Method comparison of automated matching software-assisted cone-beam CT and stereoscopic kilovoltage x-ray positional verification image-guided radiation therapy for head and neck cancer: A prospective analysis
Department of Radiation Oncology, University of Texas Health Science Center-San Antonio, San Antonio, TX, USA. Physics in Medicine and Biology
(Impact Factor: 2.76).
12/2009; 54(24):7401-15. DOI: 10.1088/0031-9155/54/24/010
We sought to characterize interchangeability and agreement between cone-beam computed tomography (CBCT) and digital stereoscopic kV x-ray (KVX) acquisition, two methods of isocenter positional verification currently used for IGRT of head and neck cancers (HNC). A cohort of 33 patients were near-simultaneously imaged by in-room KVX and CBCT. KVX and CBCT shifts were suggested using manufacturer software for the lateral (X), vertical (Y) and longitudinal (Z) dimensions. Intra-method repeatability, systematic and random error components were calculated for each imaging modality, as were recipe-based PTV expansion margins. Inter-method agreement in each axis was compared using limits of agreement (LOA) methodology, concordance analysis and orthogonal regression. 100 daily positional assessments were performed before daily therapy in 33 patients with head and neck cancer. Systematic error was greater for CBCT in all axes, with larger random error components in the Y- and Z-axis. Repeatability ranged from 9 to 14 mm for all axes, with CBCT showing greater repeatability in 2/3 axes. LOA showed paired shifts to agree 95% of the time within +/-11.3 mm in the X-axis, +/-9.4 mm in the Y-axis and +/-5.5 mm in the Z-axis. Concordance ranged from 'mediocre' to 'satisfactory'. Proportional bias was noted between paired X- and Z-axis measures, with a constant bias component in the Z-axis. Our data suggest non-negligible differences in software-derived CBCT and KVX image-guided directional shifts using formal method comparison statistics.
Available from: William Y Song
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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 x 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 x 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 x 45°-scan angle for the half-fan mode, each with 1024 x 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 x 512) x 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 x 512 x 256 volume, for the full-fan and half-fan modes, respectively. In turn, this meant that our implementation can process > 13 projections-per-second (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 achievement, further image processing and real-time applications that was prohibited prior due to time restrictions can now be tempered with.
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High resolution digital imaging systems were recently introduced to capture and visualize serum protein electrophoresis results. In this study, we compared the performance of five, experienced interpreters using digital images and physical gels to identify and characterize monoclonal gammopathies by immunofixation.
Design and methods:
Immunofixation gels were generated using Sebia's HYDRASYS and digital images were captured with Sebia's Gelscan system. Interpreters blindly reviewed 200 consecutively obtained immunofixation results using physical gels, low resolution (LR) images, and high resolution (HR) images.
Interpretations of the physical gels were significantly more sensitive (p≤0.01) than LR and HR images, and significantly more specific (p<0.001) than the LR images. Interpreters had a sensitivity of 82.0% (45.8-95.7) using the LR images and a specificity of 71.0% (47.8-91.3); using the HR images interpreters had a sensitivity of 80.4% (68.1-86.8) and specificity of 91.8% (80.3-97.8). There was 73.6% agreement between the HR digital images and the physical gel for immunoglobulin isotype characterization. Interpreters using digital images collectively missed 19 patients with monoclonal immunoglobulins that were identified using physical gels.
Interpreters using digital images had significantly different performance than when using physical agarose gels. Differences were most pronounced for low concentration monoclonal gammopathies (<0.3 g/dL) and for complex patterns. Between-interpreter agreement was also lower using digital images. While digital images may serve as a useful resource for retrospective analysis and review of previous results, they are not equivalent to physical gels. Additional studies are warranted to explore the clinical impact of these observed differences.
Available from: William Y Song
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Utilization of respiratory correlated four-dimensional cone-beam computed tomography (4DCBCT) has enabled verification of internal target motion and volume immediately prior to treatment. However, with current standard CBCT scan, 4DCBCT poses challenge for reconstruction due to the fact that multiple phase binning leads to insufficient number of projection data to reconstruct and thus cause streaking artifacts. The purpose of this study is to develop a novel 4DCBCT reconstruction algorithm framework called motion-map constrained image reconstruction (MCIR), that allows reconstruction of high quality and high phase resolution 4DCBCT images with no more than the imaging dose as well as projections used in a standard free breathing 3DCBCT (FB-3DCBCT) scan.
The unknown 4DCBCT volume at each phase was mathematically modeled as a combination of FB-3DCBCT and phase-specific update vector which has an associated motion-map matrix. The motion-map matrix, which is the key innovation of the MCIR algorithm, was defined as the matrix that distinguishes voxels that are moving from stationary ones. This 4DCBCT model was then reconstructed with compressed sensing (CS) reconstruction framework such that the voxels with high motion would be aggressively updated by the phase-wise sorted projections and the voxels with less motion would be minimally updated to preserve the FB-3DCBCT. To evaluate the performance of our proposed MCIR algorithm, we evaluated both numerical phantoms and a lung cancer patient. The results were then compared with the (1) clinical FB-3DCBCT reconstructed using the FDK, (2) 4DCBCT reconstructed using the FDK, and (3) 4DCBCT reconstructed using the well-known prior image constrained compressed sensing (PICCS).
Examination of the MCIR algorithm showed that high phase-resolved 4DCBCT with sets of up to 20 phases using a typical FB-3DCBCT scan could be reconstructed without compromising the image quality. Moreover, in comparison with other published algorithms, the image quality of the MCIR algorithm is shown to be excellent.
This work demonstrates the potential for providing high-quality 4DCBCT during on-line image-guided radiation therapy (IGRT), without increasing the imaging dose. The results showed that (at least) 20 phase images could be reconstructed using the same projections data, used to reconstruct a single FB-3DCBCT, without streak artifacts that are caused by insufficient projections.
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