Evaluation of motion artefact reduction depending on the artefacts' directions in head MRI using conditional generative adversarial networks.

Motion artefacts caused by the patient's body movements affect magnetic resonance imaging (MRI) accuracy. This study aimed to compare and evaluate the accuracy of motion artefacts correction using a conditional generative adversarial network (CGAN) with an autoencoder and U-net models. The training dataset consisted of motion artefacts generated through simulations. Motion artefacts occur in the phase encoding direction, which is set to either the horizontal or vertical direction of the image. To create T2-weighted axial images with simulated motion artefacts, 5500 head images were used in each direction. Of these data, 90% were used for training, while the remainder were used for the evaluation of image quality. Moreover, the validation data used in the model training consisted of 10% of the training dataset. The training data were divided into horizontal and vertical directions of motion artefact appearance, and the effect of combining this data with the training dataset was verified. The resulting corrected images were evaluated using structural image similarity (SSIM) and peak signal-to-noise ratio (PSNR), and the metrics were compared with the images without motion artefacts. The best improvements in the SSIM and PSNR were observed in the consistent condition in the direction of the occurrence of motion artefacts in the training and evaluation datasets. However, SSIM > 0.9 and PSNR > 29 dB were accomplished for the learning model with both image directions. The latter model exhibited the highest robustness for actual patient motion in head MRI images. Moreover, the image quality of the corrected image with the CGAN was the closest to that of the original image, while the improvement rates for SSIM and PSNR were approximately 26% and 7.7%, respectively. The CGAN model demonstrated a high image reproducibility, and the most significant model was the consistent condition of the learning model and the direction of the appearance of motion artefacts.

[Effect of Contrast on FLAIR by Changing the Number of Packages].

We have found that the number of packages influences contrast for brain tissue signals on fluid-attenuated inversion recovery (FLAIR). The purpose of this study was to evaluate the contrast of white and gray matters by changing the number of packages. In a volunteer study (n=8), FLAIR images were obtained with the various number of packages (number of package=2, 3, 4, 5). We investigated the same imaging condition at both 1.5 and 3.0T. The signal intensity of white and gray matters in all volunteers was increased as increasing the number of packages. Moreover, the contrast ratio between white and gray matters was slightly decreased. In our conclusion, the contrast between the gray and white matters on FLAIR was influenced by the number of packages.

[The Effect of Scan Parameters on the Synthetic MRI].

Synthetic MRI can provide proton density (PD), T1 value, T2 value for each pixel by only one data acquisition and can create various contrast-weighted images. The aim of this study is to evaluate the effect on the calculation of the T1·T2 value when changing the scan parameters for synthetic MRI. In the phantom study, when changing 1st TE/2nd TE/TR/TSE factor, the effect on the T1·T2 value calculated by synthetic MRI was examined. In the volunteer study, the brain was imaged and compared with known T1·T2 value. In phantom study, the effect on the T2 value by the 1st TE/2nd TE/TSE factor was shown. In volunteer study, there was no problem in the calculated value of brain parenchyma. However, the T2 value of cerebrospinal fluid had the error of known value. The results show that it is necessary to set appropriate scan parameters on synthetic MRI.

Dynamic cross-sectional changes of the middle cerebral artery in atherosclerotic stenosis detected by 3·0-Tesla MRI.

OBJECTIVES: Atherosclerotic stenosis of the middle cerebral artery (MCA) is one of the causes of ischemic stroke, but aside from investigations using magnetic resonance angiography (MRA), studies evaluating stenosis are rare. The purpose of this study was to assess dynamic changes of MCA cross section between the systolic and diastolic phases in patients with cerebral infarction using 3·0-Tesla magnetic resonance imaging (3T MRI). METHODS: We assessed 12 stroke patients with M1 stenosis in the MCA and 12 healthy volunteers. We measured MCA cross sections (proximal/distal to stenosis and on the stenosis) in the systolic and diastolic phases by synchronizing imaging with heartbeats, as well as the maximum flow velocity by using cine-phase contrast (PC) MRI. Each patient also underwent conventional MRA. RESULTS: Differences in cross sections between systolic and diastolic phases were significantly smaller in the stenosed artery compared to the distal (P < 0·05) and proximal areas (P < 0·01) in stroke patients. The difference in maximal blood velocity between systolic and diastolic phases at the M1 stenosis was significantly larger than that in the area proximal to the stenosis (P < 0·05). DISCUSSION: We clearly demonstrated dynamic cross-sectional changes in the stenotic areas by 3T MRI, suggesting hemodynamic shear stress, which may further enhance MCA atherosclerosis.