تقرير
Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction
| العنوان: | Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction |
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| المؤلفون: | Cho, Jaejin, Gagoski, Borjan, Kim, Taehyung, Tian, Qiyuan, Frost, Stephen Robert, Chatnuntawech, Itthi, Bilgic, Berkin |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning |
| الوصف: | Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further extend this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS). Method: Recently introduced MoDL technique successfully incorporates convolutional neural network (CNN)-based regularizers into physics-based parallel imaging reconstruction using a small number of network parameters. Wave-CAIPI is an emerging parallel imaging method that accelerates the imaging speed by employing sinusoidal gradients in the phase- and slice-encoding directions during the readout to take better advantage of 3D coil sensitivity profiles. In wave-MoDL, we propose to combine the wave-encoding strategy with unrolled network constraints to accelerate the acquisition speed while enforcing wave-encoded data consistency. We further extend wave-MoDL to reconstruct multi-contrast data with controlled aliasing in parallel imaging (CAIPI) sampling patterns to leverage similarity between multiple images to improve the reconstruction quality. Result: Wave-MoDL enables a 47-second MPRAGE acquisition at 1 mm resolution at 16-fold acceleration. For quantitative imaging, wave-MoDL permits a 2-minute acquisition for T1, T2, and proton density mapping at 1 mm resolution at 12-fold acceleration, from which contrast weighted images can be synthesized as well. Conclusion: Wave-MoDL allows rapid MR acquisition and high-fidelity image reconstruction and may facilitate clinical and neuroscientific applications by incorporating unrolled neural networks into wave-CAIPI reconstruction. Comment: 8 figures, 1 table |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2202.02814 |
| رقم الأكسشن: | edsarx.2202.02814 |
| قاعدة البيانات: | arXiv |
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