Hashemi SayedMasoud, Huynh Christopher, Sahgal Arjun, Song William Y, Nordström Håkan, Eriksson Markus, Mainprize James G, Lee Young, Ruschin Mark
Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Department of Physics, Ryerson University, Toronto, ON, Canada.
J Appl Clin Med Phys. 2018 Nov;19(6):200-208. doi: 10.1002/acm2.12477. Epub 2018 Oct 19.
A Contrast and Attenuation-map Linearity Improvement (CALI) framework is proposed for cone-beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is tailored to improve soft tissue contrast of a new point-of-care image-guided SRS system that employs a challenging half cone beam geometry, but can be readily reproduced on any CBCT platform. CALI includes a pre- and post-processing step. In pre-processing we apply a shading and beam hardening artifact correction to the projections, and in post-processing step we correct the dome/capping artifact on reconstructed images caused by the spatial variations in X-ray energy generated by the bowtie-filter. The shading reduction together with the beam hardening and dome artifact correction algorithms aim to improve the linearity and accuracy of the CT-numbers (CT#). The CALI framework was evaluated using CatPhan to quantify linearity, contrast-to-noise (CNR), and CT# accuracy, as well as subjectively on patient images acquired on a clinical system. Linearity of the reconstructed attenuation-map was improved from 0.80 to 0.95. The CT# mean absolute measurement error was reduced from 76.1 to 26.9 HU. The CNR of the acrylic insert in the sensitometry module was improved from 1.8 to 7.8. The resulting clinical brain images showed substantial improvements in soft tissue contrast visibility, revealing structures such as ventricles which were otherwise undetectable in the original clinical images obtained from the system. The proposed reconstruction framework also improved CT# accuracy compared to the original images acquired on the system. For frameless image-guided SRS, improving soft tissue visibility can facilitate evaluation of MR to CBCT co-registration. Moreover, more accurate CT# may enable the use of CBCT for daily dose delivery measurements.
针对用于脑部立体定向放射治疗（SRS）的锥束CT（CBCT）图像，提出了一种对比度和衰减图线性改进（CALI）框架。所提出的框架旨在改善一种新型床旁图像引导SRS系统的软组织对比度，该系统采用具有挑战性的半锥束几何结构，但可以在任何CBCT平台上轻松再现。CALI包括预处理和后处理步骤。在预处理中，我们对投影应用阴影和束硬化伪影校正，在后处理步骤中，我们校正由蝴蝶结滤波器产生的X射线能量的空间变化在重建图像上引起的穹顶/盖帽伪影。阴影减少以及束硬化和穹顶伪影校正算法旨在提高CT值（CT#）的线性和准确性。使用CatPhan对CALI框架进行评估，以量化线性度、对比度噪声比（CNR）和CT#准确性，并在临床系统上采集的患者图像上进行主观评估。重建衰减图的线性度从0.80提高到0.95。CT#平均绝对测量误差从76.1 HU降低到26.9 HU。感光度模块中丙烯酸插入物的CNR从1.8提高到7.8。由此产生的临床脑部图像在软组织对比度可见性方面有显著改善，揭示了如脑室等在系统获取的原始临床图像中无法检测到的结构。与系统上获取的原始图像相比，所提出的重建框架还提高了CT#准确性。对于无框架图像引导SRS，提高软组织可见性有助于评估MR与CBCT的配准。此外，更准确的CT#可能使CBCT能够用于每日剂量递送测量。
