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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.19407 |
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| _version_ | 1866913051094024192 |
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| author | Bschorr, Fabian Gebhard, Pia Speidel, Tobias Rasche, Volker |
| author_facet | Bschorr, Fabian Gebhard, Pia Speidel, Tobias Rasche, Volker |
| contents | Purpose: Quasi-random Sobol-based sampling schemes exhibit deterministic structural artifacts when aggressively undersampled, particularly at low encoding densities required for accelerated 2D SPI/CSI. To address these limitations, two advanced undersampling strategies are investigated to mitigate deterministic behavior, improving image quality for time-constrained applications such as hyperpolarized MRI.
Methods: An optimized Sobol sequence-derived point distribution with Heaviside-type density gradient center oversampling served as the initial sampling pattern. Undersampling was performed using two point-reduction algorithms: radius-adaptive stochastic undersampling (RAST), which applies a geometric, radius-dependent minimum-distance criterion, and Bayesian Information Gain Optimization (BINGO), that removes points based on their information gain to the reconstructed image. Phantom experiments were conducted on a 3 T clinical MRI system using up to 16-fold undersampling. Image quality was quantified using a performance score derived from RMSE, SSIM, and HFEN.
Results: Both RAST and BINGO outperformed deterministic undersampling across all metrics. RAST achieved highest and most robust performance, with improvements up to 238% in the averaged metric score, while BINGO yielded improvements of 133% across matrix resolutions.
Conclusion: The proposed strategies effectively reduce the number of encoding points in low-discrepancy 2D SPI point distributions while maintaining image quality under strong acceleration. RAST provides superior metric performance, whereas BINGO offers broad applicability, including suitability for non-linear encoding fields. These approaches support rapid acquisition workflows required for real-time and hyperpolarized applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19407 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimized encoding point distributions for efficient single-point imaging Bschorr, Fabian Gebhard, Pia Speidel, Tobias Rasche, Volker Medical Physics Purpose: Quasi-random Sobol-based sampling schemes exhibit deterministic structural artifacts when aggressively undersampled, particularly at low encoding densities required for accelerated 2D SPI/CSI. To address these limitations, two advanced undersampling strategies are investigated to mitigate deterministic behavior, improving image quality for time-constrained applications such as hyperpolarized MRI. Methods: An optimized Sobol sequence-derived point distribution with Heaviside-type density gradient center oversampling served as the initial sampling pattern. Undersampling was performed using two point-reduction algorithms: radius-adaptive stochastic undersampling (RAST), which applies a geometric, radius-dependent minimum-distance criterion, and Bayesian Information Gain Optimization (BINGO), that removes points based on their information gain to the reconstructed image. Phantom experiments were conducted on a 3 T clinical MRI system using up to 16-fold undersampling. Image quality was quantified using a performance score derived from RMSE, SSIM, and HFEN. Results: Both RAST and BINGO outperformed deterministic undersampling across all metrics. RAST achieved highest and most robust performance, with improvements up to 238% in the averaged metric score, while BINGO yielded improvements of 133% across matrix resolutions. Conclusion: The proposed strategies effectively reduce the number of encoding points in low-discrepancy 2D SPI point distributions while maintaining image quality under strong acceleration. RAST provides superior metric performance, whereas BINGO offers broad applicability, including suitability for non-linear encoding fields. These approaches support rapid acquisition workflows required for real-time and hyperpolarized applications. |
| title | Optimized encoding point distributions for efficient single-point imaging |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2604.19407 |