Literature DB >> 28833534

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

Ramin Jafari1, Shalini Chhabra2, Martin R Prince2, Yi Wang1,2, Pascal Spincemaille2.   

Abstract

PURPOSE: To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver.
METHODS: We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time.
RESULTS: Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters.
CONCLUSIONS: Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018.
© 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  bolus arrival time; dynamic contrast-enhanced MRI; hepatic lesion perfusion analysis; linear and nonlinear least squares fitting; linear inversion

Mesh:

Substances:

Year:  2017        PMID: 28833534      PMCID: PMC5811380          DOI: 10.1002/mrm.26888

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


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