Fast Voxelwise SNR Estimation for Iterative MRI Reconstructions
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
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2026
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| author | Dalmaz, Onat Abraham, Daniel Toews, Alexander R. Chaudhari, Akshay S. Setsompop, Kawin Hargreaves, Brian A. |
| author_facet | Dalmaz, Onat Abraham, Daniel Toews, Alexander R. Chaudhari, Akshay S. Setsompop, Kawin Hargreaves, Brian A. |
| contents | Purpose: To develop a fast, general-purpose framework for voxelwise noise characterization in linear and nonlinear iterative MRI reconstructions, recovering the image-domain noise variance from which SNR, $g$-factor, and related image-quality metrics are derived. The framework addresses both the intractability of closed-form formulas beyond Cartesian sampling and the long runtime of Pseudo Multiple Replica (PMR) methods.
Methods: We propose PICO (Probing Image-space COvariance), an estimator that operates in the image domain by probing the image-domain noise covariance operator -- or, for nonlinear compressed-sensing reconstructions, the Jacobian of the converged solution -- with random probe images. Complex random-phase probes are shown theoretically and empirically to minimize estimator variance compared with Gaussian or real-valued alternatives. PICO was validated against analytical benchmarks and high-replica PMR references using retrospective Cartesian knee data ($R=2$), prospective non-Cartesian spiral brain phantom data ($R=2,3,4$), and compressed-sensing knee reconstructions ($R=2$).
Results: In Cartesian experiments, PICO accurately reproduced analytical SENSE $g$-factor maps. In non-Cartesian spiral imaging ($R=2$), it achieved 1% estimation error in 64 s compared with 462 s for PMR (approximately 7.2x speedup), with the efficiency advantage persisting at higher acceleration. For nonlinear compressed sensing, the Jacobian-based estimator produced noise maps consistent with PMR while converging faster (52 s vs. 95 s; approximately 1.8x speedup).
Conclusion: PICO provides a computationally efficient alternative to PMR for voxelwise noise and $g$-factor estimation across generalized iterative MRI reconstructions. By reusing existing reconstruction primitives, it enables voxelwise noise maps to be produced as a routine by-product of the reconstruction pipeline. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_09590 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Voxelwise SNR Estimation for Iterative MRI Reconstructions Dalmaz, Onat Abraham, Daniel Toews, Alexander R. Chaudhari, Akshay S. Setsompop, Kawin Hargreaves, Brian A. Signal Processing 92C55, 65F10, 65C05, 94A12 Purpose: To develop a fast, general-purpose framework for voxelwise noise characterization in linear and nonlinear iterative MRI reconstructions, recovering the image-domain noise variance from which SNR, $g$-factor, and related image-quality metrics are derived. The framework addresses both the intractability of closed-form formulas beyond Cartesian sampling and the long runtime of Pseudo Multiple Replica (PMR) methods. Methods: We propose PICO (Probing Image-space COvariance), an estimator that operates in the image domain by probing the image-domain noise covariance operator -- or, for nonlinear compressed-sensing reconstructions, the Jacobian of the converged solution -- with random probe images. Complex random-phase probes are shown theoretically and empirically to minimize estimator variance compared with Gaussian or real-valued alternatives. PICO was validated against analytical benchmarks and high-replica PMR references using retrospective Cartesian knee data ($R=2$), prospective non-Cartesian spiral brain phantom data ($R=2,3,4$), and compressed-sensing knee reconstructions ($R=2$). Results: In Cartesian experiments, PICO accurately reproduced analytical SENSE $g$-factor maps. In non-Cartesian spiral imaging ($R=2$), it achieved 1% estimation error in 64 s compared with 462 s for PMR (approximately 7.2x speedup), with the efficiency advantage persisting at higher acceleration. For nonlinear compressed sensing, the Jacobian-based estimator produced noise maps consistent with PMR while converging faster (52 s vs. 95 s; approximately 1.8x speedup). Conclusion: PICO provides a computationally efficient alternative to PMR for voxelwise noise and $g$-factor estimation across generalized iterative MRI reconstructions. By reusing existing reconstruction primitives, it enables voxelwise noise maps to be produced as a routine by-product of the reconstruction pipeline. |
| title | Fast Voxelwise SNR Estimation for Iterative MRI Reconstructions |
| topic | Signal Processing 92C55, 65F10, 65C05, 94A12 |
| url | https://arxiv.org/abs/2605.09590 |