Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue

Research output: Contribution to journalArticle

External organisations

  • Department of Oncology, Birmingham Children's Hospital
  • Department of Oncology, Birmingham Children's Hospital
  • Department of Oncology, Birmingham Children’s Hospital
  • Department of Oncology, Birmingham Children’s Hospital
  • Department of Oncology, Birmingham Children’s Hospital
  • University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

Abstract

PURPOSE: To investigate the robustness of constrained and simultaneous intravoxel incoherent motion (IVIM) fitting methods and the estimated IVIM parameters (D, D* and f) for applications in brain and low-perfused tissues.

MATERIALS AND METHODS: Model data simulations relevant to brain and low-perfused tumor tissues were computed to assess the accuracy, relative bias, and reproducibility (CV%) of the fitting methods in estimating the IVIM parameters. The simulations were performed at a series of signal-to-noise ratio (SNR) levels to assess the influence of noise on the fitting.

RESULTS: The estimated IVIM parameters from model simulations were found significantly different (P < 0.05) using simultaneous and constrained fitting methods at low SNR. Higher accuracy and reproducibility were achieved with the constrained fitting method. Using this method, the mean error (%) for the estimated IVIM parameters at a clinically relevant SNR = 40 were D 0.35, D* 41.0 and f 4.55 for the tumor model and D 1.87, D* 2.48, and f 7.49 for the gray matter model. The most robust parameters were the IVIM-D and IVIM-f. The IVIM-D* was increasingly overestimated at low perfusion.

CONCLUSION: A constrained IVIM fitting method provides more accurate and reproducible IVIM parameters in low-perfused tissue compared with simultaneous fitting. J. Magn. Reson. Imaging 2016.

Details

Original languageEnglish
Pages (from-to)1325–1334
Number of pages23
JournalJournal of Magnetic Resonance Imaging
Volume45
Issue number5
Early online date22 Aug 2016
Publication statusPublished - 18 Apr 2017