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

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.