首页|70 kVp联合深度学习图像重建算法对胸主动脉血管内修复术后双低CTA图像质量的影响

70 kVp联合深度学习图像重建算法对胸主动脉血管内修复术后双低CTA图像质量的影响

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目的 探讨70 kVp联合深度学习图像重建(DLIR)算法在低辐射剂量低对比剂用量的情况下对胸主动脉血管内修复术(TEVAR)后主动脉CT血管成像(CTA)图像质量的影响.方法 前瞻性纳入65例TEVAR术后需接受主动脉CTA扫描的患者,随机分为两组.常规剂量组(A组)接受100 kVp管电压和60 mL对比剂扫描,低剂量组(B组)接受"双低"方案:管电压70kVp,对比剂用量为0.5 mL·kg-1.A组图像采用50%多模型迭代重建算法(ASIR-V)重建,B组图像分别采用滤波反投影(FBP)算法(B1亚组)、50%ASIR-V(B2亚组)、90%ASIR-V(B3亚组)及DLIR-H算法(B4亚组)重建.测量并比较各组主动脉CT值、图像噪声、对比噪声比(CNR)及品质因数(FOM).采用5分制对图像噪声、血管锐利度和整体图像质量进行评估.结果 B4亚组噪声最低,较A组降低约23.49%(P<0.001).A组、B4亚组主动脉各感兴趣区(ROI)CT值、CNR值及图像主观评分差异无统计学意义(P>0.05);B4亚组FOM值大于A组(P<0.001).与A组比较,B组辐射剂量降低约52.38%(P<0.001),对比剂用量和注射流率分别降低约39%和33.6%(P<0.001).结论 采用70 kVp结合DLIR算法在TEVAR术后主动脉CTA扫描中可获得与常规剂量相当的图像质量,且辐射剂量和对比剂用量大幅度降低.
Impact of 70 kVp Combined with Deep Learning Image Reconstruction on the Quality of Double Low CTA Images After Thoracic Endovascular Aortic Repair
Objective To explore the effect of 70 kVp combined with deep learning image reconstruction(DLIR)algorithm on the image quality of aortic CT angiography(CTA)after thoracic endovascular aortic repair(TEVAR)under low radiation dose and low contrast agent dosage.Methods A total of 65 patients that accepted follow-up aortic CTA after TEVAR were included in the prospective study and were individed into two groups.The conventional protocol group(group A)accepted standard tube voltage of 100 kVp and contrast medium volume of 60 mL,the low-dose group(group B)received a"double low"regimen,tube voltage of 70 kVp and contrast medium dosage of 0.5 mL·kg-1.The images in group A were reconstructed with 50%multiple model adaptive statistical iterative reconstruction-Veo(ASIR-V),and four sets for group B were reconstructed with filtered back projection(FBP)(subgroup B1),50%ASIR-V(subgroup B2),90%ASIR-V(subgroup B3)and DLIR-H(subgroup B4),respectively.CT values,image noise,contrast-to noise ratio(CNR)and FOM of the aorta were measured and compared.The image noise,sharpness and overall image quality were scored on a 5-point scale.Results Subgroup B4 had the lowest noise level,with a reduction of approximately 23.49%compared to group A(P<0.001).There was no statistically difference in region of interest(ROI)CT values,CNR values and subjective image scores of the aortic between group A and subgroup B4(P>0.05).FOM value in subgroup B4 was greater than group A(P<0.001).Compared with group A,group B showed a reduction of approximately 52.38%in radiation dose(P<0.001),with a decrease of approximately 39%in contrast medium dosage and 33.6%in injection flow rate,respectively(P<0.001).Conclusion The use of 70 kVp combined with DLIR algorithm can achieve image quality comparable to conventional dose in TEVAR postoperative aortic CTA scanning,and significantly reduce radiation dose and contrast medium dosage.

deep learning image reconstruction algorithmimage processingcomputed tomographyaorta

侯平、唐丽、陈岩、刘杰、王小鹏、吕培杰、高剑波

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郑州大学第一附属医院放射科,河南郑州 450052

深度学习图像重建算法 图像处理 计算机断层扫描 主动脉

2024

河南医学研究
河南省医学科学院

河南医学研究

影响因子:0.979
ISSN:1004-437X
年,卷(期):2024.33(16)