A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
This work addresses a critical bottleneck in fast MRI for medical diagnostics by enhancing reconstruction quality, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of inaccurate coil sensitivity maps in accelerated MRI reconstruction, which degrade image quality, by proposing a joint deep sensitivity estimation and image reconstruction network (JDSI) that improves reconstruction performance, achieving state-of-the-art results visually and quantitatively, especially at high acceleration factors.
Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.