An information theory model for optimizing quantitative magnetic resonance imaging acquisitions. (12th November 2020)

• Record Type: Journal Article
• Authors: Mitchell, Drew P; Hwang, Ken-Pin; Bankson, James A; Jason Stafford, R; Banerjee, Suchandrima; Takei, Naoyuki; Fuentes, David
• Is Part Of: Physics in medicine & biology. Volume 65:Number 22(2020:Nov.)
• Publication Date: 2020-11-12
• Subjects: image variability -- information theory -- mutual information -- optimization -- quantitative MRI; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/abb9f6
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.