Bioimaging and Biomedical Optics Commons^™

A Convolutional Neural Network Approach To Breast Cancer Tumor Boundary Detection, James Leah Matlosz

Master's Theses

Breast cancer is the second most common form of cancer and often goes undetected in its initial stages due to its subtle symptoms. As the tumors grow, they become more difficult to surgically remove with clean borders. To minimize the chance of recurrence, a 2D convolutional neural network is developed in this work for delineating tumor boundaries. Specifically, the U-shaped network model is trained, validated, and tested with longitudinal MRI scans of patients diagnosed with breast cancer and undergoing neoadjuvant therapy. For training, image masks were generated as ground truths using signal enhancement ratio segmentation, thresholding, and contour detection. The …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

A Radiofrequency Coil To Facilitate Task-Based Fmri Of Awake Marmosets, Kyle Gilbert, Audrey Dureux, Azadeh Jafari, Alessandro Zanini, Peter Zeman, Ravi S. Menon, Stefan Everling

Robarts Imaging Publications

Background: The small common marmoset (Callithrix jacchus) is an ideal nonhuman primate for awake fMRI in ultra-high field small animal MRI scanners. However, it can often be challenging in task-based fMRI experiments to provide a robust stimulus within the MRI environment while using hardware (an RF coil and restraint system) that is compatible with awake imaging. New Method: Here we present an RF coil and restraint system that permits unimpeded access to an awake marmoset’s head subsequent to immobilization, thereby permitting the setup of peripheral devices and stimuli proximal to the head.

Results: As an example application, an …

Water Exchange Rate Across The Blood-Brain Barrier Is Associated With Csf Amyloid-Β 42 In Healthy Older Adults, Brian T. Gold, Xingfeng Shao, Tiffany L. Sudduth, Gregory A. Jicha, Donna M. Wilcock, Elayna R. Seago, Danny J. J. Wang

Sanders-Brown Center on Aging Faculty Publications

INTRODUCTION: We tested if water exchange across the blood-brain barrier (BBB), estimated with a noninvasive magnetic resonance imaging (MRI) technique, is associated with cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) and neuropsychological function.

METHODS: Forty cognitively normal older adults (67–86 years old) were scanned with diffusion‐prepared, arterial spin labeling (DP‐ASL), which estimates water exchange rate across the BBB (k_w). Participants also underwent CSF draw and neuropsychological testing. Multiple linear regression models were run with kw as a predictor of CSF concentrations and neuropsychological scores.

RESULTS: In multiple brain regions, BBB kw was positively associated with CSF amyloid …

Neurobiological Markers For Remission And Persistence Of Childhood Attention-Deficit/Hyperactivity Disorder, Yuyang Luo

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation …

Speckle-Noise Reduction In Knee Articular Cartilage Ultrasound Image Using Anisotropic Diffusion, Ali Muhammad Shoaib

Student Works (2020-2029)

Knee arthritis is the most common type of arthritis which effects the people and may cause severe pain to the patient and can lead to joint effusion. Ultrasound (US) imaging is an appropriate and consistent substitute for other imaging techniques like magnetic resonance imaging or X-rays in the investigation or screening of knee injury. Nevertheless, one of the major problems in US images which make the analysis of these images hard is the presence of speckle noise. For the reduction of speckle noise, the performance of the anisotropic method is found much better over other approaches. In removing the speckle …

Review Of Methods For Intraoperative Margin Detection For Breast Conserving Surgery, Benjamin W. Maloney, David M. Mcclatchy, Brian W. Pogue, Keith D. Paulsen, Wendy A. Wells, Richard J. Barth

Dartmouth Scholarship

Breast conserving surgery (BCS) is an effective treatment for early-stage cancers as long as the margins of the resected tissue are free of disease according to consensus guidelines for patient management. However, 15% to 35% of patients undergo a second surgery since malignant cells are found close to or at the margins of the original resection specimen. This review highlights imaging approaches being investigated to reduce the rate of positive margins, and they are reviewed with the assumption that a new system would need high sensitivity near 95% and specificity near 85%. The problem appears to be twofold. The first …

Tissue Oxygen Saturation Predicts Response To Breast Cancer Neoadjuvant Chemotherapy Within 10 Days Of Treatment, Jeffrey M. Cochran, David R. Busch, Anais Leproux, Zheng Zhang, Thomas D. O'Sullivan, Albert E. Cerussi, Philip M. Carpenter, Rita S. Mehta, Darren Roblyer, Wei Yang, Keith D. Paulsen, Brian Pogue, Shudong Jiang, Peter A. Kaufman

Dartmouth Scholarship

Ideally, neoadjuvant chemotherapy (NAC) assessment should predict pathologic complete response (pCR), a surrogate clinical endpoint for 5-year survival, as early as possible during typical 3- to 6-month breast cancer treatments. We introduce and demonstrate an approach for predicting pCR within 10 days of initiating NAC. The method uses a bedside diffuse optical spectroscopic imaging (DOSI) technology and logistic regression modeling. Tumor and normal tissue physiological properties were measured longitudinally throughout the course of NAC in 33 patients enrolled in the American College of Radiology Imaging Network multicenter breast cancer DOSI trial (ACRIN-6691). An image analysis scheme, employing z-score normalization to …

Computational Modeling For Abnormal Brain Tissue Segmentation, Brain Tumor Tracking, And Grading, Syed Mohammad Shamin Reza

