Abstract
Motion estimation using four-dimensional (4D) X-ray cone-beam computed tomography (CBCT) is important to achieve a precise radiation therapy of lung cancer patients. If the conventional three-dimensional (3D) reconstruction methods were used, the radiation dose would be much higher to obtain satisfying 4D images. Previously, we developed a simultaneous motion estimation and image reconstruction (SMEIR) method to reconstruct high-quality images and to obtain the motion model using a regular 3D CBCT scan. Due to non-convexity of the problem, the solution of SMEIR depends on the initial deformation vector fields (DVFs) and could be trapped in local optima. In this study, we develop a practical approach to alleviate this problem: First, each phase image is reconstructed using SMEIR with initial DVFs estimated from 3D reconstruction with total variation (TV) minimization. Then, DVFs are updated by applying the demons registration method on all SMEIR reconstructed phase images and used as the initial DVFs for the second round SMEIR. This modified SMEIR (m-SMEIR) method was tested using a CBCT simulation study of 4D NCAT digital phantom. The results demonstrate that m-SMEIR can yield better image quality than the SMEIR method. More importantly, m-SMEIR can produce much improved motion fields compared to SMEIR (47% improvement measured by the mean deviation from the true tumor motion), likely due to m-SMEIR's capability of jumping out of the local optimum.
Published Version
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