著者

Michiru Kajiwara Tomoyuki Haishi Dwi Prananto Susumu Sasaki Ryohei Kaseda Ichiei Narita Yasuhiko Terada

出版者

Japanese Society for Magnetic Resonance in Medicine

雑誌

Magnetic Resonance in Medical Sciences (ISSN:13473182)

巻号頁・発行日

pp.tn.2021-0094, (Released:2021-12-10)

参考文献数

19

被引用文献数

1

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23Na-MRI provides information on Na+ content, and its application in the medical field has been highly anticipated. However, for existing clinical 1H-MRI systems, its implementation requires an additional broadband RF transmitter, dedicated transceivers, and RF coils for Na+ imaging. However, a standard medical MRI system cannot often be modified to perform 23Na imaging. We have developed an add-on crossband RF repeater system that enables 23Na-MRI simply by inserting it into the magnet bore of an existing 1H MRI. The three axis gradient fields controlled by the 1H-MRI system were directly used for 23Na imaging without any deformation. A crossband repeater is a common technique used for amateur radio. This concept was proven by a saline solution phantom and in vivo mouse experiments. This add-on RF platform is applicable to medical 1H MRI systems and can enhance the application of 23Na-MRI in clinical usage.

著者

Tomoki Miyasaka Michiru Kajiwara Akito Kawasaki Yoshikazu Okamoto Yasuhiko Terada

出版者

Japanese Society for Magnetic Resonance in Medicine

雑誌

Magnetic Resonance in Medical Sciences (ISSN:13473182)

巻号頁・発行日

pp.tn.2021-0158, (Released:2022-04-26)

参考文献数

12

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Portable MRI scanners, in which a permanent magnet with a low magnetic field is mounted on a small car, have enabled the performance of MRI examinations in various remote environments. Here, we have modified the portable MRI system to enable the early diagnosis of wrist sports injuries among tennis players. A RF probe specifically designed for the human wrist was developed, and a power supply scheme using a small generator was introduced. The portable MRI system was located at a tennis school and imaging of the wrists of junior tennis players was performed. To demonstrate clinical feasibility, image quality was assessed by a radiologist and clinical evaluations were performed. In most cases, the image quality was sufficient for diagnosis, and triangular fibrocartilage complex damage could be detected. The results indicated that the modified portable MRI system could be applied for an early diagnosis of wrist injuries.

著者

Naoto Fujita Suguru Yokosawa Toru Shirai Yasuhiko Terada

出版者

Japanese Society for Magnetic Resonance in Medicine

雑誌

Magnetic Resonance in Medical Sciences (ISSN:13473182)

巻号頁・発行日

pp.mp.2023-0031, (Released:2023-07-28)

参考文献数

45

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Purpose: Deep neural networks (DNNs) for MRI reconstruction often require large datasets for training. Still, in clinical settings, the domains of datasets are diverse, and how robust DNNs are to domain differences between training and testing datasets has been an open question. Here, we numerically and clinically evaluate the generalization of the reconstruction networks across various domains under clinically practical conditions and provide practical guidance on what points to consider when selecting models for clinical application.Methods: We compare the reconstruction performance between four network models: U-Net, the deep cascade of convolutional neural networks (DC-CNNs), Hybrid Cascade, and variational network (VarNet). We used the public multicoil dataset fastMRI for training and testing and performed a single-domain test, where the domains of the dataset used for training and testing were the same, and cross-domain tests, where the source and target domains were different. We conducted a single-domain test (Experiment 1) and cross-domain tests (Experiments 2–4), focusing on six factors (the number of images, sampling pattern, acceleration factor, noise level, contrast, and anatomical structure) both numerically and clinically.Results: U-Net had lower performance than the three model-based networks and was less robust to domain shifts between training and testing datasets. VarNet had the highest performance and robustness among the three model-based networks, followed by Hybrid Cascade and DC-CNN. Especially, VarNet showed high performance even with a limited number of training images (200 images/10 cases). U-Net was more robust to domain shifts concerning noise level than the other model-based networks. Hybrid Cascade showed slightly better performance and robustness than DC-CNN, except for robustness to noise-level domain shifts. The results of the clinical evaluations generally agreed with the results of the quantitative metrics.Conclusion: In this study, we numerically and clinically evaluated the robustness of the publicly available networks using the multicoil data. Therefore, this study provided practical guidance for clinical applications.