Inverse Problem In Quantitative Susceptibility Mapping: Numerical And Machine Learning Approaches
| dc.contributor.author | Liu, Zhe | |
| dc.contributor.chair | Wang, Yi | |
| dc.contributor.committeeMember | Doerschuk, Peter | |
| dc.date.accessioned | 2019-10-15T15:32:54Z | |
| dc.date.available | 2019-10-15T15:32:54Z | |
| dc.date.issued | 2019-05-30 | |
| dc.description.abstract | Magnetic susceptibility reflects the concentration of bio-metal elements such as iron, calcium or gadolinium, providing means to investigate diseases such as multiple sclerosis, Alzheimer’s disease, hemorrhage and calcification. Numerous approaches have been proposed to provide magnetic susceptibility estimation from magnetic resonance imaging (MRI). While those methods are designed for specific body parts or pathologies, a unified framework is elusive from literature for reliable susceptibility estimation in both normal and pathological scenarios. This thesis developed algorithms that improve the accuracy, robustness and applicability of quantitative susceptibility mapping (QSM) for both healthy and pathological subjects. First, a dedicated regularized model was proposed to enable automated zero reference for QSM using cerebrospinal fluid. Second, convolutional neural network was combined with numerical optimization for superior anatomical contrast in QSM reconstruction. Finally, a total field inversion approach was presented to enable QSM for both healthy subject and hemorrhage patient. With the technical advances in this thesis, QSM requires less manual effort in susceptibility quantification, admits detailed recovery of anatomical structures and applies to both healthy subject and patient via a unified framework. | |
| dc.identifier.doi | https://doi.org/10.7298/k08a-nm52 | |
| dc.identifier.other | Liu_cornellgrad_0058F_11290 | |
| dc.identifier.other | http://dissertations.umi.com/cornellgrad:11290 | |
| dc.identifier.other | bibid: 11050460 | |
| dc.identifier.uri | https://hdl.handle.net/1813/67478 | |
| dc.language.iso | en_US | |
| dc.subject | Medical imaging | |
| dc.subject | Mathematics | |
| dc.subject | MRI | |
| dc.subject | QSM | |
| dc.subject | Computer science | |
| dc.subject | preconditioning | |
| dc.subject | machine learning | |
| dc.title | Inverse Problem In Quantitative Susceptibility Mapping: Numerical And Machine Learning Approaches | |
| dc.type | dissertation or thesis | |
| dcterms.license | https://hdl.handle.net/1813/59810 | |
| thesis.degree.discipline | Biomedical Engineering | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph.D., Biomedical Engineering |
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