著者

Mungunkhuyag MAJIGSUREN Takashi ABE Teruyoshi KAGEJI Kenji MATSUZAKI Mayumi TAKEUCHI Seiji IWAMOTO Yoichi OTOMI Naoto UYAMA Shinji NAGAHIRO Masafumi HARADA

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.15, no.1, pp.34-40, 2016-01-01 (Released:2016-01-12)

参考文献数

21

被引用文献数

10 21

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Purpose: T1-Cube (GE HealthCare) is a relatively new 3-dimensional (3D) fast spin-echo (FSE)-based magnetic resonance (MR) imaging sequence that uses a variable flip angle to acquire gap-free volume scans. We compared the gadolinium enhancement characteristics of a heterogeneous population of brain tumors imaged by T1-Cube and then 3D fast spoiled gradient recall acquisition in steady state (3D FSPGR) 3-tesla MR imaging to identify the superior modality for specific diagnostic purposes.Methods: We examined 61 lesions from 32 patients using the 2 sequences after administration of gadopentetic acid (Gd-DTPA; 0.1 mmol/kg). Two neuroradiologists independently measured each lesion twice using a region-of-interest (ROI) method. We measured the contrast-to-noise ratio (CNR), the difference in signal intensity (SI) between the tumor and normal white matter relative to the standard deviation (SD) of the SI within the lesion, for both post-contrast 3D FSPGR and post-contrast T1-Cube images of the same tumor and compared modality-specific CNRs for all tumors and in subgroups defined by tumor size, enhancement ratio, and histopathology.Results: The mean CNR was significantly higher on T1-Cube images than 3D FSPGR images for the total tumor population (1.85 ± 0.97 versus 1.12 ± 1.05, P < 0.01) and the histologic types, i.e., metastasis (P < 0.01) and lymphoma (P < 0.05). The difference in CNR was even larger for smaller tumors in the metastatic group (4.95 to 23.5 mm2) (P < 0.01). In contrast, mean CNRs did not differ between modalities for high grade glioma and meningioma.Conclusions: Gadolinium enhancement of brain tumors was generally higher when imaged by T1-Cube than 3D FSPGR, and T1-Cube with Gd enhancement may be superior to 3D FSPGR for detecting smaller metastatic tumors.

著者

Shohei Ouchi Satoshi Ito

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.2019-0139, (Released:2020-07-02)

参考文献数

28

被引用文献数

8

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Purpose: A deep residual learning convolutional neural network (DRL-CNN) was applied to improve image quality and speed up the reconstruction of compressed sensing magnetic resonance imaging. The reconstruction performances of the proposed method was compared with iterative reconstruction methods.Methods: The proposed method adopted a DRL-CNN to learn the residual component between the input and output images (i.e., aliasing artifacts) for image reconstruction. The CNN-based reconstruction was compared with iterative reconstruction methods. To clarify the reconstruction performance of the proposed method, reconstruction experiments using 1D-, 2D-random under-sampling and sampling patterns that mix random and non-random under-sampling were executed. The peak-signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) were examined for various numbers of training images, sampling rates, and numbers of training epochs.Results: The experimental results demonstrated that reconstruction time is drastically reduced to 0.022 s per image compared with that for conventional iterative reconstruction. The PSNR and SSIM were improved as the coherence of the sampling pattern increases. These results indicate that a deep CNN can learn coherent artifacts and is effective especially for cases where the randomness of k-space sampling is rather low. Simulation studies showed that variable density non-random under-sampling was a promising sampling pattern in 1D-random under-sampling of 2D image acquisition.Conclusion: A DRL-CNN can recognize and predict aliasing artifacts with low incoherence. It was demonstrated that reconstruction time is significantly reduced and the improvement in the PSNR and SSIM is higher in 1D-random under-sampling than in 2D. The requirement of incoherence for aliasing artifacts is different from that for iterative reconstruction.

