Deep-learning-based Multi-visit Magnetic Resonance Imaging Reconstruction: Proof of Concept and Robustness Evaluation on a Cohort of Glioblastoma Patients
Date
2023-01-19
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Abstract
Magnetic Resonance (MR) imaging is a powerful imaging technique for assessing brain-related diseases. However, MR scans suffer from long acquisition times and as a consequence, patients in Canada must wait extensive periods for access to a scanning session. Compressed Sensing (CS) and Parallel Imaging (PI) are two proven techniques employed to enable accelerated acquisitions. However, they both require complex reconstruction algorithms which disable real-time results. The renewed advent of deep-learning has helped tackle this problem of long reconstruction times. But, currently deep-learning based reconstruction methods do not leverage the wealth of mutual information contained across multiple patient visits to the scanner. This led to the proposal of the Multi-visit Integration Model (MIM) which is a framework for reconstructing a follow-up scan, that has been aggressively undersampled, by leveraging a previous scan. This thesis aims to investigate the performance of the MIM when similarity is not guaranteed between the previous and follow-up scan, such as in the case of glioblastoma patients. The results demonstrated that the MIM leaves localized regions, which have undergone a structural change from one scan to the next, the same. However, this conservative behaviour is not demonstrated during our robustness analysis when synthetic lesions are added to the previous scan to simulate a structural change. The effect of the single-visit reconstruction model on the multi-visit reconstruction performance demonstrated that regardless of the model used, statistically significant improvements to reconstruction quality were observed after multi-visit integration. Multi-visit reconstruction produced using older scans compared to newer scans was found to be of lower quality but still did not introduce biases towards the previous time-point. Finally, the accumulation of system error when using a multi-visit reconstruction as a previous scan in the MIM was minimal. This investigation provided insight into the behaviour of multi-visit integration in the face of structural brain changes and paves the early road towards clinical adoption.
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Deep-learning
Citation
Beauferris, Y. (2023). Deep-learning-based multi-visit magnetic resonance imaging reconstruction: proof of concept and robustness evaluation on a cohort of glioblastoma patients (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.