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1.G. T. Herman and R. M. Lewitt, “Evaluation of a preprocessing algorithm for truncated ct projections,” J. Comput. Assisted Tomogr. 5, 127135 (1981).
2.B. Ohnesorge, T. Flohr, K. Schwarz, J. P. Heiken, and J. P. Bae, “Efficient correction for CT image artifacts caused by objects extending outside the scan field of view,” Med. Phys. 27(1), 3946 (2000).
3.J. Hsieh, E. Chao, J. Thibault, B. Grekowicz, A. Horst, S. McOlash, and T. J. Myers, “A novel reconstruction algorithm to extend the CT scan field-of-view,” Med. Phys. 31(9), 23852391 (2004).
4.K. Sourbelle, M. Kachelriess, and W. A. Kalender, “Reconstruction from truncated projections in CT using adaptive detruncation,” Eur. Radiol. 15(5), 10081014 (2005).
5.B. Zellerhoff, B. Scholz, E. P. Ruehrnschopf, and T. Brunner, “Low contrast 3D-reconstruction from C-arm data,” Proc. SPIE 5745, 646655 (2005).
6.J. D. Maltz, S. Bose, H. P. Shukla, and A. R. Bani-Hashemi, “CT truncation artifact removal using water-equivalent thicknesses derived from truncated projection data,” in IEEE Engineering in Medicine and Biology Society (IEEE EMBS, Lyon, 2007), pp. 29052911.
7.K. J. Ruchala, G. H. Olivera, J. M. Kapatoes, R. J. Reckwerdt, and T. R. Mackie, “Methods for improving limited field-of-view radiotherapy reconstructions using imperfect a priori images,” Med. Phys. 29, 25902605 (2002).
8.J. Wiegert, M. Bertram, T. Netsch, J. Wulff, J. Weese, and G. Rose, “Projection extension for region of interest imaging in cone-beam CT,” Acad. Radiol. 12, 10101023 (2005).
9.D. Kolditz, Y. Kyriakou, and W. A. Kalender, “Volume-of-interest (VOI) imaging in C-arm flat-detector CT for high image quality at reduced dose,” Med. Phys. 37(6), 27192730 (2010).
10.K. Sen Sharma, C. Holzner, D. M. Vasilescu, X. Jin, S. Narayanan, M. Agah, E. A. Hoffman, H. Yu, and G. Wang, “Scout-view assisted interior micro-CT,” Phys. Med. Biol. 58, 42974314 (2013).
11.H. Yu, G. Cao, L. Burk, Y. Lee, J. Lu, P. Santago, O. Zhou, and G. Wang, “Compressive sampling based interior reconstruction for dynamic carbon nanotube micro-CT,” J. Xray. Sci. Technol. 17, 295303 (2009).
12.R. Chityala, K. R. Hoffmann, D. R. Bednarek, and S. Rudin, “Region of interest (ROI) computed tomography,” Proc. SPIE 5368(2), 534541 (2004).
13.L. Chen, C. C. Shaw, M. C. Altunbas, C. J. Lai, X. Liu, T. Han, T. Wang, W. T. Yang, and G. J. Whitman, “Feasibility of volume-of-interest (VOI) scanning technique in cone beam breast CT – A preliminary study,” Med. Phys. 35(8), 34823490 (2008).
14.S. Schafer, P. B. Noel, A. Walczak, and K. Hoffmann, “Filtered region of interest cone-beam rotational angiography,” Med. Phys. 37(2), 694703 (2010).
15.F. Dennerlein and A. Maier, “Approximate truncation robust computed tomography - ATRACT,” Phys. Med. Biol. 58, 61336148 (2013).
16.L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone beam algorithm,” J. Opt. Soc. Am. A 1, 612619 (1984).
17.Y. Xia, F. Dennerlein, S. Bauer, H. Hofmann, J. Hornegger, and A. Maier, “Scaling calibration in region of interest reconstruction with the 1D and 2D ATRACT algorithm,” Int. J. Comput. Assisted Radiol. Surg. 9, 345356 (2014).
18.Y. Ye, H. Yu, and G. Wang, “Exact interior reconstruction from truncated limited-angle projection data,” Int. J. Biomed. Imaging 2008, 16.
19.H. Kudo, M. Courdurier, F. Noo, and M. Defrise, “Tiny a priori knowledge solves the interior problem in computed tomography,” Phys. Med. Biol. 52(9), 22072231 (2008).
20.H. Yu and G. Wang, “Compressed sensing based interior tomography,” Phys. Med. Biol. 54(9), 27912805 (2009).
21.H. Yu, G. Wang, J. Hsieh, D. W. Entrikin, S. Ellis, B. Liu, and J. J. Carr, “Compressive sensing-based interior tomography: Preliminary clinical application,” J. Comput. Assisted Tomogr. 35, 762764 (2011).
22.J. Yang, W. Cong, M. Jiang, and G. Wang, “Theoretical study on high order interior tomography,” J. X-Ray Sci. Technol. 20, 423436 (2012).
23.E. Katsevich, A. Katsevich, and G. Wang, “Stability of the interior problem with polynomial attenuation in the region of interest,” Inverse Probl. 28, 065022 (2012).
24.D. Kolditz, M. Meyer, Y. Kyriakou, and W. A. Kalender, “Comparison of extended field-of-view reconstructions in C-arm flat-detector CT using patient size, shape or attenuation information,” Phys. Med. Biol. 56(1), 3956 (2011).

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Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be . It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow.

A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anterior–posterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate.

The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique.

The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.


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