Lei Yang, Tang Xiangyang, Higgins Kristin, Wang Tonghe, Liu Tian, Dhabaan Anees, Shim Hyunsuk, Curran Walter J, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA 30322.
Proc SPIE Int Soc Opt Eng. 2018 Feb;10573. doi: 10.1117/12.2292886. Epub 2018 Mar 9.
We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients' data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.
我们提出了一种基于解剖特征和自动上下文交替回归森林的CBCT图像质量改进方法。从对齐的训练图像中提取特定患者的解剖特征，并将其用作每个体素的特征。识别出最相关和最具信息性的特征来训练回归森林。训练良好的回归森林用于校正新患者的CBCT。使用10名患者的CBCT和CT图像数据对该算法进行了评估。使用平均绝对误差（MAE）、峰值信噪比（PSNR）和归一化互相关（NCC）指标来量化该算法的校正精度。校正后的CBCT与真实CT之间的平均MAE、PSNR和NCC分别为16.66HU、37.28dB和0.98，这证明了所提出的基于学习的方法的CBCT校正精度。我们开发了一种基于学习的方法，并证明该方法可以显著提高CBCT图像质量。所提出的方法在将CBCT图像质量提高到接近计划CT的水平方面具有巨大潜力，因此，允许其在CBCT引导的自适应放疗中进行定量使用。
