髙野 一輝

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.

OBJECTIVES: To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD). MATERIALS AND METHODS: This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups. RESULTS: A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002). CONCLUSION: Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD. CLINICAL RELEVANCE STATEMENT: The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD. KEY POINTS: Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.