Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD United States of America.
Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, MD United States of America.
Phys Med Biol. 2021 Feb 20;66(5):055012. doi: 10.1088/1361-6560/abde97.
Model-based iterative reconstruction (MBIR) for cone-beam CT (CBCT) offers better noise-resolution tradeoff and image quality than analytical methods for acquisition protocols with low x-ray dose or limited data, but with increased computational burden that poses a drawback to routine application in clinical scenarios. This work develops a comprehensive framework for acceleration of MBIR in the form of penalized weighted least squares optimized with ordered subsets separable quadratic surrogates. The optimization was scheduled on a set of stages forming a morphological pyramid varying in voxel size. Transition between stages was controlled with a convergence criterion based on the deviation between the mid-band noise power spectrum (NPS) measured on a homogeneous region of the evolving reconstruction and that expected for the converged image, computed with an analytical model that used projection data and the reconstruction parameters. A stochastic backprojector was developed by introducing a random perturbation to the sampling position of each voxel for each ray in the reconstruction within a voxel-based backprojector, breaking the deterministic pattern of sampling artifacts when combined with an unmatched Siddon forward projector. This fast, forward and backprojector pair were included into a multi-resolution reconstruction strategy to provide support for objects partially outside of the field of view. Acceleration from ordered subsets was combined with momentum accumulation stabilized with an adaptive technique that automatically resets the accumulated momentum when it diverges noticeably from the current iteration update. The framework was evaluated with CBCT data of a realistic abdomen phantom acquired on an imaging x-ray bench and with clinical CBCT data from an angiography robotic C-arm (Artis Zeego, Siemens Healthineers, Forchheim, Germany) acquired during a liver embolization procedure. Image fidelity was assessed with the structural similarity index (SSIM) computed with a converged reconstruction. The accelerated framework provided accurate reconstructions in 60 s (SSIM = 0.97) and as little as 27 s (SSIM = 0.94) for soft-tissue evaluation. The use of simple forward and backprojectors resulted in 9.3× acceleration. Accumulation of momentum provided extra ∼3.5× acceleration with stable convergence for 6-30 subsets. The NPS-driven morphological pyramid resulted in initial faster convergence, achieving similar SSIM with 1.5× lower runtime than the single-stage optimization. Acceleration of MBIR to provide reconstruction time compatible with clinical applications is feasible via architectures that integrate algorithmic acceleration with approaches to provide stable convergence, and optimization schedules that maximize convergence speed.
基于模型的迭代重建（MBIR）在锥形束 CT（CBCT）中提供了更好的噪声分辨率权衡和图像质量，优于低 X 射线剂量或有限数据采集协议的分析方法，但计算负担增加，这对临床场景中的常规应用构成了障碍。本工作开发了一种综合框架，以惩罚加权最小二乘形式加速 MBIR，并用有序子集可分离二次代理进行优化。优化在一组形成形态金字塔的阶段上进行，这些阶段在体素大小上有所不同。阶段之间的转换由基于在演变重建的均匀区域上测量的中带噪声功率谱（NPS）与分析模型计算的收敛图像的预期 NPS 之间的偏差的收敛标准来控制，该分析模型使用投影数据和重建参数。通过在基于体素的反向投影器中为重建中的每条射线的每个体素的采样位置引入随机扰动，开发了一种随机反向投影器，当与不匹配的 Siddon 正向投影器结合使用时，它会破坏采样伪影的确定性模式。这种快速、正向和反向投影器对被组合到多分辨率重建策略中，以支持部分超出视场的物体。有序子集的加速与使用自适应技术稳定的动量积累相结合，该技术在累积动量明显偏离当前迭代更新时自动重置累积动量。该框架使用在成像 X 射线台上获取的真实腹部体模的 CBCT 数据以及在肝脏栓塞过程中从血管造影机器人 C 臂（德国西门子健康西门子医疗的 Artis Zeego）获取的临床 CBCT 数据进行了评估。使用与收敛重建计算的结构相似性指数（SSIM）评估图像保真度。在 60 秒（SSIM=0.97）和 27 秒（SSIM=0.94）内，加速框架为软组织评估提供了准确的重建。简单正向和反向投影器的使用可实现 9.3 倍的加速。动量积累提供了额外的约 3.5 倍的加速，对于 6-30 个子集，具有稳定的收敛性。基于 NPS 的形态金字塔导致初始更快的收敛，与单阶段优化相比，以低 1.5 倍的运行时间实现了相似的 SSIM。通过将算法加速与提供稳定收敛的方法以及最大化收敛速度的优化方案集成到架构中，可以实现 MBIR 的加速，以提供与临床应用兼容的重建时间。
