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.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/mrm.30084 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Background: This study quantified tau binding in the brain with F-PI2620 PET using a non-invasive Image-Derived Input function(IDIF), derived using a new total-body EXPLORER PET/CT scanner (Spencer et al.,2021). Additionally, we explored how PET scan duration influences the quantification of kinetic parameters across brain regions of interest(ROIs) that are vulnerable in Alzheimer's Disease.
View Article and Find Full Text PDFPublic Health.
Alzheimers Dement
December 2024
National Center for Neurological Disorders, Shanghai, China.
Background: Whether lung function prospectively affects cognitive brain health independent of their overlapping factors remains largely unknown. This study aimed to investigate the longitudinal association between decreased lung function and cognitive brain health and to explore underlying biological and brain structural mechanisms.
Method: This population-based cohort included 43,1834 non-demented participants with spirometry from the UK Biobank.
Alzheimer's Imaging Consortium.
Alzheimers Dement
December 2024
University of California Davis, Davis, CA, USA.
Background: This study quantified tau binding in the brain with 18F-PI2620 PET using a non-invasive Image-Derived Input function(IDIF), derived using a new total-body EXPLORER PET/CT scanner (Spencer et al.,2021). Additionally, we explored how PET scan duration influences the quantification of kinetic parameters across brain regions of interest(ROIs) that are vulnerable in Alzheimer's Disease.
View Article and Find Full Text PDFSimultaneous Concentration and T Mapping of Brain Metabolites by Fast Multi-Echo Spectroscopic Imaging.
NMR Biomed
February 2025
MR Methodology, Department for Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland.
The purpose of this study was to produce metabolite-specific T and concentration maps in a clinically compatible time frame. A multi-TE 2D MR spectroscopic imaging (MRSI) experiment (multi-echo single-shot MRSI [MESS-MRSI]) deployed truncated and partially sampled multi-echo trains from single scans and was combined with simultaneous multiparametric model fitting. It was tested in vivo for the brain in five healthy subjects.
View Article and Find Full Text PDFImpact of Species and Tissue Differences on In Vitro Glucuronidation of Diclofenac.
Molecules
December 2024
Department of Pharmaceutical Science, College of Pharmacy and Health Sciences, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004, USA.
Background: The aim of this study is to determine the impact of species and tissue differences on the glucuronidation of diclofenac in vitro.
Method: Microsomes from different species (rat, monkey, mouse, dog, and human) and rat and human tissues (liver, intestine, and kidney) were used to assess the rate of glucuronidation reaction of diclofenac. The metabolites were quantified using ultra high-performance liquid chromatography (UHPLC) and fitted into a Michaelis-Menten model to determine the metabolic kinetic parameters.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!