D) View of the medial aspect of the right hemisphere. (E) View of the medial aspect of the left hemisphere.</p