Title | Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Jafari R, Chhabra S, Prince MR, Wang Y, Spincemaille P |
Journal | Magn Reson Med |
Volume | 79 |
Issue | 4 |
Pagination | 2415-2421 |
Date Published | 2018 04 |
ISSN | 1522-2594 |
Keywords | Algorithms, Computer Simulation, Contrast Media, Humans, Image Processing, Computer-Assisted, Kinetics, Least-Squares Analysis, Linear Models, Liver, Liver Neoplasms, Models, Theoretical, Perfusion, Reproducibility of Results |
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. |
DOI | 10.1002/mrm.26888 |
Alternate Journal | Magn Reson Med |
PubMed ID | 28833534 |
PubMed Central ID | PMC5811380 |
Grant List | R01 NS072370 / NS / NINDS NIH HHS / United States S10 OD021782 / OD / NIH HHS / United States R01 NS095562 / NS / NINDS NIH HHS / United States R01 NS090464 / NS / NINDS NIH HHS / United States R01 CA181566 / CA / NCI NIH HHS / United States |
Related Institute:
MRI Research Institute (MRIRI)