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评估计算机断层扫描中的迭代重建性能。

Evaluating iterative reconstruction performance in computed tomography.

作者信息

Chen Baiyu, Ramirez Giraldo Juan Carlos, Solomon Justin, Samei Ehsan

机构信息

Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, North Carolina 27705.

Siemens Healthcare, Malvern, Pennsylvania 19355.

出版信息

Med Phys. 2014 Dec;41(12):121913. doi: 10.1118/1.4901670.

DOI:10.1118/1.4901670
PMID:25471973
Abstract

PURPOSE

Iterative reconstruction (IR) offers notable advantages in computed tomography (CT). However, its performance characterization is complicated by its potentially nonlinear behavior, impacting performance in terms of specific tasks. This study aimed to evaluate the performance of IR with both task-specific and task-generic strategies.

METHODS

The performance of IR in CT was mathematically assessed with an observer model that predicted the detection accuracy in terms of the detectability index (d'). d' was calculated based on the properties of the image noise and resolution, the observer, and the detection task. The characterizations of image noise and resolution were extended to accommodate the nonlinearity of IR. A library of tasks was mathematically modeled at a range of sizes (radius 1-4 mm), contrast levels (10-100 HU), and edge profiles (sharp and soft). Unique d' values were calculated for each task with respect to five radiation exposure levels (volume CT dose index, CTDIvol: 3.4-64.8 mGy) and four reconstruction algorithms (filtered backprojection reconstruction, FBP; iterative reconstruction in imaging space, IRIS; and sinogram affirmed iterative reconstruction with strengths of 3 and 5, SAFIRE3 and SAFIRE5; all provided by Siemens Healthcare, Forchheim, Germany). The d' values were translated into the areas under the receiver operating characteristic curve (AUC) to represent human observer performance. For each task and reconstruction algorithm, a threshold dose was derived as the minimum dose required to achieve a threshold AUC of 0.9. A task-specific dose reduction potential of IR was calculated as the difference between the threshold doses for IR and FBP. A task-generic comparison was further made between IR and FBP in terms of the percent of all tasks yielding an AUC higher than the threshold.

RESULTS

IR required less dose than FBP to achieve the threshold AUC. In general, SAFIRE5 showed the most significant dose reduction potentials (11-54 mGy, 77%-84%), followed by SAFIRE3 (7-36 mGy, 50%-61%) and IRIS (6-26 mGy, 37%-50%). The dose reduction potentials highly depended on task size and task contrast, with tasks of lower contrasts and smaller sizes, i.e., more challenging tasks, indicating higher dose reductions. Softer edge profile showed higher dose reduction potentials with SAFIRE3 and SAFIRE5, but not with IRIS. The task-generic comparison between IR and FBP demonstrated the overall superiority of IR performance, as IR allowed a larger percent of tasks to exceed the threshold AUC: IRIS, 8%-12%; SAFIRE3, 10%-16%; and SAFIRE5, 20%-33%. The improvement with IR was generally more pronounced at lower dose levels.

CONCLUSIONS

Expanding beyond traditional contrast and noise based assessments of IR, we performed both task-specific and task-generic evaluations of IR performance. The task-specific evaluation demonstrated the dependency of IR's dose reduction potential on task attributes, which can be employed to optimize IR for clinical indications with specific range of size and contrast. The task-generic evaluation demonstrated IR's overall superiority over FBP in terms of the range of tasks exceeding a threshold performance level, which can be employed for general comparisons between algorithms.

摘要

目的

迭代重建(IR)在计算机断层扫描(CT)中具有显著优势。然而,其性能特征因潜在的非线性行为而变得复杂,这会影响特定任务方面的性能。本研究旨在通过特定任务和通用任务策略评估IR的性能。

方法

使用观察者模型对CT中IR的性能进行数学评估,该模型根据可检测性指数(d')预测检测准确性。d'基于图像噪声和分辨率、观察者以及检测任务的属性进行计算。图像噪声和分辨率的表征得到扩展,以适应IR的非线性。在一系列尺寸(半径1 - 4毫米)、对比度水平(10 - 100亨氏单位)和边缘轮廓(锐利和柔和)下对任务库进行数学建模。针对五种辐射暴露水平(容积CT剂量指数,CTDIvol:3.4 - 64.8毫戈瑞)和四种重建算法(滤波反投影重建,FBP;成像空间中的迭代重建,IRIS;以及强度为3和5的正弦图确认迭代重建,SAFIRE3和SAFIRE5;均由德国福希海姆的西门子医疗提供),为每个任务计算独特的d'值。将d'值转换为接收器操作特征曲线下的面积(AUC),以代表人类观察者的性能。对于每个任务和重建算法,得出阈值剂量,即达到阈值AUC为0.9所需的最小剂量。IR的特定任务剂量降低潜力计算为IR和FBP的阈值剂量之差。在产生高于阈值的AUC的所有任务的百分比方面,进一步对IR和FBP进行通用任务比较。

结果

IR比FBP需要更少的剂量来达到阈值AUC。一般来说,SAFIRE5显示出最显著的剂量降低潜力(11 - 54毫戈瑞,77% - 84%),其次是SAFIRE

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