著者

Hisayoshi Araki Takeshi Yoshizako Rika Yoshida Mitsunari Maruyama Noriyoshi Ishikawa Hajime Kitagaki

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.2019-0028, (Released:2019-05-07)

参考文献数

2

被引用文献数

1

著者

Stéren Chabert Jorge Verdu Gamaliel Huerta Cristian Montalba Pablo Cox Rodrigo Riveros Sergio Uribe Rodrigo Salas Alejandro Veloz

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.2019-0061, (Released:2019-10-15)

参考文献数

44

被引用文献数

12

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Purpose: Intravoxel incoherent motion (IVIM) analysis has attracted the interest of the clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for its implementation; one of the questions being which b-value distribution to use. This study aimed to stress the importance of the sampling scheme and to show that an optimized b-value distribution decreases the variance associated with IVIM parameters in the brain with respect to a regular distribution in healthy volunteers.Methods: Ten volunteers were included in this study; images were acquired on a 1.5T MR scanner. Two distributions of 16 b-values were used: one considered ‘regular’ due to its close association with that used in other studies, and the other considered ‘optimized’ according to previous studies. IVIM parameters were adjusted according to the bi-exponential model, using two-step method. Analysis was undertaken in ROI defined using in the Automated Anatomical Labeling atlas, and parameters distributions were compared in a total of 832 ROI.Results: Maps with fewer speckles were obtained with the ‘optimized’ distribution. Coefficients of variation did not change significantly for the estimation of the diffusion coefficient D but decreased by approximately 39% for the pseudo-diffusion coefficient estimation and by 21% for the perfusion fraction. Distributions of adjusted parameters were found significantly different in 50% of the cases for the perfusion fraction, in 80% of the cases for the pseudo-diffusion coefficient and 17% of the cases for the diffusion coefficient. Observations across brain areas show that the range of average values for IVIM parameters is smaller in the ‘optimized’ case.Conclusion: Using an optimized distribution, data are sampled in a way that the IVIM signal decay is better described and less variance is obtained in the fitted parameters. The increased precision gained could help to detect small variations in IVIM parameters.

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

Masami YONEYAMA Taro TAKAHARA Thomas C. KWEE Masanobu NAKAMURA Takashi TABUCHI

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.2012-0063, (Released:2013-05-10)

被引用文献数

28 68

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Purpose: To introduce, optimize, and assess the feasibility of a new scheme to rapidly acquire high-resolution volumetric neurographic images using a three-dimensional turbo spin-echo sequence combined with a diffusion-weighted pre-pulse called improved motion-sensitized driven equilibrium (iMSDE): Diffusion-prepared MR Neurography (D-prep MRN). Methods: In order to optimize the signal suppression of blood vessels and muscle at D-prep MRN, coronal lumbosacral plexus images were acquired in five volunteers at 3T, and the following parameters were examined: iMSDE gradient-strength (b-value) of 0, 2 and 10 s/mm2 (with the aim to suppress blood vessels) and iMSDE preparation duration (iMSDEprep-time) of 18, 50 and 100 ms (with the aim to suppress muscle signal). Subsequently, the feasibility of the optimized D-prep MRN sequence in visualizing the brachial plexus, lumbosacral plexus, and cranial nerves was evaluated in 5 healthy volunteers. Results: A higher b-value of 10 s/mm2 was better in signal suppression of blood vessels, whereas an intermediate iMSDEprep-time of 50 ms provided the best compromise between suppression of muscle signal and minimization of signal loss of nerves. With these parameters, the normal nerve structures showed high signal intensity, while the blood vessels and muscles were effectively suppressed. The optimized D-prep MRN sequence clearly showed the three-dimensional trajectory of the brachial plexus, lumbosacral plexus, and cranial nerves. Conclusion: D-prep MRN was introduced and optimized, and clearly showed detailed anatomy of the brachial plexus, lumbosacral plexus, and cranial nerves. These results suggest that the D-prep MRN can be used for fast, high-resolution, volumetric imaging of the peripheral nervous system.