Xiaokun Huang and You Zhang contributed equally to the work.
Phys Med Biol. 2018 Feb 8;63(4):045002. doi: 10.1088/1361-6560/aaa730.
Reconstructing four-dimensional cone-beam computed tomography (4D-CBCT) images directly from respiratory phase-sorted traditional 3D-CBCT projections can capture target motion trajectory, reduce motion artifacts, and reduce imaging dose and time. However, the limited numbers of projections in each phase after phase-sorting decreases CBCT image quality under traditional reconstruction techniques. To address this problem, we developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, an iterative method that can reconstruct higher quality 4D-CBCT images from limited projections using an inter-phase intensity-driven motion model. However, the accuracy of the intensity-driven motion model is limited in regions with fine details whose quality is degraded due to insufficient projection number, which consequently degrades the reconstructed image quality in corresponding regions. In this study, we developed a new 4D-CBCT reconstruction algorithm by introducing biomechanical modeling into SMEIR (SMEIR-Bio) to boost the accuracy of the motion model in regions with small fine structures. The biomechanical modeling uses tetrahedral meshes to model organs of interest and solves internal organ motion using tissue elasticity parameters and mesh boundary conditions. This physics-driven approach enhances the accuracy of solved motion in the organ's fine structures regions. This study used 11 lung patient cases to evaluate the performance of SMEIR-Bio, making both qualitative and quantitative comparisons between SMEIR-Bio, SMEIR, and the algebraic reconstruction technique with total variation regularization (ART-TV). The reconstruction results suggest that SMEIR-Bio improves the motion model's accuracy in regions containing small fine details, which consequently enhances the accuracy and quality of the reconstructed 4D-CBCT images.
直接从呼吸相位分类的传统 3D-CBCT 投影重建四维锥形束 CT（4D-CBCT）图像可以捕捉目标运动轨迹，减少运动伪影，降低成像剂量和时间。然而，相位分类后每个相位中的投影数量有限，会降低传统重建技术下的 CBCT 图像质量。为了解决这个问题，我们开发了一种同时运动估计和图像重建（SMEIR）算法，这是一种迭代方法，可以使用相位间强度驱动运动模型从有限的投影重建更高质量的 4D-CBCT 图像。然而，强度驱动运动模型的准确性在具有精细细节的区域受到限制，由于投影数量不足，这些区域的质量会下降，从而导致相应区域的重建图像质量下降。在这项研究中，我们通过将生物力学建模引入 SMEIR（SMEIR-Bio）中开发了一种新的 4D-CBCT 重建算法，以提高具有小精细结构区域的运动模型的准确性。生物力学建模使用四面体网格来模拟感兴趣的器官，并使用组织弹性参数和网格边界条件来求解内部器官运动。这种物理驱动的方法提高了器官精细结构区域中求解运动的准确性。这项研究使用了 11 个肺部患者病例来评估 SMEIR-Bio 的性能，对 SMEIR-Bio、SMEIR 和具有总变分正则化的代数重建技术（ART-TV）进行了定性和定量比较。重建结果表明，SMEIR-Bio 提高了包含小精细细节区域的运动模型的准确性，从而提高了重建的 4D-CBCT 图像的准确性和质量。
