Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

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.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

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.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

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.