著者

Noriyuki Fujima Koji Kamagata Daiju Ueda Shohei Fujita Yasutaka Fushimi Masahiro Yanagawa Rintaro Ito Takahiro Tsuboyama Mariko Kawamura Takeshi Nakaura Akira Yamada Taiki Nozaki Tomoyuki Fujioka Yusuke Matsui Kenji Hirata Fuminari Tatsugami Shinji Naganawa

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.rev.2023-0047, (Released:2023-08-01)

参考文献数

123

被引用文献数

4

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Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

著者

Masami Goto Osamu Abe Akifumi Hagiwara Shohei Fujita Koji Kamagata Masaaki Hori Shigeki Aoki Takahiro Osada Seiki Konishi Yoshitaka Masutani Hajime Sakamoto Yasuaki Sakano Shinsuke Kyogoku Hiroyuki Daida

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.rev.2021-0096, (Released:2022-02-18)

参考文献数

106

被引用文献数

31

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Surface-based morphometry (SBM) is extremely useful for estimating the indices of cortical morphology, such as volume, thickness, area, and gyrification, whereas voxel-based morphometry (VBM) is a typical method of gray matter (GM) volumetry that includes cortex measurement. In cases where SBM is used to estimate cortical morphology, it remains controversial as to whether VBM should be used in addition to estimate GM volume. Therefore, this review has two main goals. First, we summarize the differences between the two methods regarding preprocessing, statistical analysis, and reliability. Second, we review studies that estimate cortical morphological changes using VBM and/or SBM and discuss whether using VBM in conjunction with SBM produces additional values. We found cases in which detection of morphological change in either VBM or SBM was superior, and others that showed equivalent performance between the two methods. Therefore, we concluded that using VBM and SBM together can help researchers and clinicians obtain a better understanding of normal neurobiological processes of the brain. Moreover, the use of both methods may improve the accuracy of the detection of morphological changes when comparing the data of patients and controls.In addition, we introduce two other recent methods as future directions for estimating cortical morphological changes: a multi-modal parcellation method using structural and functional images, and a synthetic segmentation method using multi-contrast images (such as T1- and proton density-weighted images).

著者

Akihiko Wada Yuya Saito Shohei Fujita Ryusuke Irie Toshiaki Akashi Katsuhiro Sano Shinpei Kato Yutaka Ikenouchi Akifumi Hagiwara Kanako Sato Nobuo Tomizawa Yayoi Hayakawa Junko Kikuta Koji Kamagata Michimasa Suzuki Masaaki Hori Atsushi Nakanishi Shigeki Aoki

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.2021-0068, (Released:2021-12-10)

参考文献数

32

被引用文献数

5

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Purpose: Myelination-related MR signal changes in white matter are helpful for assessing normal development in infants and children. A rule-based myelination evaluation workflow regarding signal changes on T1-weighted images (T1WIs) and T2-weighted images (T2WIs) has been widely used in radiology. This study aimed to simulate a rule-based workflow using a stacked deep learning model and evaluate age estimation accuracy.Methods: The age estimation system involved two stacked neural networks: a target network-to extract five myelination-related images from the whole brain, and an age estimation network from extracted T1- and T2WIs separately. A dataset was constructed from 119 children aged below 2 years with two MRI systems. A four-fold cross-validation method was adopted. The correlation coefficient (CC), mean absolute error (MAE), and root mean squared error (RMSE) of the corrected chronological age of full-term birth, as well as the mean difference and the upper and lower limits of 95% agreement, were measured. Generalization performance was assessed using datasets acquired from different MR images. Age estimation was performed in Sturge–Weber syndrome (SWS) cases.Results: There was a strong correlation between estimated age and corrected chronological age (MAE: 0.98 months; RMSE: 1.27 months; and CC: 0.99). The mean difference and standard deviation (SD) were −0.15 and 1.26, respectively, and the upper and lower limits of 95% agreement were 2.33 and −2.63 months. Regarding generalization performance, the performance values on the external dataset were MAE of 1.85 months, RMSE of 2.59 months, and CC of 0.93. Among 13 SWS cases, 7 exceeded the limits of 95% agreement, and a proportional bias of age estimation based on myelination acceleration was exhibited below 12 months of age (P = 0.03).Conclusion: Stacked deep learning models automated the rule-based workflow in radiology and achieved highly accurate age estimation in infants and children up to 2 years of age.

