No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Real-time out-of-plane artifact subtraction tomosynthesis imaging using prior CT for scanning beam digital x-ray system
1.A. R. Pineda, S. Yoon, D. S. Paik, and R. Fahrig, “Optimization of a tomosynthesis system for the detection of lung nodules,” Med. Phys. 33, 1372–1379 (2006).
2.S. Yoon, B. P. Wilfley, K. Jasperson, G. Krishna, and R. Fahrig, “Real-time scanning beam digital x-ray image guidance system for transbronchial needle biopsy,” Proc. SPIE 7961, 79611F (2011).
5.E. G. Solomon, B. P. Wilfley, M. S. Van Lysel, A. W. Joseph, and J. A. Heanue, “Scanning-beam digital x-ray (SBDX) system for cardiac angiography,” Proc. SPIE 3659, 246–257 (1999).
6.M. A. Speidel, B. P. Wilfley, J. M. Star-Lack, J. A. Heanue, T. D. Betts, and M. S. Van Lysel, “Comparison of entrance exposure and signal-to-noise ratio between an SBDX prototype and a wide-beam cardiac angiographic system,” Med. Phys. 33, 2728–2743 (2006).
7.M. A. Speidel, B. P. Wilfley, J. M. Star-Lack, J. A. Heanue, and M. S. Van Lysel, “Scanning-beam digital x-ray (SBDX) technology for interventional and diagnostic cardiac angiographym,” Med. Phys. 33, 2714–2727 (2006).
8.M. A. Speidel, M. T. Tomkowiak, A. N. Raval, and M. S. Van Lysel, “Three-dimensional tracking of cardiac catheters using an inverse geometry x-ray fluoroscopy system,” Med. Phys. 37, 6377–6389 (2010).
9.M. T. Tomkowiak, M. A. Speidel, A. N. Raval, and M. S. Van Lysel, “Calibration-free device sizing using an inverse geometry x-ray system,” Med. Phys. 38, 283–293 (2011).
11.T. Mertelmeier, J. Orman, W. Haerer, and M. K. Dudam, “Optimizing filtered backprojection reconstruction for a breast tomosynthesis prototype device Thomas,” Proc. SPIE 6142, 61420–61420F (12pp.) (2006).
12.D. J. Godfrey, H. P. McAdams, and J. T. Dobbins III, “Optimization of the matrix inversion tomosynthesis (MITS) impulse response and modulation transfer function characteristics for chest imaging,” Med. Phys. 33, 655–667 (2006).
13.B. Li, G. B. Avinash, J. W. Eberhard, and B. E. H. Claus, “Optimization of slice sensitivity profile for radiographic tomosynthesis,” Med. Phys. 34, 2907–2916 (2007).
14.Y. Schwarz, J. Greif, H. D. Becker, A. Ernst, and A. Mehta, “Real-time electromagnetic navigation bronchoscopy to peripheral lung lesions using overlaid CT images: The first human study,” Chest 129, 988–994 (2006).
15.Y. Long, J. A. Fessler, and J. M. Balter, “Accuracy estimation for projection-to-volume targeting during rotational therapy: A feasibility study,” Med. Phys. 37, 2480–2490 (2010).
16.R. Zeng, J. A. Fessler, and J. M. Balter, “Respiratory motion estimation from slowly rotating x-ray projections: Theory and simulation,” Med. Phys. 32, 984–991 (2005).
17.L. Ren, J. Zhang, D. Thongphiew, D. J. Godfrey, Q. J. Wu, S.-M. Zhou, and F.-F. Yin, “A novel digital tomosynthesis (DTS) reconstruction method using a deformation field map,” Med. Phys. 35, 3110–3115 (2008).
18.J. Wang and X. Gu, “Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT,” Med. Phys. 40, 101912 (11pp.) (2013).
20.Y. Zhang, F.-F. Yin, W. P. Segars, and L. Ren, “A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections,” Med. Phys. 40, 121701 (16pp.) (2013).
21.K. Zeng, H. Yu, S. Zhao, L. L. Fajardo, C. Ruth, Z. Jing, and G. Wang, “Digital tomosynthesis aided by low-resolution exact computed tomography,” J. Comput. Assist. Tomo. 31, 976–983 (2007).
22.J. G. Kim, S. O. Jin, M. H. Cho, and S. Y. Lee, “Inter-plane artifact suppression in tomosynthesis using 3D CT image data,” Biomed. Eng. Online 10, 106 (15pp.) (2011).
23.L. Ren, Y. Zhang, and F.-F. Yin, “A limited-angle intrafraction verification (LIVE) system for radiation therapy,” Med. Phys. 41, 020701 (9pp.) (2014).
25.M. Wu and R. Fahrig, “Blurring artifacts simulation using blur-and-add model for scanning beam digital x-ray tomosynthesis system,” in Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (2013), pp. 353–356.
26.G. Nelson, S. Yoon, G. Krishna, B. Wilfley, and R. Fahrig, “Patient dose simulations for scanning-beam digital x-ray tomosynthesis of the lungs,” Med. Phys. 11, 111917 (11pp.) (2013).
28.Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Med. Imaging 13, 600–612 (2004).
29.W. J. Kostis, A. P. Reeves, D. F. Yankelevitz, and C. I. Henschke, “Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images,” IEEE Trans. Med. Imaging 22, 1259–1274 (2003).
30.K. Lange, M. Bahn, and R. Little, “A theoretical study of some maximum likelihood algorithms for emission and transmission tomography,” IEEE Trans. Med. Imaging 6, 106–114 (1987).
31.T. Wu, R. H. Moore, E. A. Rafferty, and D. B. Kopans, “A comparison of reconstruction algorithms for breast tomosynthesis,” Med. Phys. 31, 2636–2647 (2004).
Article metrics loading...
The scanning beam digital x-ray system (SBDX) is an inverse geometry fluoroscopic system with high dose efficiency and the ability to perform continuous real-time tomosynthesis in multiple planes. This system could be used for image guidance during lung nodule biopsy. However, the reconstructed images suffer from strong out-of-plane artifact due to the small tomographic angle of the system.
The authors propose an out-of-plane artifact subtraction tomosynthesis (OPAST) algorithm that utilizes a prior CT volume to augment the run-time image processing. A blur-and-add (BAA) analytical model, derived from the project-to-backproject physical model, permits the generation of tomosynthesis images that are a good approximation to the shift-and-add (SAA) reconstructed image. A computationally practical algorithm is proposed to simulate images and out-of-plane artifacts from patient-specific prior CT volumes using the BAA model. A 3D image registration algorithm to align the simulated and reconstructed images is described. The accuracy of the BAA analytical model and the OPAST algorithm was evaluated using three lung cancer patients’ CT data. The OPAST and image registration algorithms were also tested with added nonrigid respiratory motions.
Image similarity measurements, including the correlation coefficient, mean squared error, and structural similarity index, indicated that the BAA model is very accurate in simulating the SAA images from the prior CT for the SBDX system. The shift-variant effect of the BAA model can be ignored when the shifts between SBDX images and CT volumes are within ±10 mm in the x and y directions. The nodule visibility and depth resolution are improved by subtracting simulated artifacts from the reconstructions. The image registration and OPAST are robust in the presence of added respiratory motions. The dominant artifacts in the subtraction images are caused by the mismatches between the real object and the prior CT volume.
Their proposed prior CT-augmented OPAST reconstruction algorithm improves lung nodule visibility and depth resolution for the SBDX system.
Full text loading...
Most read this month