Quantitative body DW-MRI biomarkers uncertainty estimation using unscented wild-bootstrap

M. Freiman, S. D. Voss, R. V. Mulkern, J. M. Perez-Rossello, S. K. Warfield

Research output: Contribution to journalConference articlepeer-review

Abstract

We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of ~36% in the uncertainty values.

Original languageEnglish
Pages (from-to)74-81
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
StatePublished - 2011
Externally publishedYes
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 18 Sep 201122 Sep 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Quantitative body DW-MRI biomarkers uncertainty estimation using unscented wild-bootstrap'. Together they form a unique fingerprint.

Cite this