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DEVELOPMENT OF BIOMARKERS OF SMALL VESSEL DISEASE IN AGING
Title
DEVELOPMENT OF BIOMARKERS OF SMALL VESSEL DISEASE IN AGING
Creator
Makkinejad, Nazanin
Date
2021
Description
Age-related neuropathologies including cerebrovascular and neurodegenerative diseases play a critical role in cognitive dysfunction, and...
Show moreAge-related neuropathologies including cerebrovascular and neurodegenerative diseases play a critical role in cognitive dysfunction, and development of dementia. Designing methodologies for early prediction of these diseases are much needed. Since multiple pathologies commonly coexist in brains of older adults, clinical diagnosis lacks the specificity to isolate the pathology of interest, and gold standard is determined only at autopsy. Magnetic resonance imaging (MRI) provides a non-invasive tool to study abnormalities in brain characteristics that is unique to each pathology. Utilizing ex-vivo MRI for brain imaging proves to be useful as it eliminates two important biases of in-vivo MRI. First, no additional pathology would develop between imaging and pathologic examination, and second, frail older adults would not be excluded from MRI.Hence, the aims of this dissertation were two-fold: to study brain correlates of age- related neuropathologies, and to develop and validate classifiers of small vessel diseases by combining ex-vivo MRI and pathology in a large community cohort of older adults. The structure of the project is as follows.First, the association of amygdala volume and shape with transactive response DNA-binding protein 43 (TDP-43) pathology was investigated. Using a regularized regression technique, higher TDP-43 was associated with lower amygdala volume. Also, shape analysis of amygdala showed unique patterns of spatial atrophy associated with TDP-43 independent of other pathologies. Lastly, using linear mixed effect models, amygdala volume was shown to explain an additional portion of variance in cognitive decline above and beyond what was explained by the neuropathologies and demographics.Second, the previous study was extended to analyze other subcortical regions including the hippocampus, thalamus, nucleus accumbens, caudate, and putamen, and was also conducted in a larger dataset. The results showed unique contribution of TDP-43, neurofibrillary tangles (hallmark characteristic of Alzheimer’s disease pathology), and atherosclerosis (a cerebrovascular pathology) to atrophy on the surface of subcortical structures. Understanding the independent effects of each pathology on volume and shape of different brain regions can form a basis for the development of classifiers of age-related neuropathologies.Third, an in-vivo classifier of arteriolosclerosis was developed and validated. Arteriolosclerosis is one of the main pathologies of small vessel disease, is associated with cognitive decline and dementia, and currently has no standard biomarker available. In this work, the classifier was developed ex-vivo using machine learning (ML) techniques and was then translated to in-vivo. The in-vivo classifier was packaged as a software called ARTS, which outputs a score that is the likelihood of arteriolosclerosis when the required input is given to the software. It was tested and validated in various cohorts and showed to have high performance in predicting the pathology. It was also shown that higher ARTS score was associated with greater cognitive decline in domains that are specific to small vessel disease.Fourth, motivated by current trends and superiority of deep learning (DL) techniques in classification tasks in computer vision and medical imaging, a preliminary study was designed to use DL for training an ex-vivo classifier of arteriolosclerosis. Specifically, convolutional neural networks (CNNs) were applied on 3 Tesla ex-vivo MR images directly without providing prior information of brain correlates of arteriolosclerosis. One interesting aspect of the results was that the network learnt that white matter hyperintense lesions contributed the most to classification of arteriolosclerosis. These results were encouraging, and more future work will exploit the capability of DL techniques alongside the traditional ML approaches for more automation and possibly better performance.Finally, a preliminary classifier of arteriolosclerosis and small vessel atherosclerosis was developed since the existence of both pathologies in brain have devastating effects on cognition. The methodology was similar to the one used for development of arteriolosclerosis classifier with minor differences. The classifier showed a good performance in-vivo, although the testing needs to be assessed in more cohorts.The comprehensive study of age-related neuropathologies and their contribution to abnormalities of subcortical brain structures offers a great potential to develop a biomarker of each pathology. Also, the finding that the MR-based classifier of arteriolosclerosis showed high performance in-vivo demonstrate the potential of ex-vivo studies for development of biomarkers that are precise (because they are based on autopsy, which is the gold standard) and are expected to work well in-vivo. The implications of this study include development of biomarkers that could potentially be used in refined participant selection and enhanced monitoring of the treatment response in clinical drug and prevention trials.
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RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Title
RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Creator
Mo, Fei
Date
2021
Description
Deep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine...
