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.

Background and Objectives: The temporal structure of sound, characterized by regular patterns, plays a crucial role in optimizing the processing of auditory information. The meter, representing a well-organized sequence of evenly spaced beats in music, exhibits a hierarchical arrangement, with stronger beats occupying higher metrical positions. Moreover, the meter has been shown to influence behavioral and neural processing, particularly the N1, P2, and mismatch negativity components. However, the role of the P1 component in the context of metrical hierarchy remains unexplored. This study aimed to investigate the effects of metrical hierarchy on the P1 component and compare the responses between musicians and non-musicians. Subjects and Methods: Thirty participants (15 musicians and 15 non-musicians) were enrolled in the study. Auditory stimuli consisted of a synthesized speech syllable presented together with a repeating series of four tones, establishing a quadruple meter. Electrophysiological recordings were performed to measure the P1 component. Results: The results revealed that metrical position had a significant effect on P1 amplitude, with the strongest beat showing the lowest amplitude. This contrasts with previous findings, in which enhanced P1 responses were typically observed at on-the-beat positions. The reduced P1 response on the strong beat can be interpreted within the framework of predictive coding and temporal prediction, where a higher predictability of pitch changes at the strong beat leads to a reduction in the P1 response. Furthermore, higher P1 amplitudes were observed in musicians compared to non-musicians, suggesting that musicians have enhanced sensory processing. Conclusions This study demonstrates the effects of metrical hierarchy on the P1 component, thereby enriching our understanding of auditory processing. The results suggest that predictive coding and temporal prediction play important roles in shaping sensory processing. Further, they suggest that musical training may enhance P1 responses.

BACKGROUND AND PURPOSE: We investigated predictors of institutionalization in patients with Alzheimer's disease (AD) in South Korea. METHODS: In total, 2,470 patients with AD aged 74.5±7.8 years (mean±standard deviation, 68.1% females) were enrolled from November 2005 to December 2013. The dates of institutionalization were identified from the public Long-Term-Care Insurance program in January 2014. We used a Cox proportional-hazards model to identify predictors for future institutionalization among characteristics at the time of diagnosis in 2,470 AD patients. A similar Cox proportional-hazards model was also used to investigate predictors among variables that reflected longitudinal changes in clinical variables before institutionalization in 816 patients who underwent follow-up testing. RESULTS: A lower Mini Mental State Examination score [hazard ratio (HR)=0.95, 95% confidence interval (CI)=0.92–0.97] and higher scores for the Clinical Dementia Rating and Neuro-Psychiatric Inventory (HR=1.01, 95% CI=1.00–1.01) at baseline were independent predictors of institutionalization. The relationship of patients with their main caregivers, presence of the apolipoprotein E e4 allele, and medication at baseline were not significantly associated with the rate of institutionalization. In models with variables that exhibited longitudinal changes, larger annual change in Clinical Dementia Rating Sum of Boxes score (HR=1.15, 95% CI=1.06–1.23) and higher medication possession ratio of antipsychotics (HR=1.89, 95% CI=1.20–2.97) predicted earlier institutionalization. CONCLUSIONS: This study shows that among Korean patients with AD, lower cognitive ability, higher dementia severity, more-severe behavioral symptoms at baseline, more-rapid decline in dementia severity, and more-frequent use of antipsychotics are independent predictors of earlier institutionalization.
Alleles ; Alzheimer Disease* ; Antipsychotic Agents ; Apolipoproteins ; Behavioral Symptoms ; Caregivers ; Dementia ; Diagnosis ; Follow-Up Studies ; Humans ; Institutionalization* ; Insurance ; Korea*

BACKGROUND AND OBJECTIVES: Cochlear dead region (CDR) is a region in the cochlear where hearing loss has occurred due to damage to the inner hair cells and/or neurons. Recently, a subjective test involving a pure-tone test in the presence of threshold-equalizing noise (TEN) was introduced to identify CDR. However, for uncooperative patients, such a subjective method would be unsuitable and objective methods would be needed instead to detect CDR. The acoustic change complex (ACC) is an evoked potential elicited by changes in the ongoing sound. In this study, we developed an objective method of identifying CDR by combining ACC response with a TEN test, namely the TEN-ACC test, and investigated its feasibility in normal-hearing listeners. SUBJECTS AND METHOD: Ten normal-hearing subjects participated in this study. All subjects underwent both behavioral TEN test and electrophysiological TEN-ACC test. The stimuli for the TEN-ACC test consisted of TEN and embedded pure tones with different frequencies/signals to noise ratios (SNRs). To identify the thresholds, the range SNR of stimulation was varied from 0 to 20 dB, in stages of 4 dB. RESULTS: The ACC responses of all subjects who participated in this study were well elicited by stimuli developed for the TEN-ACC test. We confirm that the pure-tones embedded in TEN elicited the objective ACC response. CONCLUSION: The results of this study suggest that the novel TEN-ACC test can be applied to evoke ACC in normal-hearing listeners. Future research should incorporate hearing-impaired listeners to determine the feasibility of the TEN-ACC test as an objective method to identify CDR.
Acoustics* ; Evoked Potentials ; Hair Cells, Auditory, Inner ; Hearing Loss ; Humans ; Methods ; Neurons ; Noise