The present study utilized a porcine model for qualitative and quantitative assessment of the diagnostic quality of non-contrast abdominal computed tomography (CT) images generated by Adaptive Statistical Iterative Re...The present study utilized a porcine model for qualitative and quantitative assessment of the diagnostic quality of non-contrast abdominal computed tomography (CT) images generated by Adaptive Statistical Iterative Reconstruction (ASIR, GE Healthcare, Waukesha, Wisconsin, USA), Model-Based Iterative Reconstruction (GE company name VEO), and conventional Filtered back projection (FBP) technique. Methods: Multiple CT whole-body scans of a freshly euthanized pig carcass were performed on a 64-slice GE CT scanner at varying noise indices (5, 10, 15, 20, 30, 37, 40, 45), and with three different algorithms (VEO, FBP, and ASIR at 30%, 50%, and 70% levels of ASIR-FBP blending). Abdominal CT images were reviewed and scored in a blinded and randomized manner by two board-certified abdominal radiologists. The task was to evaluate the clarity of the images according to a rubric involving edge sharpness, presence of artifact, anatomical clarity (assessed at four regions), and perceived diagnostic acceptability. This amounted to seven criteria, each of which was graded on a scale of 1 to 5. A weighted formula was used to calculate a composite score for each scan. Results: VEO outperforms ASIR and FBP by an average of 0.5 points per the scoring system used (p < 0.05). Above a threshold noise index of 30, diagnostic acceptability is lost by all algorithms, and there is no diagnostic advantage to increasing the dose beyond a noise index of 10. Between a noise index of 25 - 30, VEO retains diagnostic acceptability, as opposed to ASIR and FBP which lose acceptability above noise index of 25. Conclusion: Model-based iterative reconstruction provides superior image quality and anatomical clarity at reduced radiation dosages, supporting the routine use of this technology, particularly in pediatric abdominal CT scans.展开更多
文摘目的探讨前置基于多模型的自适应统计迭代重建(ASiR-V)技术对胸部CT辐射剂量和图像质量的影响。方法采用GE Revolution CT对胸部仿真体模和120例胸部CT平扫患者(分为6组,每组20例)分别设定前置ASiR-V权重为0、20%、40%、60%、80%、100%进行扫描。管电压120kV,管电流采用自动毫安(Smart mA 10-500)技术,噪声指数设为11。记录胸部体模及各组患者扫描的剂量长度乘积,计算并比较各组有效剂量(ED)。以胸部体模不同组织结构(肺组织、脊柱旁软组织、主动脉和椎体)的CT值和标准差(SD)作为客观指标,结合对各组患者的图像主观评分,比较图像质量的组间差异。结果随着前置ASiR-V权重的增加,体模及患者的ED均呈对数降低,体模不同组织CT值、图像噪声均未见明显改变。ASiR-V权重为40%时纵隔窗和肺窗的主观评分开始下降;60%时纵隔窗和肺窗图像主观评分相对40%时出现明显下降(P<0.05)。ASiR-V权重为40%,ED降至ASiR-V权重为0时的57.21%。结论前置ASiR-V可以降低辐射剂量,同时又不影响图像客观指标;前置ASiR-V权重为40%时,图像仍可保证临床诊断需求,且辐射剂量明显降低,临床应用价值最高。
文摘The present study utilized a porcine model for qualitative and quantitative assessment of the diagnostic quality of non-contrast abdominal computed tomography (CT) images generated by Adaptive Statistical Iterative Reconstruction (ASIR, GE Healthcare, Waukesha, Wisconsin, USA), Model-Based Iterative Reconstruction (GE company name VEO), and conventional Filtered back projection (FBP) technique. Methods: Multiple CT whole-body scans of a freshly euthanized pig carcass were performed on a 64-slice GE CT scanner at varying noise indices (5, 10, 15, 20, 30, 37, 40, 45), and with three different algorithms (VEO, FBP, and ASIR at 30%, 50%, and 70% levels of ASIR-FBP blending). Abdominal CT images were reviewed and scored in a blinded and randomized manner by two board-certified abdominal radiologists. The task was to evaluate the clarity of the images according to a rubric involving edge sharpness, presence of artifact, anatomical clarity (assessed at four regions), and perceived diagnostic acceptability. This amounted to seven criteria, each of which was graded on a scale of 1 to 5. A weighted formula was used to calculate a composite score for each scan. Results: VEO outperforms ASIR and FBP by an average of 0.5 points per the scoring system used (p < 0.05). Above a threshold noise index of 30, diagnostic acceptability is lost by all algorithms, and there is no diagnostic advantage to increasing the dose beyond a noise index of 10. Between a noise index of 25 - 30, VEO retains diagnostic acceptability, as opposed to ASIR and FBP which lose acceptability above noise index of 25. Conclusion: Model-based iterative reconstruction provides superior image quality and anatomical clarity at reduced radiation dosages, supporting the routine use of this technology, particularly in pediatric abdominal CT scans.
