Ultra-Fast Digital Tomosynthesis Reconstruction Using General-Purpose GPU Programming for Image-Guided Radiation Therapy

Abstract

The purpose of this work is to demonstrate an ultra-fast reconstruction technique for digital tomosynthesis (DTS) imaging based on the algorithm proposed by Feldkamp, Davis, and Kress (FDK) using standard general-purpose graphics processing unit (GPGPU) programming interface. To this end, the FDK-based DTS algorithm was programmed "in-house" with C language with utilization of 1) GPU and 2) central processing unit (CPU) cards. The GPU card consisted of 480 processing cores (2 3 240 dual chip) with 1,242 MHz processing clock speed and 1,792 MB memory space. In terms of CPU hardware, we used 2.68 GHz clock speed, 12.0 GB DDR3 RAM, on a 64-bit OS. The performance of proposed algorithm was tested on twenty-five patient cases (5 lung, 5 liver, 10 prostate, and 5 head-and-neck) scanned either with a full-fan or half-fan mode on our cone-beam computed tomography (CBCT) system. For the full-fan scans, the projections from 157.5°-202.5° (45°-scan) were used to reconstruct coronal DTS slices, whereas for the half-fan scans, the projections from both 157.5°-202.5° and 337.5°-22.5° (2 3 45°-scan) were used to reconstruct larger FOV coronal DTS slices. For this study, we chose 45°-scan angle that contained ~80 projections for the full-fan and ~160 projections with 2 3 45°-scan angle for the half-fan mode, each with 1024 3 768 pixels with 32-bit precision. Absolute pixel value differences, profiles, and contrast-to-noise ratio (CNR) calculations were performed to compare and evaluate the images reconstructed using GPU-and CPU-based implementations. The time dependence on the reconstruction volume was also tested with (512 3 512) 3 16, 32, 64, 128, and 256 slices. In the end, the GPU-based implementation achieved, at most, 1.3 and 2.5 seconds to complete full reconstruction of 512 3 512 3 256 volume, for the full-fan and half-fan modes, respectively. In turn, this meant that our implementation can process  13 projections-persecond (pps) and  18 pps for the full-fan and half-fan modes, respectively. Since commercial CBCT system nominally acquires 11 pps (with 1 gantry-revolution-per-minute), our GPU-based implementation is sufficient to handle the incoming projections data as they are acquired and reconstruct the entire volume immediately after completing the scan. In addition, on increasing the number of slices (hence volume) to be reconstructed from 16 to 256, only minimal increases in reconstruction time were observed for the GPU-based implementation where from 0.73 to 1.27 seconds and 1.42 to 2.47 seconds increase were observed for the full-fan and half-fan modes, respectively. This resulted in speed improvement of up to 87 times compared with the CPU-based implementation (for 256 slices case), with visually identical images and small pixel-value discrepancies ( 6.3%), and CNR differences ( 2.3%).With this achievement, we have shown that time allocation for DTS image reconstruction is virtually eliminated and that clinical implementation of this approach has become quite appealing. In addition, with the speed achievemen...

Digital tomosynthesis (DTS) for verification of target position in early stage lung cancer patients

“…Finally, although the use of nonclinical research software for DTS was time consuming, it is reasonable to expect that computation times for CB-DTS reconstruction of projections might be significantly reduced, for example, by using the graphic card engine 32 and enhanced computational algorithms. 33 In a clinical environment, automated DTS registration could potentially be used to verify intrafraction target position with a warning signal when CB-DTS derived target position deviated from the Reference-DTS by more than a certain threshold. In addition, DTS might be used in addition to a standard CBCT, to provide fast positional verification prior to radiation delivery, including for example when unexpected changes in the respiratory signal occur.…”

Motion compensated digital tomosynthesis

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GPU-based high-performance computing for radiation therapy

“…For another image modality called digital tomosynthesis , whose reconstruction formula is identical to that for CBCT, GPU has also been applied. The most computationally intensive backprojection part was first ported to GPU (Yan et al , 2007; Yan et al , 2008) and then the filtering part was later realized via GPU-based FFT using the CUDA library CUFFT (Park et al , 2011). …”