Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: The female sex is reported to have a higher risk of adverse events (AEs) from cytotoxic chemotherapy. Few studies examined the sex differences in AEs and their impact on the use of medical services during adjuvant chemotherapy. This sub-study aimed to compare the incidence of any grade and grade ≥ 3 AEs, healthcare utilization, chemotherapy completion rate, and dose intensity according to sex. Materials and Methods: This is a sub-study of a multicenter cohort conducted in Korea that evaluated the impact of healthcare reimbursement on AE evaluation in patients who received adjuvant chemotherapy between September 2013 and December 2016 at four hospitals in Korea. Results: A total of 1,170 patients with colorectal, gastric, or non–small cell lung cancer were included in the study. Female patients were younger, had fewer comorbidities, and experienced less postoperative weight loss of > 10%. Females had significantly higher rates of any grade AEs including nausea, abdominal pain, stomatitis, vomiting, and neutropenia, and experienced more grade ≥ 3 neutropenia, nausea, and vomiting. The dose intensity of chemotherapy was significantly lower in females, and they also experienced more frequent dose reduction after the first cycle. Moreover, female patients receiving platinum-containing regimens had significantly higher rates of unscheduled outpatient visits. Conclusion Our study found that females experienced a higher incidence of multiple any-grade AEs and severe neutropenia, nausea, and vomiting, across various cancer types, leading to more frequent dose reductions. Physicians should be aware of sex differences in AEs for chemotherapy decisions.

Background: The management of dental plaque and the appropriate use of oral hygiene tools are crucial for oral health. Withincreasing interest in oral health care, various oral hygiene products are available on the market. As the most basic oral hygiene tool, the large-head toothbrush has gained significant consumer interest. This study evaluates the suitability of large-head toothbrushes as oral hygiene tools by comparing their efficacy with that of regular-sized toothbrushes. Methods: We evaluated the efficacy of large-head and regular-sized toothbrushes. The amount of dental plaque before and afterbrushing was measured using quantitative light-induced fluorescence digital (QLF-D) images and the plaque index (PI) in adult participants with more than 28 teeth (n=15/group). After brushing, the collected toothbrushes were rinsed 10 times in clean water, thoroughly dried, and weighed on an electronic scale. Data were analyzed using the Wilcoxon signed-rank test with SPSS Statistics Version 29.0. Results: QLF-D analysis showed a significant decrease in fluorescence intensity scores after use of a regular-sized toothbrush,whereas the scores after use of a large-head toothbrush were not significantly reduced. PI measurements indicated significant reductions in dental plaque for both head types. However, detailed analysis by tooth surface revealed that the regular-sized toothbrush significantly reduced plaque on the proximal, palatal/lingual, and labial/buccal surfaces. In contrast, the large-head toothbrush only showed significant reductions in place on the proximal and palatal/lingual surfaces, not on the labial/buccal surface. Additionally, compared to the regular-sized toothbrush, the large-head toothbrush retained more toothpaste residue despite similar cleaning efforts after brushing. Conclusion Compared to the regular-sized toothbrush, the large-head toothbrush showed lower overall efficacy in dentalplaque removal. The results of this study provide significant information regarding the use of large-head toothbrushes.

