Meeting Banner
Abstract #1595

Multicompartment modelling of diffusion-weighted MRI data with no prior assumptions

Emma Metcalfe-Smith1,2,3, Niloufar Zarinabad2,3, Jan Novak2,3, Hamid Dehghani1,4, and Andrew Peet2,3

1Physical Sciences for Health Doctoral Training Centre, University of Birmingham, Birmingham, United Kingdom, 2Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 3Department of Oncology, Birmingham Children's Hospital, Birmingham, United Kingdom, 4School of Computer Science, Birmingham, United Kingdom

Multi-compartment modelling of Diffusion-Weighted MRI data can provide additional diffusion related parameters. However, to ensure meaningful parameters are attained, multi-compartment models have to make several assumptions prior to fitting, including initial parameter values and multi-step fitting procedures. The novel Autoregressive Discrete Acquisition Points Transformation (ADAPT) method was applied to in vivo data. ADAPT demonstrated that it could infer the number of compartments within the data. When 1- and 2-compartment ADAPT models were investigated, the ADAPT coefficients were found to correlate with the parameters attained by the Apparent Diffusion Coefficient (ADC) and the Intravoxel Incoherent Motion (IVIM) models.

This abstract and the presentation materials are available to members only; a login is required.

Join Here