Model-based super-resolution reconstruction with joint motion estimation for improved quantitative MRI parameter mapping. (September 2022)

• Record Type: Journal Article
• Authors: Beirinckx, Quinten; Jeurissen, Ben; Nicastro, Michele; Poot, Dirk H.J.; Verhoye, Marleen; Dekker, Arnold J. den; Sijbers, Jan
• Is Part Of: Computerized medical imaging and graphics. Volume 100(2022)
• Publication Date: 2022-09
• Subjects: Bayesian estimation -- Model-based reconstruction -- Motion correction -- Quantitative magnetic resonance imaging -- Super-resolution; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102071
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method's performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI. Highlights: Joint estimation of quantitative MRI parameter maps and motion parameters. Bayesian estimation to exploit prior knowledge of the tissue and noise statistics. Validation and benchmarking using whole brain Monte Carlo simulations. In vivo evaluation for a whole brain T 1 and T 2 quantitative mapping protocol. Superior accuracy compared to quantitative MRI with motion pre-compensation.