4D liver tumor localization using cone-beam projections and a biomechanical model

Abstract

Purpose: To improve the accuracy of liver tumor localization, this study tests a biomechanical modeling-guided liver cone-beam CT (CBCT) estimation (Bio-CBCT-est) technique, which generates new CBCTs by deforming a prior high-quality CT or CBCT image using deformation vector fields (DVFs). The DVFs can be used to propagate tumor contours from the prior image to new CBCTs for automatic 4D tumor localization. Methods/ Materials: To solve the DVFs, the Bio-CBCT-est technique employs an iterative scheme that alternates between intensity-driven 2D-3D deformation and biomechanical modelingguided DVF regularization and optimization. The 2D-3D deformation step solves DVFs by matching digitally reconstructed radiographs of the 3D deformed prior image to 2D phase-sorted on-board projections according to imaging intensities. This step's accuracy is limited at lowcontrast intra-liver regions without sufficient intensity variations. To boost the DVF accuracy in these regions, we use the intensity-driven DVFs solved at higher-contrast liver boundaries to finetune the intra-liver DVFs by finite element analysis-based biomechanical modeling. We evaluated Bio-CBCT-est's accuracy with seven liver cancer patient cases. For each patient, we simulated 4D cone-beam projections from 4D-CT images, and used these projections for Bio-CBCT-est based image estimations. After Bio-CBCT-est, the DVF-propagated liver tumor/cyst contours were quantitatively compared with the manual contours on the original 4D-CT 'reference' images, using the DICE similarity index, the center-of-mass-error (COME), the Hausdorff distance (HD) and the voxel-wise cross-correlation (CC) metrics. In addition to

Medical physics challenges in clinical MR-guided radiotherapy

“…Nonetheless, CBCT exhibits intrinsic limitations, due to suboptimal image quality, poor soft tissue contrast and the additional imaging dose. Furthermore, the use of CBCT to manage specific uncertainties, such as tumor motion, is not established and may be questioned when considering the inherent shortcomings [6,7].…”

Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning

“…Such techniques could be grafted with SMEIR-type algorithms to yield the final 4D-CBCT directly applicable for accurate dose calculation. Based on the DVF-accumulated doses on each treatment fraction and throughout all fractions, and based on the DVF-propagated tumor and normal tissue contours [32,85], decisions can be rendered in regards to the necessity of adaptive radiation therapy. Based on high-quality CBCTs, radiotherapy plans can be adapted and optimized to meet the patient's clinical needs to maximize the radiotherapy benefits.…”

“…In our study, we used the Mooney-Rivlin hyper-elastic material, a model usually used to describe soft tissues with relatively large deformation. Detailed information of the model can be found in previous publications [27,32,55]. In this study, we modeled the lung as a homogeneous organ with the same elastic parameters throughout the whole volume.…”

“…The 2D-3D deformation algorithm iteratively optimizes the DVF, such that it deforms the prior image until the digitally-reconstructed-radiographs (DRRs) of the deformed image match the acquired cone-beam projections. The 2D-3D deformation technique can not only generate 4D-CBCT images, but provide DVFs to potentially allow automatic tumor localization, target tracking, dose accumulation and adaptive radiation therapy [29][30][31][32]. The incorporation of highquality prior information also introduces more accurate Hounsfield units [30], and allows further imaging dose reduction by acquiring fewer projections for reconstruction.…”

Percutaneous insertion of hepatic fiducial true-spherical markers for real-time adaptive radiotherapy

“…Because its success greatly depends on the accuracy of daily setup of the patient, as well as the management of respiratory tumor motion, image-guided radiation therapy (IGRT) using imaging equipment in the treatment room is expected to further improve the clinical results. Daily set-up of the patient has been shown to be improved by IGRT using on-line X-ray cone beam computed tomography (CBCT), deformable registration software, four-dimensional reconstruction of CBCT, and on-line magnetic resonance imaging (MRI) [2,3,4]. For the management of respiratory tumor motion during irradiation, real-time adaptive radiotherapy (RAR) is expected to reduce uncertainty [5].…”