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- Title
Using a deep learning prior for accelerating hyperpolarized <sup>13</sup>C MRSI on synthetic cancer datasets.
- Authors
Wang, Zuojun; Luo, Guanxiong; Li, Ye; Cao, Peng
- Abstract
Purpose: We aimed to incorporate a deep learning prior with k‐space data fidelity for accelerating hyperpolarized carbon‐13 MRSI, demonstrated on synthetic cancer datasets. Methods: A two‐site exchange model, derived from the Bloch equation of MR signal evolution, was firstly used in simulating training and testing data, that is, synthetic phantom datasets. Five singular maps generated from each simulated dataset were used to train a deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled MRI k‐space data. The proposed method was assessed on synthetic human brain tumor images (N = 33), prostate cancer images (N = 72), and mouse tumor images (N = 58) for three undersampling factors and 2.5% additive Gaussian noise. Furthermore, varied levels of Gaussian noise with SDs of 2.5%, 5%, and 10% were added on synthetic prostate cancer data, and corresponding reconstruction results were evaluated. Results: For quantitative evaluation, peak SNRs were approximately 32 dB, and the accuracy was generally improved for 5 to 8 dB compared with those from compressed sensing with L1‐norm regularization or total variation regularization. Reasonable normalized RMS error were obtained. Our method also worked robustly against noise, even on a data with noise SD of 10%. Conclusion: The proposed singular value decomposition + iterative deep learning model could be considered as a general framework that extended the application of deep learning MRI reconstruction to metabolic imaging. The morphology of tumors and metabolic images could be measured robustly in six times acceleration using our method.
- Subjects
DEEP learning; PRIOR learning; SINGULAR value decomposition; BLOCH equations; SPEECH processing systems; RANDOM noise theory; COMPUTATIONAL linguistics
- Publication
Magnetic Resonance in Medicine, 2024, Vol 92, Issue 3, p945
- ISSN
0740-3194
- Publication type
Article
- DOI
10.1002/mrm.30053