• Progress in Medical Physics
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• v.30 no.4
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• pp.139-149
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• 2019

Purpose: With neurodegeneration, the signal intensity of the cerebrospinal fluid (CSF) in the brain increases. The objective of this study was to evaluate chemical exchange saturation transfer (CEST) signals with and without the contribution of CSF signals in elderly human brains using two different 3T magnetic resonance imaging (MRI) sequences Methods: Full CEST signals were acquired in ten subjects (Group I) with a three-dimensional (3D)-segmented gradient-echo echo-planar imaging (EPI) sequence and in ten other subjects (Group II) with a 3D gradient and spin-echo (GRASE) sequence using two different 3T MRI systems. The segmented tissue compartments of gray and white matter were used to mask the CSF signals in the full CEST images. Two sets of magnetization transfer ratio asymmetry (MTR_asym) maps were obtained for each offset frequency in each subject with and without masking the CSF signals (masked and unmasked conditions, respectively) and later compared using paired t-tests. Results: The region-of-interest (ROI)-based analyses showed that the MTR_asym values for both the 3D-segmented gradient-echo EPI and 3D GRASE sequences were altered under the masked condition compared with the unmasked condition at several ROIs and offset frequencies. Conclusions: Depending on the imaging sequence, the MTR_asym values can be overestimated for some areas of the elderly human brain when CSF signals are unmasked. Therefore, it is necessary to develop a method to minimize this overestimation in the case of elderly patients.

• Journal of radiological science and technology
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• v.47 no.1
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• pp.29-37
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• 2024

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.