Optimizing functional parameter accuracy for breath-hold DCE-MRI of liver tumours

Phys Med Biol. 2009 Apr 7;54(7):2197-215. doi: 10.1088/0031-9155/54/7/023. Epub 2009 Mar 17.

Abstract

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is a valuable tool for assessing treatment response to novel cancer therapeutics. With appropriate data acquisition, quantitative functional parameter estimates can be obtained by fitting a model to the data. This research focuses on applying a dual-input single-compartment pharmacokinetic model to breath-hold DCE-MRI imaging of the liver. In this paper, the use of two breath-holds, providing greater temporal information, is compared with a single breath-hold approach. Computer simulations are used to assess the accuracy, precision and sensitivity to input function errors obtained for parameters estimated from the two imaging protocols. Data from ten patients were analysed to assess the noise statistics obtained from the two breath-hold protocols. The noise statistics were used with a pharmacokinetic liver model to simulate data, from which the estimation accuracy, precision and sensitivity for the two protocols were assessed. Data from the ten patients were also analysed, and the estimates were compared with literature values. This work demonstrates the feasibility of obtaining functional liver perfusion estimates over a 3D volume using a sequential breath-hold protocol. The simulation results show that the protocol consisting of two images per breath-hold is to be preferred as it requires identical patient co-operation, but provides parameter estimates that have superior accuracy and precision.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Contrast Media*
  • Humans
  • Liver Neoplasms / blood supply
  • Liver Neoplasms / diagnosis*
  • Liver Neoplasms / pathology
  • Liver Neoplasms / physiopathology*
  • Magnetic Resonance Imaging
  • Models, Biological
  • Respiration*
  • Sensitivity and Specificity

Substances

  • Contrast Media