著者

Tomoko Maekawa Masaaki Hori Katsutoshi Murata Thorsten Feiweier Kouhei Kamiya Christina Andica Akifumi Hagiwara Shohei Fujita Koji Kamagata Akihiko Wada Osamu Abe Shigeki Aoki

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.ici.2021-0083, (Released:2021-09-10)

参考文献数

12

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Oscillating-gradient spin-echo sequences enable the measurement of diffusion weighting with a short diffusion time and can provide indications of internal structures. We report two cases of brain abscess in which the apparent diffusion coefficient (ADC) values appear higher at short diffusion times in comparison with those at long diffusion times. Diffusion time dependence of the ADC in brain abscesses suggests not only substrate viscosity but also restricted diffusion due to the structure within the lesions.

著者

Akihiko Wada Kohei Tsuruta Ryusuke Irie Koji Kamagata Tomoko Maekawa Shohei Fujita Saori Koshino Kanako Kumamaru Michimasa Suzuki Atsushi Nakanishi Masaaki Hori Shigeki Aoki

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.2018-0091, (Released:2018-12-03)

参考文献数

40

被引用文献数

22

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Purpose: Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are representative disorders of dementia of the elderly and the neuroimaging has contributed to early diagnosis by estimation of alterations of brain volume, blood flow and metabolism. A brain network analysis by MR imaging (MR connectome) is a recently developed technique and can estimate the dysfunction of the brain network in AD and DLB. A graph theory which is a major technique of network analysis is useful for a group study to extract the feature of disorders, but is not necessarily suitable for the disorder differentiation at the individual level. In this investigation, we propose a deep learning technique as an alternative method of the graph analysis for recognition and classification of AD and DLB at the individual subject level.Materials and Methods: Forty-eight brain structural connectivity data of 18 AD, 8 DLB and 22 healthy controls were applied to the machine learning consisting of a six-layer convolution neural network (CNN) model. Estimation of the deep learning model to classify AD, DLB and non-AD/DLB was performed using the 4-fold cross-validation method.Results: The accuracy, average precision and recall of our CNN model were 0.73, 0.78 and 0.73, and the specificity precision and recall were 0.68 and 0.79 in AD, 0.94 and 0.65 in DLB and 0.73 and 0.75 in non-AD/DLB. The triangular probability map of the MR connectome revealed the probability of AD, DLB and non-AD/DLB in each subject.Conclusion: Our preliminary investigation revealed the adaptation of deep learning to the MR connectome and proposed its utility in the differentiation of dementia disorders at the individual subject level.

著者

Shizen Ohnishi Noriyuki Miyake Toru Takeuchi Fumiko Kousaka Satoshi Hiura Osamu Kanehira Miki Saito Takashi Sayama Ayako Higashi Masao Ishimoto Yoshinori Tanaka Shohei Fujita

出版者

Japanese Society of Breeding

雑誌

Breeding Science (ISSN:13447610)

巻号頁・発行日

vol.61, no.5, pp.618-624, 2012 (Released:2012-02-04)

参考文献数

30

被引用文献数

11 17 7

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Soybean dwarf virus (SbDV) causes serious dwarfing, yellowing and sterility in soybean (Glycine max). The soybean cv. Adams is tolerant to SbDV infection in the field and exhibits antibiosis to foxglove aphid (Aulacorthum solani), which transmits SbDV. This antibiosis (termed “aphid resistance”) is required for tolerance to SbDV in the field in segregated progenies of Adams. A major quantitative trait locus, Raso1, is reported for foxglove aphid resistance. Our objectives were to fine map Raso1 and to reveal whether Raso1 alone is sufficient to confer both aphid resistance and SbDV tolerance. We introduced Raso1 into cv. Toyomusume by backcrossing and investigated the degree of aphid antibiosis to foxglove aphid and the degree of tolerance to SbDV in the field. All Raso1-introduced backcross lines showed aphid resistance. Interestingly, only one Raso1-introduced backcross line (TM-1386) showed tolerance to SbDV in the field. The results demonstrated Raso1 alone is sufficient to confer aphid resistance but insufficient for SbDV tolerance. Tolerance to SbDV was indicated to require additional gene(s) to Raso1. Additionally, Raso1 was mapped to a 63-kb interval on chromosome 3 of the Williams 82 sequence assembly (Glyma1). This interval includes a nucleotide-binding site–leucine-rich repeat encoding gene and two other genes in the Williams 82 soybean genome sequence.