PURPOSE: We examined the effect of stress on the pathophysiology of bladder stability in terms of enzyme levels, Rho-kinase, and bladder relaxation. MATERIALS AND METHODS: A total of 48 female Sprague-Dawley rats were studied in scheduled stress environments for 7, 14, and 28 days; 24 rats were in the control group and 24 rats were in the test (stressed) group. RESULTS: Estrogen decreased significantly whereas testosterone and dopamine increased significantly in the stress group (p<0.05). Rho-kinase was significantly increased in the rats exposed to stress stimuli for 14 days (p<0.05). Collagen types I and III in the bladder tissue were significantly higher in rats exposed to stress for 14 days and 28 days (collagen type I in the 14-day group, p<0.01; collagen type I in the 28-day group, p<0.05; collagen type III in the 14-day and 28-day groups, p<0.05). Voiding frequency increased significantly as the duration of stress exposure was prolonged, in addition to a significant decrease in volume per voiding (p<0.05). CONCLUSIONS: The changes observed in micturition pattern, factors that contribute to smooth muscle contraction, and relaxation in the female rat bladder support the hypothesis that stress affects bladder stability.
Animals ; Collagen ; Collagen Type I ; Collagen Type III ; Contracts ; Dopamine ; Estrogens ; Female ; Humans ; Muscle, Smooth ; Rats ; Rats, Sprague-Dawley ; Relaxation ; rho-Associated Kinases ; Testosterone ; Urinary Bladder ; Urination

OBJECTIVE: To prospectively compare technical success rate and reliable measurements of virtual touch quantification (VTQ) elastography and elastography point quantification (ElastPQ), and to correlate liver stiffness (LS) measurements obtained by the two elastography techniques. MATERIALS AND METHODS: Our study included 85 patients, 80 of whom were previously diagnosed with chronic liver disease. The technical success rate and reliable measurements of the two kinds of point shear wave elastography (pSWE) techniques were compared by χ2 analysis. LS values measured using the two techniques were compared and correlated via Wilcoxon signed-rank test, Spearman correlation coefficient, and 95% Bland-Altman limit of agreement. The intraobserver reproducibility of ElastPQ was determined by 95% Bland-Altman limit of agreement and intraclass correlation coefficient (ICC). RESULTS: The two pSWE techniques showed similar technical success rate (98.8% for VTQ vs. 95.3% for ElastPQ, p = 0.823) and reliable LS measurements (95.3% for VTQ vs. 90.6% for ElastPQ, p = 0.509). The mean LS measurements obtained by VTQ (1.71 ± 0.47 m/s) and ElastPQ (1.66 ± 0.41 m/s) were not significantly different (p = 0.209). The LS measurements obtained by the two techniques showed strong correlation (r = 0.820); in addition, the 95% limit of agreement of the two methods was 27.5% of the mean. Finally, the ICC of repeat ElastPQ measurements was 0.991. CONCLUSION: Virtual touch quantification and ElastPQ showed similar technical success rate and reliable measurements, with strongly correlated LS measurements. However, the two methods are not interchangeable due to the large limit of agreement.
Elasticity Imaging Techniques* ; Humans ; Liver Cirrhosis ; Liver Diseases ; Liver* ; Methods* ; Prospective Studies*

