A multinational study of deep learning-based image enhancement for multiparametric glioma MRI.
Authors
Affiliations (10)
Affiliations (10)
- Department of Radiology and Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, Seoul, Korea.
- Department of Radiology, New York University Grossman School of Medicine, 550 1 st Ave, New York, NY, USA.
- Airs Medical, Seoul, Korea.
- Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul, Korea.
- Department of Radiology and Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea. [email protected].
- Department of Neurosurgery, New York University Grossman School of Medicine, 550 1 st Ave, New York, NY, USA.
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehangno, Jongno-gu, Seoul, 03080, Republic of Korea. [email protected].
- Center for Nanoparticle Research, Institute for Basic Science, and School of Chemical and Biological Engineering, Seoul National University, 1, Gwanak- ro, Gwanak-gu, Seoul, 302-909, Republic of Korea. [email protected].
Abstract
This study aimed to validate the utility of commercially available vendor-neutral deep learning (DL) image enhancement software for improving the image quality of multiparametric MRI for gliomas in a multinational setting. A total of 294 patients from three institutions (NYU, Severance, and SNUH) who underwent glioma MRI protocols were included in this retrospective study. DL image enhancement was performed on T2-weighted (T2W), T2 FLAIR, and postcontrast T1-weighted (T1W) imaging using commercially available DL image enhancement software. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for both conventional and DL-enhanced images. Three neuroradiologists, one from each institution, independently evaluated the following image quality parameters in both images using a 5-point scale: overall image quality, noise, gray-white matter differentiation, truncation artifact, motion artifact, pulsation artifact, and main lesion conspicuity. The quantitative and qualitative image parameters were compared between conventional and DL-enhanced images. Compared with conventional images, DL-enhanced images showed significantly higher SNRs and CNRs in T2W, T2 FLAIR, and postcontrast T1W imaging (all P < 0.001). The average scores of radiologist assessments in overall image quality, noise, gray-white matter differentiation, and main lesion conspicuity were significantly higher for DL-enhanced images than conventional images in T2W, T2 FLAIR, and postcontrast T1W imaging (all P < 0.001). Regarding artifacts, truncation artifacts decreased (all P < 0.001), while pre-existing motion and pulsation artifacts were not further exaggerated in most structural MRI sequences. In conclusion, DL image enhancement using commercially available vendor-neutral software improved image quality and reduced truncation artifacts in multiparametric glioma MRI.