Electrical & Computer Engineering Theses & Dissertations

This dissertation proposes novel texture feature-based computational models for quantitative analysis of abnormal tissues in two neurological disorders: brain tumor and stroke. Brain tumors are the cells with uncontrolled growth in the brain tissues and one of the major causes of death due to cancer. On the other hand, brain strokes occur due to the sudden interruption of the blood supply which damages the normal brain tissues and frequently causes death or persistent disability. Clinical management of these brain tumors and stroke lesions critically depends on robust quantitative analysis using different imaging modalities including Magnetic Resonance (MR) and Digital Pathology …

Applications Of Novel Mri Technologies In Tissue Engineering And Disease Diagnosis, Vahid Khalilzad-Sharghi

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Magnetic resonance imaging (MRI) and magnetic resonance elastography (MRE) are increasingly under investigation to explore their potential role in establishing effective evaluation methods for the procedure of tissue regeneration carried out in vitro, in vivo, and in disease diagnosis. To this end, there is a continuous pursuit of novel tools both in vitro and in vivo. For instance, there is a great need for the development and evaluation of an MR-compatible incubation system that enables simultaneous monitoring and culturing of cell and tissue constructs using MRI techniques. Such an imagingcompatible incubation system eliminates exposing the culture to the risks of …

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …

Alterations In Multiple Measures Of White Matter Integrity In Normal Women At High Risk For Alzheimer's Disease, Brian T. Gold, David K. Powell, Anders H. Andersen, Charles D. Smith

Neuroscience Faculty Publications

There is evidence that disruption of white matter (WM) microstructure is an early event in the course of Alzheimer's disease (AD). However, the neurobiological bases of WM microstructural declines in presymptomatic AD are unknown. In the present study we address this issue using a multimodal imaging approach to the study of presymptomatic AD. Participants were 37 high-risk (both family history of dementia and one or more APOE4 alleles) women and 20 low-risk (neither family history nor APOE4) women. Groups were matched for age, education, neuropsychological performance, and vascular factors that could affect white matter. Whole-brain analyses of diffusion tensor imaging …

Statistical Hypothesis Testing For Postreconstructed And Postregistered Medical Images, Eugene Demidenko

Dartmouth Scholarship

Postreconstructed and postregistered medical images are typically treated as the raw data, implicitly assuming that those operations are error free. We question this assumption and explore how the precision of reconstruction and affine registration can be assessed by the image covariance matrix and confidence interval, called the confidence eigenimage, using a statistical model-based approach. Various hypotheses may be tested after image reconstruction and registration using classical statistical hypothesis testing vehicles: Is there a statistically significant difference between images? Does the intensity at a specific location or area of interest belong to the “normal” range? Is there a tumor? Does the …

Brain Tumor Progression Assessment Using Multiple Mri Volumes, Yufei Shen

Electrical & Computer Engineering Theses & Dissertations

Predicting and assessing tumor progression is important in brain tumor treatment. We attempt to use machine learning techniques to achieve consistency in assessing brain tumor progression. This thesis presents a prediction method of brain tumor progression by exploring a large MR database, which contains two patients ' complete records covering all their visits in the past two years. All ten MRI series, namely, apparent diffusion coefficient (ADC) , diffusion tensor imaging (DTI) , fractional anisotropy (FA), fluid attenuated inversion recovery (FLAIR), max eigenvalue (MAX), mid eigenvalue (MID), min eigenvalue (MIN) , post-contrast T₁-weighted, T₁- weighted, and …

Imaging Breast Adipose And Fibroglandular Tissue Molecular Signatures By Using Hybrid Mri-Guided Near-Infrared Spectral Tomography, Ben Brooksby, Brian W. Pogue, Shudong Jiang, Hamid Dehghani, Subhadra Srinivasan, Christine Kogel, Tor D. Tosteson, John Weaver, Steven P. Poplack, Keith D. Paulsen

Dartmouth Scholarship

Magnetic resonance (MR)-guided near-infrared spectral tomography was developed and used to image adipose and fibroglandular breast tissue of 11 normal female subjects, recruited under an institutional review board-approved protocol. Images of hemoglobin, oxygen saturation, water fraction, and subcellular scattering were reconstructed and show that fibroglandular fractions of both blood and water are higher than in adipose tissue. Variation in adipose and fibroglandular tissue composition between individuals was not significantly different across the scattered and dense breast categories. Combined MR and near-infrared tomography provides fundamental molecular information about these tissue types with resolution governed by MR T1 images.

Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj

Electrical & Computer Engineering Faculty Research

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …

Using Kriging To Interpolate Spatially Distributed Volumetric Medical Data, Stephen M. Matechik

Theses and Dissertations

Routine cases in diagnostic radiology require the interpolation of volumetric medical imaging data sets. Inaccurate renditions of interpolated volumes can lead to the misdiagnosis of a patient's condition. It is therefore essential that interpolated modality space estimates accurately portray patient space. Kriging is investigated in this research to interpolate medical imaging volumes. Kriging requires data to be spatially distributed. Therefore, magnetic resonance imaging (MRI) data is shown to exhibit spatially regionalized characteristics such that it can be modeled using regionalized variables and subsequently be interpolated using kriging. A comprehensive, automated, three-dimensional structural analysis of the MRI data is accomplished to …