Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon’s statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTF_Task), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNR_LO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTF_Task was measured at four contrast levels, and CNR_LO was measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

现代 CT 迭代重建算法正在从基于统计的方法转变为基于模型的方法。然而，不断增加的复杂性并不能确保所有适应症的图像质量得到改善，新算法的全面表征对于了解其潜在的临床影响很重要。本研究对图像质量进行定量和定性分析，以比较佳能基于统计的自适应迭代剂量减少 3D (AIDR 3D) 算法与其基于模型的算法、基于前向投影模型的迭代重建解决方案 (FIRST)。体模用于测量特定任务的调制传递函数 (MTF _Task )、噪声功率谱 (NPS) 和低对比度的特定对象 CNR (CNR _LO) 对于使用三个剂量水平的每个算法和适用于腹部、肺和脑成像的卷积算法（内核）。此外，MTF_任务在四个对比度级别进行测量，CNR _LO测量了两种物体尺寸。最后，三位放射科医生参与了一项偏好研究，以比较三种研究类型的临床图像质量：非对比剂腹部、肺栓塞 (PE) 和肺部筛查。每种类型的 20 次考试对与解剖特征或图像质量特征的外观有关的 9 个问题进行了评分。两种算法的行为在很大程度上取决于所选的内核。体模测量表明，FIRST 在腹部成像方面应该优于 AIDR 3D，但并不表明 FIRST 在肺或脑成像方面有明显的整体优势；指标表明性能可能相当于或略微偏向 AIDR 3D，具体取决于被成像对象的大小以及空间分辨率或低对比度分辨率对手头的任务更重要。全面的，放射科医师强烈推荐 AIDR 3D 进行肺部筛查，略微偏爱 AIDR 3D 用于非造影腹部，不偏爱 PE。FIRST 在减少金属伪影方面具有优势。放射科医生的偏好可能会受到噪声纹理变化的影响。