Dissertation_Apr26_4PM.pdf (3.67 MB)
Diffusion Tensor Imaging Analysis for Subconcussive Trauma in Football and Convolutional Neural Network-Based Image Quality Control That Does Not Require a Big Dataset
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.
History
Degree Type
- Doctor of Philosophy
Department
- Electrical and Computer Engineering
Campus location
- West Lafayette
Advisor/Supervisor/Committee Chair
Thomas M. TalavageAdditional Committee Member 2
Eric A. NaumanAdditional Committee Member 3
Edward J. DelpAdditional Committee Member 4
Michael D. ZoltowskiUsage metrics
Categories
- Medical biochemistry and metabolomics not elsewhere classified
- Central nervous system
- Epidemiology not elsewhere classified
- Health care administration
- Artificial intelligence not elsewhere classified
- Health informatics and information systems
- Image processing
- Pattern recognition
- Data mining and knowledge discovery
- Neurosciences not elsewhere classified
- Psychophysiology
Keywords
Diffusion Tensor ImagingTraumatic Brain InjurySubconcussive InjuryDiffusion-Weighted ImagingMagnetic Resonance ImagingImage Quality AssessmentQuality ControlConvolutional Neural NetworkTransfer LearningFootballSportConcussionQuality AssuranceBiomarkersCentral Nervous SystemDiseasesHealth CareArtificial Intelligence and Image ProcessingHealth InformaticsImage ProcessingPattern Recognition and Data MiningNeuroscienceNeuroscience and Physiological Psychology