著者

Shoko Hara Masaaki Hori Koji Kamagata Christina Andica Motoki Inaji Yoji Tanaka Shigeki Aoki Tadashi Nariai Taketoshi Maehara

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.2022-0146, (Released:2023-04-18)

参考文献数

57

被引用文献数

3

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Purpose: Moyamoya disease (MMD) is a cerebrovascular disease associated with steno-occlusive changes in the arteries of the circle of Willis and with hemodynamic impairment. Previous studies have reported that parenchymal extracellular free water levels may be increased and the number of neurites may be decreased in patients with MMD. The aim of the present study was to investigate the postoperative changes in parenchymal free water and neurites and their relationship with cognitive improvement.Methods: Multi-shell diffusion MRI (neurite orientation dispersion and density imaging and free water imaging using a bi-tensor model) was performed in 15 hemispheres of 13 adult patients with MMD (11 female, mean age 37.9 years) who had undergone revascularization surgery as well as age- and sex-matched normal controls. Parameter maps of free water and free-water-eliminated neurites were created, and the regional parameter values were compared among controls, patients before surgery, and patients after surgery.Results: The anterior and middle cerebral artery territories of patients showed higher preoperative free water levels (P ≤ 0.007) and lower postoperative free water levels (P ≤ 0.001) than those of normal controls. The change in the dispersion of the white matter in the anterior cerebral artery territory correlated with cognitive improvement (r = –0.75; P = 0.004).Conclusion: Our study suggests that increased parenchymal free water levels decreased after surgery and that postoperative changes in neurite parameters are related to postoperative cognitive improvement in adult patients with MMD. Diffusion analytical methods separately calculating free water and neurites may be useful for unraveling the pathophysiology of chronic ischemia and the postoperative changes that occur after revascularization surgery in this disease population.

著者

Tomoko Maekawa Kouhei Kamiya Katsutoshi Murata Thorsten Feiweier 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

vol.20, no.2, pp.227-230, 2021 (Released:2021-06-01)

参考文献数

12

被引用文献数

2 5

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The microstructural underpinnings of reduced diffusivity in transient splenial lesion remain unclear. Here, we report findings from oscillating gradient spin-echo (OGSE) diffusion imaging in a case of transient splenial lesion. Compared with normal-appearing white matter, the splenial lesion exhibited greater differences between diffusion time t = 6.5 and 35.2 ms, indicating microstructural changes occurring within the corresponding length scale. We also conducted 2D Monte-Carlo simulation. The results suggested that emergence of small and non-exchanging compartment, as often imagined in intramyelinic edema, does not fit well with the in vivo observation. Simulations with axonal swelling and microglial infiltration yielded results closer to the in vivo observations. The present report exemplifies the importance of controlling t for more specific radiological image interpretations.

著者

Junpei Nagasawa Kenichi Suzuki Sayori Hanashiro Masaru Yanagihashi Takehisa Hirayama Masaaki Hori Osamu Kano

出版者

The University of Tokushima Faculty of Medicine

雑誌

The Journal of Medical Investigation (ISSN:13431420)

巻号頁・発行日

vol.70, no.3.4, pp.411-414, 2023 (Released:2023-11-09)

参考文献数

19

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Introduction:Branch atheromatous disease (BAD) is a type of cerebral infarction caused by stenosis or occlusion at the entrance of the penetrating branch due to the presence of plaque. Despite its clinical significance, it is not clear how these plaques are formed. Focal geometrical characteristics are expected to be as important as vascular risk factors in the development of atherosclerosis. This study aimed to analyze the association between middle cerebral artery (MCA) geometric features and the onset of BAD. Shear stress results from the blood flow exerting force on the inner wall of the vessels and places with low wall shear stress may be prone to atherosclerosis. At the curvature of blood vessels, the shear stress is weak on the inside of the curve and plaque is likely to form. When this is applied to the MCA M1 segment, downward type M1 is likely to form plaques on the superior side. Because the lenticulostriate artery usually branches off from the superior side of the MCA M1 segment, in downward type M1, a plaque is likely to be formed at the entrance of the penetrating branch, and for that reason, BAD is likely to onset. Methods:We retrospectively reviewed hospitalized stroke patients with BAD and investigated the morphology of their MCA using magnetic resonance imaging. The M1 segment was classified as straight or curved. Additionally, we compared the difference between the symptomatic and the asymptomatic side. Data regarding patients’ medical history were also collected. Results:A total of 56 patients with lenticulostriate artery infarctions and BAD were analyzed. On the symptomatic side, downward type M1 accounted for the largest proportion at 44%, whereas on the asymptomatic side, it was the lowest, at 16%. Conclusion:A downward type MCA may be associated with the onset of BAD and the morphological characteristics might affect the site of plaque formation. J. Med. Invest. 70 : 411-414, August, 2023

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.