OBJECTIVE: To evaluate the diagnostic value of T₂* mapping using 3D multi-echo Dixon gradient echo acquisition on gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) as a tool to evaluate hepatic function. MATERIALS AND METHODS: This retrospective study was approved by the IRB and the requirement of informed consent was waived. 242 patients who underwent liver MRIs, including 3D multi-echo Dixon fast gradient-recalled echo (GRE) sequence at 3T, before and after administration of gadoxetic acid, were included. Based on clinico-laboratory manifestation, the patients were classified as having normal liver function (NLF, n = 50), mild liver damage (MLD, n = 143), or severe liver damage (SLD, n = 30). The 3D multi-echo Dixon GRE sequence was obtained before, and 10 minutes after, gadoxetic acid administration. Pre- and post-contrast T₂* values, as well as T₂* reduction rates, were measured from T₂* maps, and compared among the three groups. RESULTS: There was a significant difference in T₂* reduction rates between the NLF and SLD groups (−0.2 ± 4.9% vs. 5.0 ± 6.9%, p = 0.002), and between the MLD and SLD groups (3.2 ± 6.0% vs. 5.0 ± 6.9%, p = 0.003). However, there was no significant difference in both the pre- and post-contrast T₂* values among different liver function groups (p = 0.735 and 0.131, respectively). A receiver operating characteristic (ROC) curve analysis showed that the area under the ROC curve for using T₂* reduction rates to differentiate the SLD group from the NLF group was 0.74 (95% confidence interval: 0.63–0.83). CONCLUSION: Incorporation of T₂* mapping using 3D multi-echo Dixon GRE sequence in gadoxetic acid-enhanced liver MRI protocol may provide supplemental information for liver function deterioration in patients with SLD.
Ethics Committees, Research ; Humans ; Informed Consent ; Liver ; Liver Cirrhosis ; Magnetic Resonance Imaging* ; Retrospective Studies ; ROC Curve

Objective: This study aimed to investigate the feasibility of using artificial intelligence (AI) to identify normal chest radiography (CXR) from the worklist of radiologists in a health-screening environment. Materials and Methods: This retrospective simulation study was conducted using the CXRs of 5887 adults (mean age ± standard deviation, 55.4 ± 11.8 years; male, 4329) from three health screening centers in South Korea using a commercial AI (Lunit INSIGHT CXR3, version 3.5.8.8). Three board-certified thoracic radiologists reviewed CXR images for referable thoracic abnormalities and grouped the images into those with visible referable abnormalities (identified as abnormal by at least one reader) and those with clearly visible referable abnormalities (identified as abnormal by at least two readers). With AI-based simulated exclusion of normal CXR images, the percentages of normal images sorted and abnormal images erroneously removed were analyzed. Additionally, in a random subsample of 480 patients, the ability to identify visible referable abnormalities was compared among AI-unassisted reading (i.e., all images read by human readers without AI), AI-assisted reading (i.e., all images read by human readers with AI assistance as concurrent readers), and reading with AI triage (i.e., human reading of only those rendered abnormal by AI). Results: Of 5887 CXR images, 405 (6.9%) and 227 (3.9%) contained visible and clearly visible abnormalities, respectively. With AI-based triage, 42.9% (2354/5482) of normal CXR images were removed at the cost of erroneous removal of 3.5% (14/405) and 1.8% (4/227) of CXR images with visible and clearly visible abnormalities, respectively. In the diagnostic performance study, AI triage removed 41.6% (188/452) of normal images from the worklist without missing visible abnormalities and increased the specificity for some readers without decreasing sensitivity. Conclusion This study suggests the feasibility of sorting and removing normal CXRs using AI with a tailored cut-off to increase efficiency and reduce the workload of radiologists.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Diabetic nephropathy (DN) can affect quality of life (QoL) because it requires arduous lifelong management. This study analyzed QoL differences between DN patients and patients with other chronic kidney diseases (CKDs). Methods: The analysis included subjects (n = 1,766) from the KNOW-CKD (Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease) cohort who completed the Kidney Disease Quality of Life Short Form questionnaire. After implementing propensity score matching (PSM) using factors that affect the QoL of DN patients, QoL differences between DN and non-DN participants were examined. Results: Among all DN patients (n = 390), higher QoL scores were found for taller subjects, and lower scores were found for those who were unemployed or unmarried, received Medical Aid, had lower economic status, had higher platelet counts or alkaline phosphatase levels, or used clopidogrel or insulin. After PSM, the 239 matched DN subjects reported significantly lower patient satisfaction (59.9 vs. 64.5, p = 0.02) and general health (35.3 vs. 39.1, p = 0.04) than the 239 non-DN subjects. Scores decreased in both groups during the 5-year follow-up, and the scores in the work status, sexual function, and role-physical domains were lower among DN patients than non-DN patients, though those differences were not statistically significant. Conclusion: Socioeconomic factors of DN were strong risk factors for impaired QoL, as were high platelet, alkaline phosphatase, and clopidogrel and insulin use. Clinicians should keep in mind that the QoL of DN patients might decrease in some domains compared with non-DN CKDs.