Schmidt Taly Gilat
Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201, USA.
Med Phys. 2009 Jul;36(7):3018-27. doi: 10.1118/1.3148535.
This paper investigates a method of reconstructing images from energy-resolved CT data with negligible beam-hardening artifacts and improved contrast-to-nosie ratio (CNR) compared to conventional energy-weighting methods. Conceptually, the investigated method first reconstructs separate images from each energy bin. The final image is a linear combination of the energy-bin images, with the weights chosen to maximize the CNR in the final image. The optimal weight of a particular energy-bin image is derived to be proportional to the contrast-to-noise-variance ratio in that image. The investigated weighting method is referred to as "image-based" weighting, although, as will be described, the weights can be calculated and the energy-bin data combined prior to reconstruction. The performance of optimal image-based energy weighting with respect to CNR and beam-hardening artifacts was investigated through simulations and compared to that of energy integrating, photon counting, and previously studied optimal "projection-based" energy weighting. Two acquisitions were simulated: dedicated breast CT and a conventional thorax scan. The energy-resolving detector was simulated with five energy bins. Four methods of estimating the optimal weights were investigated, including task-specific and task-independent methods and methods that require a single reconstruction versus multiple reconstructions. Results demonstrated that optimal image-based weighting improved the CNR compared to energy-integrating weighting by factors of 1.15-1.6 depending on the task. Compared to photon-counting weighting, the CNR improvement ranged from 1.0 to 1.3. The CNR improvement factors were comparable to those of projection-based optimal energy weighting. The beam-hardening cupping artifact increased from 5.2% for energy-integrating weighting to 12.8% for optimal projection-based weighting, while optimal image-based weighting reduced the cupping to 0.6%. Overall, optimal image-based energy weighting provides images with negligible beam-hardening artifacts and improved CNR compared to energy-integrating and photon-counting methods.
本文研究了一种从能量分辨CT数据重建图像的方法，与传统能量加权方法相比，该方法的硬化伪影可忽略不计，且对比度噪声比（CNR）得到了改善。从概念上讲，所研究的方法首先从每个能量区间重建单独的图像。最终图像是能量区间图像的线性组合，权重的选择是为了使最终图像中的CNR最大化。特定能量区间图像的最佳权重被推导为与该图像中的对比度噪声方差比成正比。所研究的加权方法被称为“基于图像”的加权，不过，正如将要描述的那样，权重可以在重建之前计算出来，并且能量区间数据可以合并。通过模拟研究了基于图像的最佳能量加权在CNR和硬化伪影方面的性能，并与能量积分、光子计数以及之前研究的最佳“基于投影”的能量加权进行了比较。模拟了两种采集情况：专用乳腺CT和传统胸部扫描。用五个能量区间模拟了能量分辨探测器。研究了四种估计最佳权重的方法，包括特定任务和非特定任务的方法，以及需要单次重建与多次重建的方法。结果表明，与能量积分加权相比，基于图像的最佳加权根据任务不同将CNR提高了1.15至1.6倍。与光子计数加权相比，CNR的提高幅度在1.0至1.3之间。CNR的提高系数与基于投影的最佳能量加权相当。硬化杯状伪影从能量积分加权的5.2%增加到基于投影的最佳加权的12.8%，而基于图像的最佳加权将杯状伪影降低到了0.6%。总体而言，与能量积分和光子计数方法相比，基于图像的最佳能量加权提供的图像硬化伪影可忽略不计，且CNR得到了改善。
