Four dimensional cone-beam computed tomography (4D-CBCT) has been developed to manage respiratory motion in patients undergoing radiation therapy. In addition to a set of motion artifacts reduced 3D volumetric images, 4D-CBCT also generates a patient motion model just prior to treatment. 4D-CBCT has potential to improve outcomes of lung cancer radiotherapy by providing an updated motion model for advanced beam delivery techniques and accurate dose verification and calculation for adaptive radiotherapy. However, the potentials of 4D-CBCT are far from realized. Currently, the clinical use of 4D- CBCT is very limited, mainly due to its poor image quality and limited accuracy of subsequent motion modeling. In response to NIH PAR-13-137, we propose to develop a new image reconstruction scheme for 4D-CBCT to overcome problems in the current 4D-CBCT process. The new scheme is based on our recently proposed technique that is able to perform simultaneous motion estimation and motion- compensated image reconstruction (SMEIR). The SMEIR utilizes all projections to reconstruct any phase 4D-CBCT by explicitly considering the motion model between different phases. Thus, view aliasing artifacts caused by the limited number of projections can be effectively suppressed. The SMEIR obtains an updated motion model from projections directly, which eliminates the uncertainties in the motion model obtained by deformable registration on reconstructed 4D imaging datasets. Our preliminary studies have shown that the SMEIR algorithm substantially improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy (maximum tacking error 1.5 mm). The overall goal of this project is to develop, optimize and validate a SMEIR-based 4D-CBCT scheme that is able to achieve: 1) 2-mm motion modeling accuracy for advanced beam delivery techniques (e.g., gated delivery); and 2) 2% dose calculation accuracy to derive dose-of-the-day for adaptive radiotherapy.
The specific aims are to: 1) further optimize the SMEIR-based 4D-CBCT scheme by developing several new motion models to enhance the motion estimation accuracy; 2) characterize the performance of the SMEIR algorithm through a series of simulation, experimental and retrospective studies; and 3) conduct a prospective clinical evaluation study on lung cancer patients. Upon completion of the proposed research, SMEIR algorithm will be developed and validated for 4D-CBCT, providing a much-needed imaging tool that can: 1) reduce margin; 2) improve targeting accuracy; 3) reduce dose delivered to organs at risk; 4) obtain updated and accurate motion model; and 5) reconstruct delivered dose at each treatment fraction. These features of 4D-CBCT will enable dose escalation and clinical utilization of adaptive radiotherapy for lung cancer patients. The will lead to improved outcomes for lung cancer radiotherapy in terms of higher rates of local control, lower rates of complication and improved overall survival.
Four dimensional cone-beam computed tomography (4D-CBCT) reconstructed by conventional methods contains severe artifacts due to highly undersampled projections at each breathing phase, causing visualization and accurate tracking of tumor motion very challenging. In this project, we will develop and optimize a new strategy that is able to perform simultaneous image reconstruction and motion estimation, resulting in substantially improved image quality of 4D-CBCT and accurate tumor motion modeling for lung cancer patients undergoing radiation therapy. The technique developed in this project will provide a much- needed imaging tool for tumor motion management and image-guided adaptive radiation therapy to improve the outcome of lung cancer radiation therapy.
|Niu, Shanzhou; Zhang, You; Zhong, Yuncheng et al. (2018) Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation. Comput Biol Med 103:167-182|
|Chen, Liyuan; Shen, Chenyang; Zhou, Zhiguo et al. (2018) Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices. Comput Biol Med 97:30-36|
|Zhang, You; Folkert, Michael R; Li, Bin et al. (2018) 4D liver tumor localization using cone-beam projections and a biomechanical model. Radiother Oncol :|
|Zhou, Zhiguo; Chen, Liyuan; Sher, David et al. (2018) Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning. Conf Proc IEEE Eng Med Biol Soc 2018:1-4|
|Huang, Xiaokun; Zhang, You; Wang, Jing (2018) A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction. Phys Med Biol 63:045002|
|Chen, Binbin; Xiang, Kai; Gong, Zaiwen et al. (2018) Statistical Iterative CBCT Reconstruction Based on Neural Network. IEEE Trans Med Imaging 37:1511-1521|
|Niu, Shanzhou; Yu, Gaohang; Ma, Jianhua et al. (2018) Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction. Inverse Probl 34:|
|Zhang, Hao; Wang, Jing; Zeng, Dong et al. (2018) Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review. Med Phys 45:e886-e907|
|Hao, Hongxia; Zhou, Zhiguo; Li, Shulong et al. (2018) Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer. Phys Med Biol 63:095007|
|Zhang, Hao; Ma, Jianhua; Wang, Jing et al. (2017) Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy. Med Phys 44:e264-e278|
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