髙野 一輝

AIM: High breast density increases breast cancer risk and decreases mammographic sensitivity, yet prior magnetic resonance imaging (MRI)-based density assessments have relied on specialized sequences or vendor-dependent software. This study aimed to evaluate a semiautomated workflow for MRI-based breast density measurement using routinely acquired MRI sequences and compare its diagnostic performance and concordance with visual and software-based mammographic classifications. MATERIALS AND METHODS: In this single-center retrospective study, we analyzed 113 women (mean age: 60.8 ± 13.6 years) who underwent both breast MRI and mammography. The contralateral, normal breast was evaluated. MRI was performed using a 3.0-T scanner with a 3D T1-weighted gradient-echo sequence. Breast and fibroglandular tissues were segmented using a deep learning-assisted automated method with manual correction. Statistical analyses included correlation analysis, Bland-Altman analysis, and receiver operating characteristic (ROC) analysis. RESULTS: MRI-based breast density showed a strong correlation with mammographic density (r = 0.781, P < 0.001), although MRI values were on average 27.7% lower. The area under the ROC curve for detecting high-density breasts was 0.869 for MRI and 0.799 for mammography (P = 0.085). CONCLUSIONS: Semiautomated MRI-based breast density assessment using routine MRI sequences allowed for quantitative evaluation of breast composition without using ionizing radiation. MRI-based density measurements exhibited classification performance comparable to mammographic assessment. These findings support the potential utility of MRI for quantitative breast density evaluation, although further validation and workflow standardization are required before broader clinical application.

This study evaluated the feasibility of generative adversarial network (GAN)-based postprocessing super-resolution for T2-weighted lumbar spine magnetic resonance imaging (MRI) using both objective resolution metrics and perceptual assessment. Sagittal lumbar spine MRI datasets from healthy volunteers were analyzed. An enhanced super-resolution GAN (ESRGAN) was trained on downsampled images, while zero-filling, bicubic, and bilinear interpolation, as well as enhanced deep residual networks for single image super-resolution (EDSR), were used as comparators. Image quality was assessed by comparing upscaled images from 256 × 256 acquisitions with corresponding 512 × 512 reference images, using full width at half maximum (FWHM) and normalized integrated power spectrum (NIPS), along with similarity metrics including structural similarity index, peak signal-to-noise ratio, and root mean square error. Subjective image quality was evaluated using a ranking method. An additional analysis using 320 × 320 and 640 × 640 image pairs was conducted to assess consistency across resolution settings. Statistical comparisons were performed using the Friedman test with Bonferroni correction. ESRGAN showed no significant differences from the original high-resolution images in FWHM and NIPS, whereas interpolation methods and EDSR demonstrated inferior performance (P < 0.05). Although interpolation methods achieved higher scores in pixel-wise metrics, ESRGAN obtained the highest subjective ratings and interrater agreement. These findings indicate that ESRGAN better restores high-frequency structural information not captured by conventional similarity metrics. GAN-based postprocessing super-resolution may improve image quality in lumbar spine MRI and warrants further investigation of its clinical impact.

OBJECTIVES: Insulin-derived amyloidosis (IDA) reduces insulin absorption, increasing the risk of poor glycemic control; however, early detection remains challenging. Although ultrasound can identify IDA, the effectiveness of portable pocket-sized devices has not been evaluated. This study was performed to evaluate the detectability of findings suggestive of IDA using a pocket-sized ultrasound device on the abdominal region, the most common site for stable insulin absorption in patients with diabetes mellitus. METHODS: This cross-sectional observational study was conducted in the diabetes ward of a university hospital between July and December 2024. The participants were inpatients with diabetes who had been receiving insulin injections for more than 1 month. Findings suggestive of IDA were assessed through visual inspection and palpation, magnetic resonance imaging (MRI), high-performance ultrasonography, and a pocket-sized ultrasound device. The concordance rate between the pocket-sized ultrasound device and other assessment methods was calculated. RESULTS: Of the 20 participants enrolled, 5 met the exclusion criteria; thus, data from 15 participants were included in the final analysis. Findings suggestive of IDA were identified in nine patients (60.0%) by visual inspection and palpation and in eight patients (53.3%) by the pocket-sized ultrasound device, high-performance ultrasound, and MRI. The concordance rate among the pocket-sized ultrasound device, high-performance ultrasound, and MRI was 100%, while the concordance rate between visual inspection with palpation and the pocket-sized ultrasound device was 80%. CONCLUSIONS: These findings suggest that a pocket-sized ultrasound device has the potential to detect findings suggestive of IDA and may be suitable for use in clinical practice.

PURPOSE: This study aimed to characterize quantitative liver-spleen contrast (Q-LSC) and hepatocellular uptake index (HUI) for evaluating hepatobiliary phase (HBP) images using gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) in liver magnetic resonance imaging and to identify differences in the results obtain from these two measurement methods. METHODS: Twenty-nine consecutive randomly selected patients were assessed using the 3.0 T MR system. Three regions of interest (ROI) were set for the liver parenchyma and spleen, and signal intensity (SI) was averaged. Q-LSC (SI of the liver divided by the SI of the spleen) and HUI [(Q-LSC-1) × liver volume] were calculated. Moreover, the volume and mean SI of the whole liver and spleen, left lateral segment (LLS), and the other segments were calculated. Subsequently, ROI-based and volume-based values for Q-LSC (R-LSC and V-LSC) and HUI (R-HUI and V-HUI), and the whole and each segment were compared. RESULTS: R-LSC and V-LSC for the whole and each segment were not significantly different. Conversely, all combinations of HUI, except between R-HUI and V-HUI were significantly different (P < 0.01), for the whole liver. Correlations between R-LSC, R-HUI, and volume-based LLS were lower than the others. CONCLUSION: Q-LSC and HUI were characterized through an imaging evaluation of HBP with Gd-EOB-DTPA. R-LSC and R-HUI, or V-HUI, of the whole liver were strongly correlated, but the LLS affected the data, and HUI depends on liver volume. R-LSC is simple and easy to use for partial image evaluation.

Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of the elderly, leading to increased falls, hospitalisation, and mortality rates. Muscle quality, reflecting microscopic and macroscopic muscle changes, is a critical determinant of physical function. To utilise radiomic features extracted from magnetic resonance (MR) images to assess age-related changes in muscle quality, a dataset of 24 adults, divided into older (male/female: 6/6, 66-79 years) and younger (male/female: 6/6, 21-31 years) groups, was used to investigate the radiomics features of the dorsiflexor and plantar flexor muscles of the lower leg that are critical for mobility. MR images were processed using MaZda software for feature extraction. Dimensionality reduction was performed using principal component analysis and recursive feature elimination, followed by classification using machine learning models, such as support vector machine, extreme gradient boosting, and naïve Bayes. A leave-one-out validation test was used to train and test the classifiers, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance. The analysis revealed that significant differences in radiomic feature distributions were found between age groups, with older adults showing higher complexity and variability in muscle texture. The plantar flexors showed similar or higher AUC than the dorsiflexors in all models. When the dorsiflexor muscles were combined with the plantar flexor muscles, they tended to have a higher AUC than when they were used alone. Radiomic features in lower-leg MR images reflect ageing, especially in the plantar flexor muscles. Radiomic analysis can offer a deeper understanding of age-related muscle quality than traditional muscle mass assessments.