Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

Background: The application of Deep Learning (DL) in MR image reconstruction is increasingly gaining attention due to its potential of increasing image quality and reducing acquisition time. However, the technology hasn’t been yet implemented in clinical routine. The aim of this study was therefore to describe the implementation of this novel DL image reconstruction for turbo spin echo (TSE) sequences in clinical workflow including a thorough explanation of the required steps and an evaluation of the obtainable image quality compared to conventional TSE. Methods: : DL image reconstruction using a variational network was clinically implemented to enable acquisition of accelerated TSE sequences. After internal review board’s approval and informed consent, 30 examinations for knee, shoulder, and lumbar spine in 15 volunteers at 3 T were included in this prospective study. Conventional TSE sequences (TSE) and TSE with deep learning reconstruction (TSE DL ) were compared regarding overall image quality, noise, sharpness, and subjective signal-to-noise-ratio (SNR), as well diagnostic confidence and image impression. Comparative analyses were conducted to assess the differences between the sequences. A survey on technologists’ acceptance was performed for DL image reconstruction. Results: : DL image reconstruction was successfully implemented in a clinical workflow and TSE DL allowed a remarkable time saving of more than 50%. Overall image quality, diagnostic confidence and image impression for TSE DL were rated as excellent (median 4, IQR 4-4) and comparable to TSE (image quality: p=0.059, diagnostic confidence: p=0.157, image impression: p=0.102). Noise, sharpness, artifacts, and subjective SNR for TSE DL reached significantly superior levels to TSE (noise: p<0.001, sharpness: p=0.001, artifacts: p=0.014, subjective SNR: p<0.001). Technologists reported high levels of acceptance for DL image reconstruction. Required time for the reconstruction process was rated moderate and longer than standard sequences (median 2, IQR 2-3). Required time and effort for the implementation in daily workflow was rated as low effort (median 4, IQR 3-4). General applicability of DL reconstruction as well as acceptance of DL sequences in clinical routine were rated excellent (median 4, IQR 3-4). Conclusion: DL image reconstruction for TSE sequences can be implemented in clinical workflow and enables a remarkable time saving (>50%) in image acquisition while maintaining excellent image quality. Trial registration: Your clinical trial is officially registered at the German DRKS with the registration number: DRKS00023278.