Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
Med Phys. 2011 Sep;38(9):4946-57. doi: 10.1118/1.3609097.
Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins.
Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i) numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system.
The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented by the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images.
Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.
通过利用能域中的数据冗余来降低光谱 CT 的图像噪声，从而可以灵活地选择能窗的数量、宽度和位置。
使用各种光谱 CT 成像方法，对常规滤波反投影（FBP）重建进行了处理，并将所得图像与使用局部高约束反投影重建（HYPR-LR）算法处理的图像进行了比较。在感兴趣区域（ROI）内测量 CT 数的平均值和标准差，并比较 FBP 和 HYPR-LR 之间的结果。为了进行这些比较，使用了以下光谱 CT 成像方法：（i）基于光子计数、探测器的 CT 系统的数值模拟，（ii）使用铷和氯化钾溶液的光子计数、探测器的微 CT 系统，（iii）配备了利用 80、100、120 和 140 kV 管电压的积分探测器的商业 CT 系统，以及（iv）临床双能 CT 检查。根据每个 CT 系统的特点，评估了管能和能窗宽度的影响。
对于每种光谱 CT 成像情况，在 FBP 和 HYPR-LR 图像之间，每个 ROI 的平均 CT 数都没有变化，与能窗宽度或管电压无关。然而，以每个 ROI 中 CT 数的标准差表示的图像噪声降低了 36%-76%。在所有情况下，HYPR-LR 算法处理后的图像噪声与使用所有可用光子的复合图像相似。在 HYPR-LR 处理和 FBP 之间没有观察到空间分辨率的差异。使用 HYPR-LR 处理的双能患者数据在单个低能和高能图像以及物质特异性基础图像中显示出噪声降低。
通过利用能域中的数据冗余，可以实现光谱 CT 的噪声降低。HYPR-LR 是一种强大的方法，可以在不损失空间分辨率或 CT 数准确性的情况下，降低各种光谱 CT 成像系统中的图像噪声。这种方法提高了选择能窗的灵活性，以优化物质识别和分离，而不会付出增加图像噪声或其必然结果，即增加患者剂量的代价。
