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

Overview

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.

publication date

  • August 22, 2017

Research

keywords

  • Image Processing, Computer-Assisted
  • Liver
  • Liver Neoplasms

Identity

PubMed Central ID

  • PMC5811380

Scopus Document Identifier

  • 85027697145

Digital Object Identifier (DOI)

  • 10.1038/nm.1919

PubMed ID

  • 28833534

Additional Document Info

volume

  • 79

issue

  • 4