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Abstract #1077

High-Fidelity Intravoxel Incoherent Motion (HIFIVIM) Parameter Mapping Using Locally Low-Rank with Temporal Subspace Constraint

Alan Finkelstein1, Congyu Liao2, Xiaozhi Cao2, Merry Mani3, Giovanni Schifitto4,5,6, and Jianhui Zhong1,5,7
1Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States, 2Department of Radiology, Stanford University, Stanford, CA, United States, 3University of Iowa, Iowa City, IA, United States, 4Department of Neurology, University of Rochester, Rochester, NY, United States, 5Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, 6Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States, 7Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States

Synopsis

Keywords: Image Reconstruction, Diffusion/other diffusion imaging techniques, Intravoxel Incoherent Motion, Model-Based Reconstruction, Subspace Reconstruction

Motivation: Intravoxel incoherent motion (IVIM) is a measure in MRI to quantify tissue perfusion. However, clinical applications are limited by noisy parameter estimates for the perfusion fraction (f) and pseudodiffusion coefficient (D*).

Goal(s): We sought to improve IVIM parameter estimation using a model-based reconstruction

Approach: We combined locally low-rank (LLR) and temporal subspace constraints to reliably perform joint reconstruction of IVIM images before fitting while correcting shot-to-shot phase variations between each b-value.

Results: Our method resulted in smoother signal decay curves before fitting and improved the estimation of IVIM parameter maps with less noise and fewer outliers.

Impact: A model-based reconstruction with low rank and temporal constraints improved IVIM image reconstruction, reducing noise and outliers in parameter estimates. Spline interpolation further facilitated reliable estimation of IVIM maps from just 5 b-values, benefiting clinical situations like stroke.

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Keywords