Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA.
Clinical Imaging Physics Group, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA.
Med Phys. 2017 Nov;44(11):5705-5717. doi: 10.1002/mp.12554. Epub 2017 Sep 30.
The purpose of this study was to quantitatively characterize the fundamental aspects of image quality (IQ) associated with different computed tomography (CT) reconstruction algorithms, the resolution, noise texture, noise magnitude per dose, and use those data to devise a methodology to match IQ between different CT systems.
This study entailed a 3-step methodology involving (a) characterizing the noise magnitude, texture, and resolution for a CT system-reconstruction using the relationship between noise magnitude and Computed Tomography Dose Index (CTDI), noise power spectrum (NPS), and modulation transfer function (MTF), (b) developing clinically relevant strategies of weighting the differences among system-reconstructions as a means to determine the best match (c) identifying for each target system-reconstruction, system-reconstructions with matched in terms of that minimum IQ differences. Images of the ACR CT phantom were acquired at two dose levels on each of two CT scanners. Images were reconstructed using all available reconstruction kernels and multiple iterative reconstruction (IR) settings. Each reconstruction was characterized as described above. Percent changes for each IQ metric were calculated for every possible pair of system-reconstructions. Weighting functions, reflecting the human visual system's limit to discriminate between spatial frequencies with differences below 5%, were applied to the differences and the product of the weighted values was used to indicate the best match for each system-reconstruction.
Noise texture and resolution are governed by choice of reconstruction kernel and IR strength, while noise magnitude is additionally dependent on dose. Harder kernels have better resolution, finer noise texture, and increase the required dose for a given noise magnitude, and vice versa. Increasing IR strength generally improves resolution, coarsens noise texture, and lowers the required dose. Seventy-one percent of Siemens matches for GE target reconstructions had percent changes in noise texture/resolution under 5%. Seventy-three percent of GE matches for Siemens target reconstructions had percent changes in noise texture/resolution under 5%. ACR phantom images for each matched reconstruction pair appeared similar in both noise magnitude and noise texture.
Matching image appearance in terms of resolution, noise magnitude, and noise texture provides a quantitative and reproducible strategy to improve consistency in image quality among different CT scanners and reconstructions.
本研究的目的是定量描述与不同计算机断层扫描（CT）重建算法相关的图像质量（IQ）的基本方面，包括分辨率、噪声纹理、噪声幅度与剂量比，以及利用这些数据设计一种方法来匹配不同 CT 系统之间的 IQ。
本研究包括三个步骤，涉及（a）使用噪声幅度与计算机断层扫描剂量指数（CTDI）、噪声功率谱（NPS）和调制传递函数（MTF）之间的关系，对 CT 系统重建的噪声幅度、纹理和分辨率进行特征描述，（b）开发临床相关的加权策略，以确定最佳匹配，（c）为每个目标系统重建确定具有匹配最小 IQ 差异的系统重建。在两台 CT 扫描仪上的两个剂量水平对 ACR CT 体模的图像进行采集。使用所有可用的重建核和多个迭代重建（IR）设置对图像进行重建。如上所述对每个重建进行特征描述。计算每个 IQ 指标在每个可能的系统重建对之间的百分比变化。应用反映人类视觉系统对低于 5%的空间频率差异的分辨极限的加权函数来处理差异，加权值的乘积用于表示每个系统重建的最佳匹配。
噪声纹理和分辨率由重建核和 IR 强度的选择决定，而噪声幅度还取决于剂量。较硬的核具有更好的分辨率、更精细的噪声纹理，并增加了给定噪声幅度所需的剂量，反之亦然。增加 IR 强度通常会提高分辨率、使噪声纹理变粗糙并降低所需的剂量。在西门子目标重建中，有 71%的通用电气匹配的噪声纹理/分辨率百分比变化小于 5%。在通用电气目标重建中，有 73%的西门子匹配的噪声纹理/分辨率百分比变化小于 5%。对于每个匹配的重建对，ACR 体模图像在噪声幅度和噪声纹理方面都看起来相似。
在分辨率、噪声幅度和噪声纹理方面匹配图像外观提供了一种定量且可重复的策略，可以提高不同 CT 扫描仪和重建之间图像质量的一致性。
