Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Purpose: This study focused on analyzing the contact pressure and area on different patellar component designs in total knee arthroplasty (TKA) to evaluate biomechanics related to the patellofemoral (PF) joint. Materials and Methods: The patellar components studied included the dome design, modified dome design, and anatomical design implants. Using finite element analysis and mechanical testing, the pressure and area were evaluated. The first loading condition was simulated at flexion angles of 0°, 15°, 45°, 90°, 120°, and 150°. The second loading condition was simulated for a clinically relevant scenario, involving a 2-mm medial shift at a flexion angle of 45°. Results: For both the modified dome and anatomical designs, the contact area and pressure increased with the flexion angle. The dome design reached its maximum contact area at a flexion angle of 120°. Among the designs, the anatomical design had the largest contact area and a lower contact pressure compared to the dome and modified dome designs. However, when a medial shift of 2 mm was simulated at a 45° flexion angle, which can occur clinically, the anatomical design showed edge contact, leading to higher contact pressure and reduced contact area. In contrast, the modified dome design demonstrated the lowest contact pressure and the greatest contact area under the same shifted conditions. Conclusion These findings suggest that the design of the patellar component significantly affects patellar biomechanics and stability. Specifically, the modified dome design showed improved biomechanical effects in clinically relevant scenarios. Therefore, patellar components with a modified dome design are expected to better manage PF joint pain and reduce complications in TKA.