Purpose: In order to detect exercised muscles by the increase in T₂, we have defined a Gaussian T₂ distribution and reference values (T_2r and SD_r) in resting state muscles, and a threshold for detecting exercised muscles.

Methods: The subjects were healthy adult volunteers (14 males and 12 females). Multiple-spin-echo (MSE) MR images were obtained with 10 TE values from 10 to 100 ms using a 0.2T MRI system. T₂ values for 10 forearm muscles were obtained in the resting state and after isometric wrist flexion exercise with 5%, 15%, and 25% of the maximum voluntary contraction (MVC). Z values were obtained by (T_2e − T_2r)/SD_r, where T_2e was T₂ after exercise. Based on sample size calculations, three thresholds (Z_T = 1.00, 2.56, and 3.07) were applied to agonist and antagonist muscles.

Results: A normal distribution of T₂ was detected in resting muscles at 34 ± 3 ms (mean ± standard deviation [SD]) in 26 subjects using the Kolmogorov–Smirnov test, the Shapiro–Wilk test, and the Jarque–Bera test (P > 0.05). No gender differences were shown between the T₂ or SD, and a similar result was obtained in 12 measurements on a single subject (P < 0.01). The T_2r and SD_r were used for reference values. The threshold Z_T = 1.00 showed the highest sensitivity (0.86) even with 5% MVC, but it showed a lower specificity (0.85) than the other thresholds. Z_T = 3.07 showed the highest specificity (1.0), but it showed a lower sensitivity (0.36) with the 5% MVC, compared with Z_T = 2.56 (0.50). The receiver operating characteristics analysis also supported these results.

Conclusion: We found that the T₂ distribution in muscles was Gaussian, suggesting that a one-sample t-test can be applied, and that Z_T = 2.56 could cover low-intensity exercise with high specificity and a low false-positive rate.

Purpose: Since the first report on the deposition of gadolinium in the brain parenchyma after repeated intravenous administrations of gadolinium-based contrast agent GBCA (IV-GBCA), the mechanisms of penetration and retention are still remaining a hot topic of discussion and a target of investigation. We routinely obtain endolymphatic hydrops (EH) images at 4 h after IV administration of a single dose (SD) of GBCA (IV-SD-GBCA) using heavily T₂-weighted three-dimensional fluid-attenuated inversion recovery imaging (hT₂W-3D-FLAIR). Occasionally, we have encountered cases, which indicate high-signal intensity (SI) in the cerebrospinal fluid (CSF) surrounding the vein of Labbe. The purpose of the present study was to investigate the degree of contrast enhancement of the CSF surrounding the vein of Labbe on hT₂W-3D-FLAIR after IV-SD-GBCA in comparison with other CSF spaces.

Materials and Methods: In 25 patients with a suspicion of EH, a magnetic resonance cisternography (MRC) and an hT₂W-3D-FLAIR were obtained at 4 h after IV-SD-GBCA. The perivascular space (PVS) in the basal ganglia, CSF spaces in the ambient cistern (CSF-Amb), the CSF surrounding the superficial middle cerebral vein (CSF-SMCV), and the CSF surrounding the vein of Labbe (CSF-VL) were segmented on MRC. The PVS and CSF regions were co-registered onto the hT₂W-3D-FLAIR and the SI of the PVS and CSF spaces were measured. The SI ratio (SIR) of the post-contrast hT₂W-3D-FLAIR to the pre-contrast hT₂W-3D-FLAIR was measured. Significant differences were evaluated using Steel-Dwass’s test for multiple comparisons.

Results: The SIR of the CSF-VL was significantly higher than that of the PVS (P = 0.008), the CSF-Amb (P = 0.021), and the CSF-SMCV (P = 0.023).

Conclusion: The strong contrast enhancement of CSF space around the vein of Labbe was confirmed on hT₂W-3D-FLAIR at 4 h after IV-GBCA compared to the PVS and the other CSF spaces.

Purpose: To assess the impact of the number of iterations of compressed sensing (CS) reconstruction on the kinetic parameters and image quality in dynamic contrast-enhanced (DCE)-MRI of the breast, with prospectively undersampled CS-accelerated scans.

Materials and Methods: Breast examinations including ultrafast DCE-MRI using CS were conducted for 21 patients. Images were reconstructed with different numbers of iterations. The peak enhancement ratio of the aorta and wash-in slope, initial area under the curve, and K^trans of the breast lesions were measured. The root mean square error and structural similarity between the images using 50 iterations and images with a lower number of iterations were evaluated as criterion for quantitative image evaluation.

