An Investigation of Deep Learning Methods to Shorten GABA-edited Magnetic Resonance Spectroscopy Scan Times

Pommot Berto, Rodrigo

An Investigation of Deep Learning Methods to Shorten GABA-edited Magnetic Resonance Spectroscopy Scan Times

dc.contributor.advisor	Medeiros de Souza, Roberto
dc.contributor.advisor	Harris, Ashley
dc.contributor.author	Pommot Berto, Rodrigo
dc.contributor.committeemember	Lebel, Marc
dc.contributor.committeemember	Zhang, Yunyan
dc.contributor.committeemember	Frayne, Richard
dc.contributor.committeemember	McCreary, Cheryl
dc.date.accessioned	2023-09-26T19:38:46Z
dc.date.available	2023-09-26T19:38:46Z
dc.date.issued	2023-09-18
dc.description.abstract	Edited magnetic resonance spectroscopy (MRS) can provide localized information on gamma-aminobutyric acid (GABA) concentration in vivo. However, GABA-edited MRS data has a low spectral quality, and many measurements, known as transients, need to be collected and averaged to obtain a high-quality spectrum, resulting in long scan times. This work investigated using deep learning (DL) with only a quarter of the number of conventionally acquired transients to shorten scan times by four while maintaining or improving spectral quality. A proof of concept was demonstrated by reconstructing GABA-edited spectra with only 80 transients and different configurations of DL-based pipelines. The best-performing pipeline used a proposed dimension-reducing 2D U-NET variation and it obtained better spectral quality metrics than conventionally reconstructed spectra with 320 transients. Simulated data was also shown to be useful in pre-training DL model weights. An open data challenge for reconstructing GABA-edited spectra from reduced transients was organized, and various DL models from different participating teams were compared. The challenge results reinforced the proof of concept conclusions that higher spectral quality can be achieved with DL reconstructions. However, the challenge metric evaluation also showed that DL models are able to undesirably exploit the limitations of conventional MRS metrics when using those as the training loss for the models, leading to good metric values but poor reconstructed spectra quality. DL reconstructions of GABA-edited MRS with 80 transients were also quantified and had significant differences from results from conventional reconstructions with 320 transients. However, given the lack of ground truths in the quantified data, it is not possible to conclude which results are closer to the actual concentrations. This work showed that DL methods can reduce GABA-edited MRS scan times while increasing spectral quality. Due to the lack of ground truths for the in vivo data, further studies are necessary to validate the concentrations obtained from the DL-based GABA-edited MRS reconstructions in comparison to conventional methods. This work was developed in the spirit of open science, and the data and code to reproduce the results were made publicly available.
dc.identifier.citation	Pommot Berto, R. (2023). An investigation of deep learning methods to shorten GABA-edited magnetic resonance spectroscopy scan times (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117198
dc.identifier.uri	https://doi.org/10.11575/PRISM/42040
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Magnetic Resonance Spectroscopy
dc.subject	Deep Learning
dc.subject	Edited-MRS Preprocessing
dc.subject	GABA
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Engineering--Biomedical
dc.title	An Investigation of Deep Learning Methods to Shorten GABA-edited Magnetic Resonance Spectroscopy Scan Times
dc.type	master thesis
thesis.degree.discipline	Engineering – Biomedical
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.