CAJ | 학술논문

本文基于数据重排方法,提出了T-BPF (Tent-BPF)算法,该算法先将锥束投影数据重排成平行投影数据,然后使用一种推导的BPF型算法重建重排后的平行投影数据. T-BPF算法将原BPF算法反投影中变化的角度积分限变成固定的,反投影中各层循环之间没有了相关性,这意味着T-BPF算法较原BPF算法具有更好的可并行性.实验结果显示:使用GPU对2563的Shepp-Logan体模的图像重建进行并行加速, T-BPF算法在保证重建质量的前提下,加速比达到了1036,较原BPF算法有很大提升. T-BPF算法为截断投影数据的3D图像快速重建提供了方法.
본문기우수거중배방법,제출료T-BPF (Tent-BPF)산법,해산법선장추속투영수거중배성평행투영수거,연후사용일충추도적BPF형산법중건중배후적평행투영수거. T-BPF산법장원BPF산법반투영중변화적각도적분한변성고정적,반투영중각층순배지간몰유료상관성,저의미착T-BPF산법교원BPF산법구유경호적가병행성.실험결과현시:사용GPU대2563적Shepp-Logan체모적도상중건진행병행가속, T-BPF산법재보증중건질량적전제하,가속비체도료1036,교원BPF산법유흔대제승. T-BPF산법위절단투영수거적3D도상쾌속중건제공료방법.
In circular cone-beam computed tomography (CT), to solve the 3D image reconstruction from truncated projection data which has no truncation along PI-line, backprojection-filtration (BPF) algorithm is a preferred choice. However, in its performance the integral interval of backprojection is variable for different PI-line, rendering the parallelism performance of backprojection low. So it cannot satisfy the requirement of fast image reconstruction in practical CT system. In this paper, a tent BPF (T-BPF) algorithm is developed based on the data rebinning method, which was performed by first rearranging the cone-beam data to tent-like parallel-beam data, and then applying the proposed BPF-type algorithm to reconstruct images from the rearranged data. T-BPF turns the variable view-angle integral interval of backprojection into a fixed integral interval, and there are no relations in the loops of backprojection calculation, which means the parallelism performance of T-BPF is an improvement over that of the original BPF algorithm. The results of experiments show that compared with the conventional CPU implementation, the GPU accelerated method provides images of the same quality with a speedup factor 1036 for the reconstruction of 2563 Shepp-Logan model. The speedup factor is an improvement in the original BPF algorithm. T-BPF provides a solution for the 3D fast reconstruction from truncated data.