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

• Published in: Magnetic resonance in medicine Vol. 79; no. 4; pp. 2415 - 2421
• Main Authors: Jafari, Ramin, Chhabra, Shalini, Prince, Martin R., Wang, Yi, Spincemaille, Pascal
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.04.2018
• Subjects: bolus arrival time; Computer simulation; Delay; dynamic contrast‐enhanced MRI; Frames per second; hepatic lesion perfusion analysis; Least squares; linear and nonlinear least squares fitting; linear inversion; Liver; Magnetic resonance; Mathematical models; Perfusion; Temporal resolution; bolus arrival time; linear inversion; linear and nonlinear least squares fitting; dynamic contrast-enhanced MRI; hepatic lesion perfusion analysis
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.26888

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.