Show moreDeep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine fields, capable of complementing human operators in disease diagnosis and treatment but optimizing and streamlining medical image display. While incredibly powerful, images produced via Deep Learning or Artificial Intelligence should be analyzed critically in order to be cognizant of how the algorithms are producing the new image and what the new imagine is. One such opportunity arose in the form of a unique collaborative project: the technical development of an image assessment tool that would analyze outputs between DL-based and non DL-based Magnetic Resonance Imaging reconstruction methods.More specifically, we examine the operator input dependence of the existing reference method in terms of accuracy and precision performance, and subsequently propose a new metric approach that preserves the heuristics of the intended quantification, overcomes operator dependence, and provides a relative comparative scoring approach that may normalize for angular dependence of examined images. In chapter 2 of this thesis, we provide a background description pertaining to the two imaging science principles that yielded our proposed method description and study design. First, if treated naively, the examined linear measurement approach exhibits potential bias with respect to the coordinate lattice space of the examined image. Second, the examined DL-based image reconstruction methods used in this thesis warrants an elaborate and explicit description of the measured noise and signal present in the reconstructed images. This specific reconstruction approach employs an iterative scheme with an embedded DL-based substep or filter to which we are blinded. In chapters 3 and 4 of this thesis, the imaging and DL-based image reconstruction experiments are described. These experiments employ cardiac MRI datasets from multiple clinical centers. We first outline the clinical and technical background for this approach, and then examine the quality of DL-based reconstructed image sharpness by two alternative methods: 1) by employing the gold-standard method that addresses the lattice point irregularity using a ‘re-gridding’ method, and 2) by applying our novel proposed method inspired by radial MRI k-space sampling, which exploits the mathematical properties of uniform radial sampling to yield the target voxel counts in the ‘gridded’ polar coordinate system. This new measure of voxel counts is shown to overcome the limitation due to the operator-dependence for the conventional approach. Furthermore, we propose this metric as a relative and comparative index between two alternative reconstruction methods from the same MRI k-space.
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Retrospective Quantitative T1 Imaging to Examine Characteristics of Multiple Sclerosis Lesions
Title
Retrospective Quantitative T1 Imaging to Examine Characteristics of Multiple Sclerosis Lesions
Creator
Young, Griffin James
Date
2024
Description
Quantitative MRI plays an essential role in assessing tissue abnormality and diseaseprogression in multiple sclerosis (MS). Specifically, T1...
Show moreQuantitative MRI plays an essential role in assessing tissue abnormality and diseaseprogression in multiple sclerosis (MS). Specifically, T1 relaxometry is gaining popularity as elevated T1 values have been shown to correlate with increased inflammation, demyelination, and gliosis. The predominant issue is that relaxometry requires parametric mapping through advanced imaging techniques not commonly included in standard clinical protocols. This leaves an information gap in large clinical datasets from which quantitative mapping could have been performed. We introduce T1-REQUIRE, a retrospective T1 mapping method that approximates T1 values from a single T1-weighted MR image. This method has already been shown to be accurate within 10% of a clinically available reference standard in healthy controls but will be further validated in MS cohorts. We also further aim to determine T1-REQUIRE’s statistical significance as a unique biomarker for the assessment of MS lesions as they relate to clinical disability and disease burden. A 14-subject comparison between T1-REQUIRE maps derived from 3D T1 weighted turbo field echoes (3D T1w TFE) and an inversion-recovery fast field echo (IRFFE) revealed a whole-brain voxel-wise Pearson’s correlation of r = 0.89 (p < 0.001) and mean bias of 3.99%. In MS white matter lesions, r = 0.81, R2 = 0.65 (p < 0.001, N = 159), bias = 10.07%, and in normal appearing white matter (NAWM), r = 0.82, R 2 = 0.67 (p < 0.001), bias = 9.48%. Mean lesional T1-REQUIRE and MTR correlated significantly (r = -0.68, p < 0.001, N = 587) similar to previously published literature. Median lesional MTR correlated significantly with EDSS (rho = -0.34, p = 0.037), and lesional T1-REQUIRE exhibited xiii significant correlations with global brain tissue atrophy as measured by brain parenchymal fraction (BPF) (r = -0.41, p = 0.010, N = 38). Multivariate linear regressions between T1- REQUIRE NAWM provided meaningful statistical relationships with EDSS (β = 0.03, p = 0.027, N = 38), as well as did mean MTR values in the Thalamus (β = -0.27, p = 0.037, N = 38). A new spoiled gradient echo variation of T1-REQUIRE was assessed as a proof of concept in a small 5-subject MS cohort compared with IR-FFE T1 maps, with a whole brain voxel-wise correlation of r = 0.88, R2 = 0.77 (p < 0.001), and Bias = 0.19%. Lesional T1 comparisons reached a correlation of r = 0.75, R2 = 0.56 (p < 0.001, N = 42), and Bias = 10.81%. The significance of these findings means that there is the potential to provide supplementary quantitative information in clinical datasets where quantitative protocols were not implemented. Large MS data repositories previously only containing structural T1 weighted images now may be used in big data relaxometric studies with the potential to lead to new findings in newly uncovered datasets. Furthermore, T1-REQUIRE has the potential for immediate use in clinics where standard T1 mapping sequences aren’t able to be readily implemented.
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