10.25394/PGS.8046698.v1 Ikbeom Jang Ikbeom Jang Diffusion Tensor Imaging Analysis for Subconcussive Trauma in Football and Convolutional Neural Network-Based Image Quality Control That Does Not Require a Big Dataset Purdue University Graduate School 2019 Diffusion Tensor Imaging Traumatic Brain Injury Subconcussive Injury Diffusion-Weighted Imaging Magnetic Resonance Imaging Image Quality Assessment Quality Control Convolutional Neural Network Transfer Learning Football Sport Concussion Quality Assurance Biomarkers Central Nervous System Diseases Health Care Artificial Intelligence and Image Processing Health Informatics Image Processing Pattern Recognition and Data Mining Neuroscience Neuroscience and Physiological Psychology 2019-05-14 15:27:44 Thesis https://hammer.purdue.edu/articles/thesis/Diffusion_Tensor_Imaging_Analysis_for_Subconcussive_Trauma_in_Football_and_Convolutional_Neural_Network-Based_Image_Quality_Control_That_Does_Not_Require_a_Big_Dataset/8046698 Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI)-based technique that has frequently been used for the identification of brain biomarkers of neurodevelopmental and neurodegenerative disorders because of its ability to assess the structural organization of brain tissue. In this work, I present (1) preclinical findings of a longitudinal DTI study that investigated asymptomatic high school football athletes who experienced repetitive head impact and (2) an automated pipeline for assessing the quality of DTI images that uses a convolutional neural network (CNN) and transfer learning. The first section addresses the effects of repetitive subconcussive head trauma on the white matter of adolescent brains. Significant concerns exist regarding sub-concussive injury in football since many studies have reported that repetitive blows to the head may change the microstructure of white matter. This is more problematic in youth-aged athletes whose white matter is still developing. Using DTI and head impact monitoring sensors, regions of significantly altered white matter were identified and within-season effects of impact exposure were characterized by identifying the volume of regions showing significant changes for each individual. The second section presents a novel pipeline for DTI quality control (QC). The complex nature and long acquisition time associated with DTI make it susceptible to artifacts that often result in inferior diagnostic image quality. We propose an automated QC algorithm based on a deep convolutional neural network (DCNN). Adaptation of transfer learning makes it possible to train a DCNN with a relatively small dataset in a short time. The QA algorithm detects not only motion- or gradient-related artifacts, but also various erroneous acquisitions, including images with regional signal loss or those that have been incorrectly imaged or reconstructed.