NeuroMix-DL: Improving imaging quality of a fast multiparametric MRI protocol using deep learning.
Authors
Affiliations (5)
Affiliations (5)
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Department of Radiology, Stanford University, Stanford, CA 94305, USA. Electronic address: [email protected].
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark; University Research and Innovation Center, Óbuda University, Budapest, Hungary.
- MR Applied Science Laboratory Europe, GE Healthcare, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
Abstract
To improve the quality of a fast multi-contrast MR protocol acquisition using deep learning. 350 patients (age: 64 ± 17 yrs; 155 male) underwent both a fast brain MR multi-contrast sequence (NeuroMix), capturing five contrasts in a single 2.5-minute sequence, and conventional high-resolution imaging. This retrospective study was approved by the Stanford IRB (eProtocol 26147, IRB registration 6208). The paired images were used to enhance resolution and image quality using a Swin U-Net Transformer (SwinUNETR) approach, focusing on T1-weighted (T1w), T2-weighted (T2w), and T2 FLAIR images (NeuroMix-DL). Evaluation included standard image quality metrics, such as the root mean squared error (RMSE) and a clinical quality assessment using a five-point image quality scale (1 = poor, 5 = excellent). A pairwise t-test was calculated to evaluate the values of the qualitative and quantitative metrics across the various image processing approaches. We found significant improvement in image quality after applying the trained SwinUNETR, with RMSE reductions of 42 ± 3%, 33 ± 2%, and 33 ± 9% for T1w, T2w, and FLAIR images, respectively (p < 0.001 for all) compared to original NeuroMix images, using conventional sequences as reference. The clinical readers found higher image quality scores for NeuroMix-DL images compared to the original NeuroMix images (12 ± 8%, 17 ± 11%, and 15 ± 6% for T1w, T2w, and FLAIR images, respectively). Visual quality assessment demonstrated improvements in prevalent artifacts, including motion, herringbone artifact, inhomogeneity artifact, and RF overflow. The SwinUNETR model offers a viable approach for improving the quality of fast multi-contrast MR images while effectively mitigating artifacts, improving the cost-benefit ratio of MRI.