Deep Cardiac MRI Reconstruction with ADMM

• URL: http://arxiv.org/abs/2310.06628v1
• Date: Tue, 10 Oct 2023 13:46:11 GMT
• Title: Deep Cardiac MRI Reconstruction with ADMM
• Authors: George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
• Abstract summary: We present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of cardiac imaging. Our method optimize in both the image and k-space domains, allowing for high reconstruction fidelity.
• Score: 7.694990352622926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiac magnetic resonance imaging is a valuable non-invasive tool for identifying cardiovascular diseases. For instance, Cine MRI is the benchmark modality for assessing the cardiac function and anatomy. On the other hand, multi-contrast (T1 and T2) mapping has the potential to assess pathologies and abnormalities in the myocardium and interstitium. However, voluntary breath-holding and often arrhythmia, in combination with MRI's slow imaging speed, can lead to motion artifacts, hindering real-time acquisition image quality. Although performing accelerated acquisitions can facilitate dynamic imaging, it induces aliasing, causing low reconstructed image quality in Cine MRI and inaccurate T1 and T2 mapping estimation. In this work, inspired by related work in accelerated MRI reconstruction, we present a deep learning (DL)-based method for accelerated cine and multi-contrast reconstruction in the context of dynamic cardiac imaging. We formulate the reconstruction problem as a least squares regularized optimization task, and employ vSHARP, a state-of-the-art DL-based inverse problem solver, which incorporates half-quadratic variable splitting and the alternating direction method of multipliers with neural networks. We treat the problem in two setups; a 2D reconstruction and a 2D dynamic reconstruction task, and employ 2D and 3D deep learning networks, respectively. Our method optimizes in both the image and k-space domains, allowing for high reconstruction fidelity. Although the target data is undersampled with a Cartesian equispaced scheme, we train our model using both Cartesian and simulated non-Cartesian undersampling schemes to enhance generalization of the model to unseen data. Furthermore, our model adopts a deep neural network to learn and refine the sensitivity maps of multi-coil k-space data. Lastly, our method is jointly trained on both, undersampled cine and multi-contrast data.

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