Results: Using an insufficient number of iterations, the contrast-enhanced effect was highly underestimated. In all semi-quantitative parameters, the number of iterations that stabilized the parameters in malignant lesions was higher than that in benign lesions. At least 15 iterations were needed for semi-quantitative parameters. For K^trans, there were no significant differences between 10 and 50 iterations in both malignant and benign lesions.

Conclusion: The kinetic parameters using ultrafast DCE-MRI with CS are affected by the number of iterations, especially in malignant lesions. However, if the images are reconstructed with an adequate number of iterations, ultrafast DCE-MRI with CS can be a powerful technique having high temporal and spatial resolution.

Purpose: To develop a fast 3D MRI simulator for arbitrary k-space sampling using a graphical processing unit (GPU) and demonstrate its performance by comparing simulation and experimental results in a real MRI system.

Materials and Methods: A fast 3D MRI simulator using a GeForce GTX 1080 GPU (NVIDIA Corporation, Santa Clara, CA, USA) was developed using C++ and the CUDA 8.0 platform (NVIDIA Corporation). The unique advantage of this simulator was that it could use the same pulse sequence as used in the experiment. The performance of the MRI simulator was measured using two GTX 1080 GPUs and 3D Cones sequences. The MRI simulation results for 3D non-Cartesian sampling trajectories like 3D Cones sequences using a numerical 3D phantom were compared with the experimental results obtained with a real MRI system and a real 3D phantom.

Results: The performance of the MRI simulator was about 3800–4900 gigaflops for 128- to 4-shot 3D Cones sequences with 256³ voxels, which was about 60% of the performance of the previous MRI simulator optimized for Cartesian sampling calculated for a Cartesian sampling gradient-echo sequence with 256³ voxels. The effects of the static magnetic field inhomogeneity, radio-frequency field inhomogeneity, gradient field nonlinearity, and fast repetition times on the MR images were reproduced in the simulated images as observed in the experimental images.

Conclusion: The 3D MRI simulator developed for arbitrary k-space sampling optimized using GPUs is a powerful tool for the development and evaluation of advanced imaging sequences including both Cartesian and non-Cartesian k-space sampling.

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.

Background: Diffusion-weighted imaging (DWI) is useful for detecting and characterizing liver lesions but is sensitive to organ motion artifact, especially in the left lobe.

Purpose: To assess the signal intensity (SI) loss in the left hepatic lobe on DWI depending on motion-proving gradient (MPG) pulse direction (preliminary study) and to evaluate the usefulness of modified signal averaging to reduce the SI loss on DWI (application study).

Methods: About 48 (preliminary) and 35 (application) patients were included. In the preliminary study, DWI with four different MPG directions, only a single MPG pulse direction (x-, y-, or z-axis) and all three directions combined (standard DWI), were reconstructed from the original data. In the application study, we examined the usefulness of the weighted averaging number of excitations (wNEX) method, in which a larger weighting factor is applied to the higher signal in pixel-by-pixel NEX signal averaging by comparing four reconstruction methods. We assumed that true signals would be the same in both lobes. The SI and apparent diffusion coefficient (ADC) ratios for the left versus right lobe were calculated by dividing the SI/ADC of the right lobe by that of the left lobe.

Results: In the preliminary study, the SI ratio was significantly lower on DWI using only the x-axis but was significantly higher on DWI using only the z-axis (both P < 0.0001) when compared with standard DWI. In the application study, the SI (mean, 1.15–1.17) and ADC (0.90–0.92) ratios on DWI with wNEX were closer to 1.0 than those on standard DWI (SI ratio, 1.32–1.38; ADC ratio 0.80–0.81); the differences were significant (all P < 0.0001).

Conclusion: The MPG pulse along the z-axis caused signal loss in the left hepatic lobe. The wNEX reconstruction method effectively reduced signal loss in the left lobe on DWI.

Computed DWI (cDWI) is a mathematical technique that calculates arbitrary higher b value images from at least two different lower b values. In addition, the removal of high intensity noise with image processing on cDWI could improve cholesteatoma-background contrast-to-noise ratio (CNR). In the present study, noise reduction was performed by the cut-off values of apparent diffusion coefficient (ADC) less than 0 and 0.4 × 10⁻³ s/mm². The cholesteatoma to non-cholesteatoma CNR was increased using a noise reduction algorithm for clinical setting.

Glycogen-rich clear cell carcinoma (GRCC) of the breast is a rare malignant tumor. Most previous reports focused on clinicopathologic findings of GRCC and imaging findings were not precisely described. Here, we report imaging findings of three cases of GRCC along with a literature review. GRCC of the breast was depicted as a mass with irregular or oval shape on mammography and complex cystic and solid composition or focal cystic change on ultrasound. GRCC showed internal high signal intensity on T₂-weighted MRI with rim enhancement after contrast injection. These might suggest the possibility of GRCC in differentiating breast tumors.