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.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

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.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

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.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

Atopic dermatitis is the most common chronic inflammatory skin disease in children and adolescents. The Korean Academy of Pediatric Allergy and Respiratory Disease published the Atopic Dermatitis Treatment Guideline in 2008, which has been helpful in atopic dermatitis treatment until now. Various reports on the development and effectiveness of new drugs have suggested that there is a need to develop and revise old treatment guidelines. Part 1 aimed to provide evidence-based recommendations for skin care management and topical treatment for atopic dermatitis. Part 2 focuses on systemic treatment, novel therapeutics, and adjuvant therapy. The goal of this guideline is intended to assist front-line doctors treating pediatric and adolescent atopic dermatitis patients make safer, more effective, and more rational decisions regarding systemic treatment, novel therapeutics, and adjuvant therapy by providing evidence-based recommendations with a clear level of evidence and benefit regarding treatment.

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.