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.

Rare endocrine diseases are complex conditions that require lifelong specialized care due to their chronic nature and associated long-term complications. In Korea, a lack of nationwide data on clinical practice and outcomes has limited progress in patient care. Therefore, the Multicenter Networks for Ideal Outcomes of Pediatric Rare Endocrine and Metabolic Disease (OUTSPREAD) study was initiated. This study involves 30 centers across Korea. The study aims to improve the long-term prognosis of Korean patients with rare endocrine diseases by collecting comprehensive clinical data, biospecimens, and patient-reported outcomes to identify complications and unmet needs in patient care. Patients with childhood-onset pituitary, adrenal, or gonadal disorders, such as craniopharyngioma, congenital adrenal hyperplasia (CAH), and Turner syndrome were prioritized. The planned enrollment is 1,300 patients during the first study phase (2022–2024). Clinical, biochemical, and imaging data from diagnosis, treatment, and follow-up during 1980–2023 were retrospectively reviewed. For patients who agreed to participate in the prospective cohort, clinical data and biospecimens will be prospectively collected to discover ideal biomarkers that predict the effectiveness of disease control measures and prognosis. Patient-reported outcomes, including quality of life and depression scales, will be evaluated to assess psychosocial outcomes. Additionally, a substudy on CAH patients will develop a steroid hormone profiling method using liquid chromatography-tandem mass spectrometry to improve diagnosis and monitoring of treatment outcomes. This study will address unmet clinical needs by discovering ideal biomarkers, introducing evidence-based treatment guidelines, and ultimately improving long-term outcomes in the areas of rare endocrine and metabolic diseases.