Objective:To investigate the feasibility of deep learning reconstruction (DLR) algorithm combined with a dual-low protocol (low radiation dose and low contrast medium dose) for thoracoabdominal aortic CT angiography (CTA).Methods:This cross-sectional study prospectively enrolled 56 patients suspected of aortic diseases who underwent aortic CTA at China-Japan Friendship Hospital from June 2023 to June 2024. All patients were randomly divided into two groups: Group A (28 cases) underwent CTA with a tube voltage of 100 kVp, automatic tube current modulation (noise index=10), and a contrast agent dose of 80 ml (flow rate 5 ml/s), with images reconstructed using the three-dimensional adaptive iterative dose reduction algorithm (AIDR). Group B (28 cases) underwent CTA with a tube voltage of 80 kVp, automatic tube current modulation (noise index=25), and a contrast agent dose of 40 ml (flow rate 3.5 ml/s), with images reconstructed using either the deep learning reconstruction algorithm-Advanced intelligent Clear-IQ Engine (AiCE subgroup) or the AIDR (AIDR subgroup). Two physicians evaluated the image quality of the three groups subjectively and objectively. Objective evaluation metrics included CT values, image noise (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at the ascending aorta, carina-level descending aorta, celiac trunk-origin abdominal aorta, and common iliac bifurcation abdominal aorta carina. Subjective evaluation metrics included image quality and noise scores. Comparisons among the three datasets (Group A, AiCE subgroup, AIDR subgroup) were performed using one-way ANOVA or the Kruskal-Wallis test, with appropriate post-hoc tests for pairwise comparisons.Results:No significant differences were observed in CT values of the ascending aorta, descending aorta, and abdominal aorta between Group A and the AiCE subgroup or the AIDR subgroup ( P0.05). However, significant overall differences were found in SD, SNR, and CNR values for the ascending aorta, descending aorta, and abdominal aorta ( P0.05). Pairwise comparisons revealed that, except for no significant differences in SD, SNR, and CNR values of the ascending and descending aorta between Group A and the AiCE subgroup, and no significant difference in SNR values of the ascending and abdominal aorta between Group A and the AIDR subgroup ( P0.05), all other intergroup comparisons showed statistically significant differences ( P0.05). Significant overall differences were also observed in image quality and noise scores between Group A and the AiCE and AIDR subgroups ( P0.05). Except for no significant differences in image quality and noise scores between Group A and the AiCE subgroup ( P0.05), all other pairwise comparisons showed statistically significant differences ( P0.05). Conclusions:The application of deep learning reconstruction algorithm combined with a dual-low protocol in thoracoabdominal aortic CTA can reduce radiation dose and contrast agent dose while maintaining diagnostic image quality, demonstrating significant clinical value for widespread adoption.

Objective:To investigate the feasibility of deep learning reconstruction (DLR) algorithm combined with a dual-low protocol (low radiation dose and low contrast medium dose) for thoracoabdominal aortic CT angiography (CTA).Methods:This cross-sectional study prospectively enrolled 56 patients suspected of aortic diseases who underwent aortic CTA at China-Japan Friendship Hospital from June 2023 to June 2024. All patients were randomly divided into two groups: Group A (28 cases) underwent CTA with a tube voltage of 100 kVp, automatic tube current modulation (noise index=10), and a contrast agent dose of 80 ml (flow rate 5 ml/s), with images reconstructed using the three-dimensional adaptive iterative dose reduction algorithm (AIDR). Group B (28 cases) underwent CTA with a tube voltage of 80 kVp, automatic tube current modulation (noise index=25), and a contrast agent dose of 40 ml (flow rate 3.5 ml/s), with images reconstructed using either the deep learning reconstruction algorithm-Advanced intelligent Clear-IQ Engine (AiCE subgroup) or the AIDR (AIDR subgroup). Two physicians evaluated the image quality of the three groups subjectively and objectively. Objective evaluation metrics included CT values, image noise (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at the ascending aorta, carina-level descending aorta, celiac trunk-origin abdominal aorta, and common iliac bifurcation abdominal aorta carina. Subjective evaluation metrics included image quality and noise scores. Comparisons among the three datasets (Group A, AiCE subgroup, AIDR subgroup) were performed using one-way ANOVA or the Kruskal-Wallis test, with appropriate post-hoc tests for pairwise comparisons.Results:No significant differences were observed in CT values of the ascending aorta, descending aorta, and abdominal aorta between Group A and the AiCE subgroup or the AIDR subgroup ( P0.05). However, significant overall differences were found in SD, SNR, and CNR values for the ascending aorta, descending aorta, and abdominal aorta ( P0.05). Pairwise comparisons revealed that, except for no significant differences in SD, SNR, and CNR values of the ascending and descending aorta between Group A and the AiCE subgroup, and no significant difference in SNR values of the ascending and abdominal aorta between Group A and the AIDR subgroup ( P0.05), all other intergroup comparisons showed statistically significant differences ( P0.05). Significant overall differences were also observed in image quality and noise scores between Group A and the AiCE and AIDR subgroups ( P0.05). Except for no significant differences in image quality and noise scores between Group A and the AiCE subgroup ( P0.05), all other pairwise comparisons showed statistically significant differences ( P0.05). Conclusions:The application of deep learning reconstruction algorithm combined with a dual-low protocol in thoracoabdominal aortic CTA can reduce radiation dose and contrast agent dose while maintaining diagnostic image quality, demonstrating significant clinical value for widespread adoption.

