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

• Published in: NeuroImage (Orlando, Fla.) Vol. 292; p. 120601
• Main Authors: Finkelstein, Alan J., Liao, Congyu, Cao, Xiaozhi, Mani, Merry, Schifitto, Giovanni, Zhong, Jianhui
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2024; Elsevier Limited; Elsevier
• Subjects: Adult; Anisotropy; Blood; Blood vessels; Brain - diagnostic imaging; Contamination; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; Dictionaries; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Diffusion MRI; Female; Fourier transforms; Humans; Image Processing, Computer-Assisted - methods; Intravoxel incoherent motion; low-rank modeling; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Methods; Microvasculature; Motion; Neuroimaging; Perfusion; Phase variations; Quantitative mri; SARS-CoV-2; SARS-CoV-2 Infection; Severe acute respiratory syndrome coronavirus 2; Subspace modeling; Substantia grisea; Subspace modeling; Diffusion MRI; SARS-CoV-2 Infection; Quantitative mri; Intravoxel incoherent motion; low-rank modeling
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120601

•Incorporation of advanced reconstruction techniques, including correction of motion-induced phase errors, locally low-rank constraint, and subspace modeling, preserved anisotropic IVIM parameters.•Elimination of motion-induced phase errors enabled the use of high SNR real-valued DWI data, removing the Rician noise bias.•The proposed method preserved neuropathological findings, such as white matter hyperintensities, enabling quantitative evaluation of WMHs. This was not observed with conventionally reconstructed magnitude data.•The proposed method was better able to identify microvascular differences between individuals with a history of SARS-CoV-2 infection and those without. 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. 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. 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. 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. [Display omitted]