Deep Cardiac MRI Reconstruction with ADMM
George Yiasemis, Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen

TL;DR
This paper introduces a deep learning-based reconstruction method for accelerated cardiac MRI, improving image quality and mapping accuracy in dynamic imaging scenarios with undersampled data.
Contribution
It presents a novel DL-based inverse problem solver using vSHARP with ADMM for high-fidelity cardiac MRI reconstruction, handling both 2D and 3D dynamic data.
Findings
Enhanced reconstruction fidelity in undersampled MRI data
Improved generalization across different undersampling schemes
Joint training on cine and multi-contrast data boosts performance
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…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Cardiac Imaging and Diagnostics · Medical Imaging Techniques and Applications
