Turco, Federico; Capiglioni, Milena; Weng, Guodong; Slotboom, Johannes (2024). TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets. (In Press). Magnetic resonance in medicine Wiley 10.1002/mrm.30084
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Magnetic_Resonance_in_Med_-_2024_-_Turco_-_TensorFit__A_torch_based_tool_for_ultrafast_metabolite_fitting_of_large_MRSI.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (5MB) | Preview |
PURPOSE
To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto-differentiation and optimization framework.
METHODS
TensorFit was implemented in Python based on Torch's auto-differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST.
RESULTS
TensorFit demonstrates a significant improvement in computation speed, achieving a 165-times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169-fold speedup.
CONCLUSION
TensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Turco, Federico, Capiglioni, Milena Sofia (A), Weng, Guodong, Slotboom, Johannes |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1522-2594 |
Publisher: |
Wiley |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
14 Mar 2024 07:54 |
Last Modified: |
15 Mar 2024 05:59 |
Publisher DOI: |
10.1002/mrm.30084 |
PubMed ID: |
38469890 |
Uncontrolled Keywords: |
3D MRSI GPU optimization deep learning frameworks metabolite fitting torch auto-differentiation |
BORIS DOI: |
10.48350/194181 |
URI: |
https://boris.unibe.ch/id/eprint/194181 |
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