Objective To investigate the clinical features and imaging findings of interstitial lung disease in antisynthetase syndrome(AS-ILD)and to compare the characteristics among AS specificities. Methods A total of 59 cases with AS-ILD at our hospital during the last 5 years were retrospectively reviewed, including anti-Jo1 positive in 37 cases and anti-PL7 positive in 6 cases and anti-PL12 positive in 6 cases and anti EJ positive in 10 cases. There were 14 males and 45 females aged (51 ± 12) years. The clinical features including myositis, arthritis, fever,"mechanic's hands", rash, proximal dysphagia, raynaud phenomenon were identified. Two radiologists evaluated the pattern, distribution and the ILD pattern of the lung abnormalities on HRCT findings. Based on the anti synthetase antibody positive subtype, 59 patients could be divided into four groups;then the differences of clinical features and HRCT findings between different subtypes were analyzed. X2 test was performed for the comparison of the differences between anti-Jo1 antibody positive and the other 3 group. Results (1)Myositis and arthritis were the most common AS manifestations, which were 61.02%(36/59)and 50.85%(30/59) respectively.(2)The lung abnormalities were predominantly basal(91.53%, 54/59) and peripheral(59.32%, 35/59). Ground-grass opacities(79.66%, 47/59)and reticulations(76.27%, 45/59) were found most frequently. Non-specific interstitial pneumonia (NSIP) was the most common HRCT pattern(64.41%, 38/59).(3)As compared with anti-Jo1 positive AS patients, anti-PL12 positive AS patients showed a high rate of traction bronchiectasis at diagnosis(83.33%, 5/6), while the difference was statistically significant(χ2=7.206, P=0.015). The prevalence of pericardial effusion(40.00%, 4/10) was significantly higher in the group of the anti-EJ positive AS patients than that with anti-Jo1 antibody AS(χ2=6.317, P=0.044). Conclusions Myositis and arthritis are the predominant clinical features. NSIP is the most common HRCT pattern in AS-ILD patients. There are some differences of signs among various subtypes, indicating that the difference of fibrosis in the lung and inflammatory reaction in the body being correlated with the AS specificities.

Objective To investigate the clinical features and imaging findings of interstitial lung disease in antisynthetase syndrome(AS-ILD)and to compare the characteristics among AS specificities. Methods A total of 59 cases with AS-ILD at our hospital during the last 5 years were retrospectively reviewed, including anti-Jo1 positive in 37 cases and anti-PL7 positive in 6 cases and anti-PL12 positive in 6 cases and anti EJ positive in 10 cases. There were 14 males and 45 females aged (51 ± 12) years. The clinical features including myositis, arthritis, fever,"mechanic's hands", rash, proximal dysphagia, raynaud phenomenon were identified. Two radiologists evaluated the pattern, distribution and the ILD pattern of the lung abnormalities on HRCT findings. Based on the anti synthetase antibody positive subtype, 59 patients could be divided into four groups;then the differences of clinical features and HRCT findings between different subtypes were analyzed. X2 test was performed for the comparison of the differences between anti-Jo1 antibody positive and the other 3 group. Results (1)Myositis and arthritis were the most common AS manifestations, which were 61.02%(36/59)and 50.85%(30/59) respectively.(2)The lung abnormalities were predominantly basal(91.53%, 54/59) and peripheral(59.32%, 35/59). Ground-grass opacities(79.66%, 47/59)and reticulations(76.27%, 45/59) were found most frequently. Non-specific interstitial pneumonia (NSIP) was the most common HRCT pattern(64.41%, 38/59).(3)As compared with anti-Jo1 positive AS patients, anti-PL12 positive AS patients showed a high rate of traction bronchiectasis at diagnosis(83.33%, 5/6), while the difference was statistically significant(χ2=7.206, P=0.015). The prevalence of pericardial effusion(40.00%, 4/10) was significantly higher in the group of the anti-EJ positive AS patients than that with anti-Jo1 antibody AS(χ2=6.317, P=0.044). Conclusions Myositis and arthritis are the predominant clinical features. NSIP is the most common HRCT pattern in AS-ILD patients. There are some differences of signs among various subtypes, indicating that the difference of fibrosis in the lung and inflammatory reaction in the body being correlated with the AS specificities.