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.

Noncystic fibrosis bronchiectasis is a chronic respiratory disease that carries high socioeconomic and medical burdens and is caused by diverse respiratory illnesses. To improve clinical outcomes, early recognition, active treatment of exacerbations, and prevention of further exacerbations are essential. However, evidence for the treatment and prevention of acute exacerbation of noncystic fibrosis bronchiectasis, especially in children, is lacking. Therefore, the evidence- and consensus-based guidelines for medical and nonmedical treatment strategies for noncystic fibrosis bronchiectasis in children and adolescents were developed by the Korean Academy of Pediatric Allergy and Respiratory Disease using the methods recommended by the Grading of Recommendations Assessment, Development, and Evaluation working group with evidence published through July 2, 2020. This guideline encompasses evidence-based treatment recommendations as well as expert opinions, addressing crucial aspects of the treatment and management of non-cystic fibrosis bronchiectasis in children. This includes considerations for antibiotics and airway clearance strategies, particularly in areas where evidence may be limited. Large, well-designed, and controlled studies are required to accumulate further evidence of management strategies for noncystic fibrosis bronchiectasis in children and adolescents.

Noncystic fibrosis bronchiectasis is a chronic respiratory disease that carries high socioeconomic and medical burdens and is caused by diverse respiratory illnesses. To improve clinical outcomes, early recognition, active treatment of exacerbations, and prevention of further exacerbations are essential. However, evidence for the treatment and prevention of acute exacerbation of noncystic fibrosis bronchiectasis, especially in children, is lacking. Therefore, the evidence- and consensus-based guidelines for medical and nonmedical treatment strategies for noncystic fibrosis bronchiectasis in children and adolescents were developed by the Korean Academy of Pediatric Allergy and Respiratory Disease using the methods recommended by the Grading of Recommendations Assessment, Development, and Evaluation working group with evidence published through July 2, 2020. This guideline encompasses evidence-based treatment recommendations as well as expert opinions, addressing crucial aspects of the treatment and management of non-cystic fibrosis bronchiectasis in children. This includes considerations for antibiotics and airway clearance strategies, particularly in areas where evidence may be limited. Large, well-designed, and controlled studies are required to accumulate further evidence of management strategies for noncystic fibrosis bronchiectasis in children and adolescents.

Noncystic fibrosis bronchiectasis is a chronic respiratory disease that carries high socioeconomic and medical burdens and is caused by diverse respiratory illnesses. To improve clinical outcomes, early recognition, active treatment of exacerbations, and prevention of further exacerbations are essential. However, evidence for the treatment and prevention of acute exacerbation of noncystic fibrosis bronchiectasis, especially in children, is lacking. Therefore, the evidence- and consensus-based guidelines for medical and nonmedical treatment strategies for noncystic fibrosis bronchiectasis in children and adolescents were developed by the Korean Academy of Pediatric Allergy and Respiratory Disease using the methods recommended by the Grading of Recommendations Assessment, Development, and Evaluation working group with evidence published through July 2, 2020. This guideline encompasses evidence-based treatment recommendations as well as expert opinions, addressing crucial aspects of the treatment and management of non-cystic fibrosis bronchiectasis in children. This includes considerations for antibiotics and airway clearance strategies, particularly in areas where evidence may be limited. Large, well-designed, and controlled studies are required to accumulate further evidence of management strategies for noncystic fibrosis bronchiectasis in children and adolescents.

Noncystic fibrosis bronchiectasis is a chronic respiratory disease that carries high socioeconomic and medical burdens and is caused by diverse respiratory illnesses. To improve clinical outcomes, early recognition, active treatment of exacerbations, and prevention of further exacerbations are essential. However, evidence for the treatment and prevention of acute exacerbation of noncystic fibrosis bronchiectasis, especially in children, is lacking. Therefore, the evidence- and consensus-based guidelines for medical and nonmedical treatment strategies for noncystic fibrosis bronchiectasis in children and adolescents were developed by the Korean Academy of Pediatric Allergy and Respiratory Disease using the methods recommended by the Grading of Recommendations Assessment, Development, and Evaluation working group with evidence published through July 2, 2020. This guideline encompasses evidence-based treatment recommendations as well as expert opinions, addressing crucial aspects of the treatment and management of non-cystic fibrosis bronchiectasis in children. This includes considerations for antibiotics and airway clearance strategies, particularly in areas where evidence may be limited. Large, well-designed, and controlled studies are required to accumulate further evidence of management strategies for noncystic fibrosis bronchiectasis in children and adolescents.

Purpose: Interest in the quality of life (QoL) of patients with inflammatory bowel disease (IBD) has recently increased. Although measurement tools have been devised for IBD in general, there is no specific tool for measuring the QoL of patients with ulcerative colitis (UC). Therefore, we developed a QoL questionnaire specifically for patients with UC. Materials and Methods: The Korean Ulcerative Colitis-Specific Questionnaire (K-UCSQ) was developed through item generation, raw-scale construction, focus group meetings, and multi-center field tests. Two hundred patients with UC were recruited for a field test of the K-UCSQ, and subsequent responses to the Inflammatory Bowel Disease Questionnaire (IBDQ) were also obtained. After performing factor analyses to ensure construct validity, the K-UCSQ was finalized as a four-domain, 28-item questionnaire. Subsequent analyses evaluated the reliability of the K-UCSQ in terms of Cronbach’s alpha, concurrent validity in comparison with the pre-established IBDQ, and predictive validity of the area under the ROC curve (AUC) for clinically relevant QoL outcomes. Results: A Cronbach’s alpha of 0.94 showed excellent reliability. Furthermore, correlation analyses demonstrated the concurrent validity of the K-UCSQ in comparison with the IBDQ. The K-UCSQ also showed high validity in predicting the perceived overall health (AUC of 0.812 vs. 0.797 using the IBDQ) and past 2-week QoL (AUC of 0.864 vs. 0.859 using the IBDQ). Conclusion The newly developed K-UCSQ is concise, bathroom problem-emphasizing, and UC-specific, suggesting that it could be a valid and reliable UC-specific instrument for QoL measurement.