Parallel imaging and convolutional neural network combined fast MR image reconstruction: Applications in low‐latency accelerated real‐time imaging

Abstract

Purpose To develop and evaluate a parallel imaging and convolutional neural network combined image reconstruction framework for low‐latency and high‐quality accelerated real‐time MR imaging. Methods Conventional Parallel Imaging reconstruction resolved as gradient descent steps was compacted as network layers and interleaved with convolutional layers in a general convolutional neural network. All parameters of the network were determined during the offline training process, and applied to unseen data once learned. The proposed network was first evaluated for real‐time cardiac imaging at 1.5 T and real‐time abdominal imaging at 0.35 T, using threefold to fivefold retrospective undersampling for cardiac imaging and threefold retrospective undersampling for abdominal imaging. Then, prospective undersampling with fourfold acceleration was performed on cardiac imaging to compare the proposed method with standard clinically available GRAPPA method and the state‐of‐the‐art L1‐ESPIRiT method. Results Both retrospective and prospective evaluations confirmed that the proposed network was able to images with a lower noise level and reduced aliasing artifacts in comparison with the single‐coil based and L1‐ESPIRiT reconstructions for cardiac imaging at 1.5 T, and the GRAPPA and L1‐ESPIRiT reconstructions for abdominal imaging at 0.35 T. Using the proposed method, each frame can be reconstructed in less than 100 ms, suggesting its clinical compatibility. Conclusion The proposed Parallel Imaging and convolutional neural network combined reconstruction framework is a promising technique that allows low‐latency and high‐quality real‐time MR imaging.

Cardiac magnetic resonance assessment of central and peripheral vascular function in patients undergoing renal sympathetic denervation as predictor for blood pressure response

“…This figure demonstrates the use of deep learning for various tasks; reconstruction of RT-MRI data (removing artefact from undersampled radial images, Figure 8a), improving image quality of RT-MRI (performing off-resonance deblurring, Figure 8b), and to enhance clinical impact (performing Needle detection and segmentation, Figure 8c). It has been shown to be popular for low-latency reconstructions of real-time MR data; in 2D cardiac imaging (92,195,196), MR-guided radiotherapy images (197), and 3D functional MRI (198).…”

Fast and accurate calculation of myocardial T1 and T2 values using deep learning Bloch equation simulations (DeepBLESS)

“…Although DeepBLESS achieved almost instantaneous T 1 /T 2 map reconstruction for the radial T 1 ‐T 2 mapping sequence, the compressed‐sensing reconstruction took approximately 3 minutes, a limitation for using the radial T 1 ‐T 2 sequence for simultaneous myocardial T 1 and T 2 mapping. Recent studies have shown that deep learning can be applied to replace compressed sensing, to reduce reconstruction time 35‐37 . These techniques may be combined with our proposed T 1 calculation technique to further reduce total imaging time and enable online use of the radial T 1 ‐T 2 mapping technique.…”

“…Recent studies have shown that deep learning can be applied to replace compressed sensing, to reduce reconstruction time. [35][36][37] These techniques may be combined with our proposed T 1 calculation technique to further reduce total imaging time and enable online use of the radial T 1 -T 2 mapping technique.…”

Parallel imaging with a combination of sensitivity encoding and generative adversarial networks

“…Then, the PI-CNN network (35), which integrated multichannel undersampled k-space data and exploited them through parallel imaging was proposed. Furthermore, the Deepcomplex CNN (36) was introduced to get the dealiased multi-channel images without the need for any prior information.…”