High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling

Alan J Finkelstein; Congyu Liao; Xiaozhi Cao; Merry Mani; Giovanni Schifitto; Jianhui Zhong

doi:10.1016/j.neuroimage.2024.120601

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling

Neuroimage. 2024 Apr 15:292:120601. doi: 10.1016/j.neuroimage.2024.120601. Epub 2024 Apr 7.

Authors

Alan J Finkelstein¹, Congyu Liao², Xiaozhi Cao², Merry Mani³, Giovanni Schifitto⁴, Jianhui Zhong⁵

Affiliations

¹ Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA.
² Department of Radiology, Stanford University, Stanford, CA, USA.
³ Department of Radiology, University of Iowa, Iowa City, IA, USA.
⁴ Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA; Department of Neurology, University of Rochester, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, Rochester, NY, USA.
⁵ Department of Biomedical Engineering, University of Rochester, Rochester, NY, USA; Department of Imaging Sciences, University of Rochester, Rochester, NY, USA; Department of Physics and Astronomy, University of Rochester, Rochester, NY, USA. Electronic address: Jianhui_Zhong@urmc.rochester.edu.

PMID: 38588832
DOI: 10.1016/j.neuroimage.2024.120601

Abstract

Purpose: Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods.

Methods: To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise.

Results: Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction.

Conclusion: The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

Keywords: Diffusion MRI; Intravoxel incoherent motion; Quantitative mri; SARS-CoV-2 Infection; Subspace modeling; low-rank modeling.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Adult
Brain / diagnostic imaging
COVID-19* / diagnostic imaging
Diffusion Magnetic Resonance Imaging / methods
Female
Humans
Image Processing, Computer-Assisted* / methods
Magnetic Resonance Imaging / methods
Male
Motion
SARS-CoV-2