Magnetic resonance imaging (MRI) is a high-resolution, non-invasive medical imaging modality that is widely used in human brain. In recent years, susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) have been proposed to utilize MR phase signal to generate contrast from tissue magnetic susceptibility and even quantify the property. On the other hand, deep learning, especially deep convolutional neural networks (DCNNs), have achieved state-of-the-art performances in numerous computer vision tasks and gained significant attention in the field of medical imaging in the recent years. This dissertation combined the idea of deep learning with the two MR phase imaging methods.

To combined deep learning with SWI, we designed and trained a 3D deep residual network that can distinguish false positive detected candidates from cerebral microbleeds (CMBs) and built an automatic CMB detection pipeline with high performance. We further confirmed the generalizability of this deep learning-based pipeline using multiple dataset with different scan parameters and pathologies and provided lessons for application and generalization of generic deep learning based medical imaging methods.

To combine deep learning with QSM, we developed a 3D U-Net based network that learns to perform dipole inversion from gold standard QSM acquired from data with multiple orientation. The model was further improved with adversarial training strategy and achieved significantly lower reconstruction error than traditional QSM algorithms. In addition, we also performed various background removal and dipole inversion algorithms on both brain tumor patients and healthy volunteers to study and compare their performances. The results could provide guidance on future application of QSM in different scenarios.

Glioma is a heterogeneous and incurable neoplastic mass derived from the glial cells in the brain. The clinical course of a glial tumor can range from slow growing to highly aggressive and it is driven by the genetic and epigenetic alterations within the neoplastic cells’ DNA. In order to diagnose both that a patient has a glioma and what kind of glioma it may be, it is necessary to acquire magnetic resonance images (MRI) of the brain. MRI not only provides unparalleled soft tissue contrast in the brain compared with other tomographic imaging techniques (e.g. CT), it is also incredibly flexible: in addition to structural anatomy of the lesion, it can probe the physiology (e.g. diffusion, perfusion) and metabolism of the brain. Together, these data guide neuroradiologists and neurooncologists to provide the optimal treatment plan for a patient with glioma. Even with the most talented clinicians and the highest-quality MR acquisitions, there still exist issues along the trajectory of diagnosing a glioma. In this dissertation, I harnessed the incredibly rich biological information within the pixels of MRI with modern advances in data science and computer vision to address three of the most urgent problems along the diagnostic pipeline: 1) Can we identify a patient’s glioma subtype prior to surgical intervention?; 2) Can we create an automatic MR dashboard for clinicians to monitor patient disease over time?; and 3) When a patient appears to recur, is it a true glioma or the effect of radiation therapy?

The use of radiation therapy for treatment of brain tumor is controversial due to its long-term effects on neurocognitive function, especially in pediatric patients. Developing objective criteria for evaluating the severity of radiation-related injury is critical for children, due to their longer survival times and impact on early cognitive development. Cerebral microbleeds (CMB) which are deposits of hemosiderin that initially accumulate around vessels and can appear as early as 6 months post radiation therapy and continue to increase in number over time, however their vascular etiology is unknown. The aim of this project is to develop a method for simultaneous visualization of arteries, veins, and CMBs in order to automatically calculate vascular metrics from the fusion of MRA and SWI images obtained from a multi echo sequence at 7 Tesla. A strategy to assess the distribution of CMB’s relative to surrounding arteries and veins would help establish a connection between CMB formation and underlying vascular pathology. The tools developed in this study will be evaluated in pediatric patients with CMBs who have been treated with uniform supratentorial cranial radiation therapy for childhood brain tumors in order to ultimately determine whether metrics describing vascular structure could serve as quantitative markers for radiation injury, help in identifying regions of the brain that are most susceptible to radiation, and predict the formation of new CMBs and subsequent cognitive impairment.

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