Objective: We intended to investigate the relationships between visual sensory impairment (VSI) or auditory sensory impairment (ASI) and brain pathological changes associated with cognitive decline in older adults. Methods: We primarily tried to examine whether each sensory impairment is related to Alzheimer’s disease (AD) pathology, specifically beta-amyloid (Aβ) deposition, through both cross-sectional and longitudinal approaches in cognitively unimpaired older adults. Self-report questionnaires on vision and hearing status were administered at the baseline.Neuroimaging scans including brain [ 11 C] Pittsburgh Compound B PET and MRI, as well as clinical assessments, were performed at baseline and 2-year follow-up. Results: Cross-sectional analyses showed that the VSI-positive group had significantly higher Aβ deposition than the VSI-negative group, whereas there was no significant association between ASI positivity and Aβ deposition. Longitudinal analyses revealed that VSI positivity at baseline was significantly associated with increased Aβ deposition over 2 years (β = 0.153, p = 0.025), although ASI positivity was not (β = 0.045, p = 0.518). VSI positivity at baseline was also significantly associated with greater atrophic changes in AD-related brain regions over the 2-year follow-up period (β = −0.207, p = 0.005), whereas ASI positivity was not (β = 0.024, p = 0.753). Neither VSI nor ASI positivity was related to cerebrovascular injury, as measured based on the white matter hyperintensity volume. Conclusion The findings suggest that VSI is probably related to AD-specific pathological changes, which possibly mediate the reported relationship between VSI and cognitive decline. In contrast, ASI appears not associated with AD pathologies but may contribute to cognitive decline via other mechanisms.

Objective: Previous studies have reported that vitamin D deficiency increased the risk of Alzheimer’s disease (AD) dementia in older adults. However, little is known about how vitamin D is involved in the pathophysiology of AD. Thus, this study aimed to examine the association and interaction of serum vitamin D levels with in vivo AD pathologies including cerebral beta-amyloid (Aβ) deposition and neurodegeneration in nondemented older adults. Methods: 428 Nondemented older adults were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease, a prospective cohort that began in 2014. All participants underwent comprehensive clinical assessments, measurement of serum 25-hydroxyvitamin D (25[OH]D), and multimodal brain imaging including Pittsburgh compound B (PiB) positron emission tomography and magnetic resonance imaging. Global PiB deposition was measured for the Aβ biomarker. Intracranial volume-adjusted hippocampal volume (HVa) was used as a neurodegeneration biomarker. Results: Overall, serum 25(OH)D level was not associated with either Aβ deposition or HVa after controlling for age, sex, apolipoprotein E ε4 positivity, and vascular risk factors. However, serum 25(OH)D level had a significant moderating effect on the association between Aβ and neurodegeneration, with lower serum 25(OH)D level significantly exacerbating cerebral Aβ-associated hippocampal volume loss (B = 34.612, p = 0.008). Conclusion Our findings indicate that lower serum vitamin D levels may contribute to AD by exacerbating Aβ-associated neurodegeneration in nondemented older adults. Further studies to explore the potential therapeutic effect of vitamin D supplementation on the progression of AD pathology will be necessary.

Objective: We intended to investigate the relationships between visual sensory impairment (VSI) or auditory sensory impairment (ASI) and brain pathological changes associated with cognitive decline in older adults. Methods: We primarily tried to examine whether each sensory impairment is related to Alzheimer’s disease (AD) pathology, specifically beta-amyloid (Aβ) deposition, through both cross-sectional and longitudinal approaches in cognitively unimpaired older adults. Self-report questionnaires on vision and hearing status were administered at the baseline.Neuroimaging scans including brain [ 11 C] Pittsburgh Compound B PET and MRI, as well as clinical assessments, were performed at baseline and 2-year follow-up. Results: Cross-sectional analyses showed that the VSI-positive group had significantly higher Aβ deposition than the VSI-negative group, whereas there was no significant association between ASI positivity and Aβ deposition. Longitudinal analyses revealed that VSI positivity at baseline was significantly associated with increased Aβ deposition over 2 years (β = 0.153, p = 0.025), although ASI positivity was not (β = 0.045, p = 0.518). VSI positivity at baseline was also significantly associated with greater atrophic changes in AD-related brain regions over the 2-year follow-up period (β = −0.207, p = 0.005), whereas ASI positivity was not (β = 0.024, p = 0.753). Neither VSI nor ASI positivity was related to cerebrovascular injury, as measured based on the white matter hyperintensity volume. Conclusion The findings suggest that VSI is probably related to AD-specific pathological changes, which possibly mediate the reported relationship between VSI and cognitive decline. In contrast, ASI appears not associated with AD pathologies but may contribute to cognitive decline via other mechanisms.