Work

Cramér–Rao Bound Optimized Subspace Reconstruction in Quantitative MRI

Year: 2024

Type: article

Abstract: Objective: We extend the traditional framework for estimating subspace bases in quantitative MRI that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps. Methods: CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression. Results: Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with minimal cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications. Conclusion: The proposed method permits subspace reconstruction with a more compact basis, thereby offering significant computational savings. Significance: Efficient subspace reconstruction facilitates the validation and translation of advanced quantitative MRI techniques, e.g., magnetization transfer and diffusion.

Source: IEEE Transactions on Biomedical Engineering

DOI: 10.1109/tbme.2024.3446763

Authors: Andrew Mao, Sebastian Flassbeck, Cem Gultekin, Jakob Assländer

Institutions: NYU Langone Health, New York University, Courant Institute of Mathematical Sciences

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green