Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans
| Autor: | Shigeki Aoki, Shohei Fujita, Ken Pin Hwang, Koji Kamagata, Naoyuki Takei, Yujiro Otsuka, Kanako K. Kumamaru, Osamu Abe, Masaaki Hori, Akihiko Wada, Christina Andica, Toshiaki Akashi, Michimasa Suzuki, Ryusuke Irie, Akifumi Hagiwara |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Wilcoxon signed-rank test Image quality Signal-To-Noise Ratio Magnetic resonance angiography 030218 nuclear medicine & medical imaging Young Adult 03 medical and health sciences Deep Learning Imaging Three-Dimensional 0302 clinical medicine medicine.artery Image Interpretation Computer-Assisted Healthy volunteers medicine Humans Radiology Nuclear Medicine and imaging cardiovascular diseases Mathematics medicine.diagnostic_test business.industry Deep learning Intracranial Aneurysm Magnetic resonance imaging General Medicine eye diseases Ophthalmic artery T2 relaxation Female Artificial intelligence business Nuclear medicine Algorithms Magnetic Resonance Angiography 030217 neurology & neurosurgery |
| Zdroj: | Investigative Radiology. 55:249-256 |
| ISSN: | 0020-9996 |
| Popis: | OBJECTIVES Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of various contrast-weighted images as well as simultaneous quantification of T1 and T2 relaxation times and proton density. However, to date, it has been challenging to generate magnetic resonance angiography (MRA) images with synthetic MRI. The purpose of this study was to develop a deep learning algorithm to generate MRA images based on 3D synthetic MRI raw data. MATERIALS AND METHODS Eleven healthy volunteers and 4 patients with intracranial aneurysms were included in this study. All participants underwent a time-of-flight (TOF) MRA sequence and a 3D-QALAS synthetic MRI sequence. The 3D-QALAS sequence acquires 5 raw images, which were used as the input for a deep learning network. The input was converted to its corresponding MRA images by a combination of a single-convolution and a U-net model with a 5-fold cross-validation, which were then compared with a simple linear combination model. Image quality was evaluated by calculating the peak signal-to-noise ratio (PSNR), structural similarity index measurements (SSIMs), and high frequency error norm (HFEN). These calculations were performed for deep learning MRA (DL-MRA) and linear combination MRA (linear-MR), relative to TOF-MRA, and compared with each other using a nonparametric Wilcoxon signed-rank test. Overall image quality and branch visualization, each scored on a 5-point Likert scale, were blindly and independently rated by 2 board-certified radiologists. RESULTS Deep learning MRA was successfully obtained in all subjects. The mean PSNR, SSIM, and HFEN of the DL-MRA were significantly higher, higher, and lower, respectively, than those of the linear-MRA (PSNR, 35.3 ± 0.5 vs 34.0 ± 0.5, P < 0.001; SSIM, 0.93 ± 0.02 vs 0.82 ± 0.02, P < 0.001; HFEN, 0.61 ± 0.08 vs 0.86 ± 0.05, P < 0.001). The overall image quality of the DL-MRA was comparable to that of TOF-MRA (4.2 ± 0.7 vs 4.4 ± 0.7, P = 0.99), and both types of images were superior to that of linear-MRA (1.5 ± 0.6, for both P < 0.001). No significant differences were identified between DL-MRA and TOF-MRA in the branch visibility of intracranial arteries, except for ophthalmic artery (1.2 ± 0.5 vs 2.3 ± 1.2, P < 0.001). CONCLUSIONS Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time. |
| Databáze: | OpenAIRE |
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