Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-10-2018 | Original Article

Tomosynthesis implementation with adaptive online calibration on clinical C-arm systems

Authors: Khanlian Chung, Lothar R. Schad, Frank G. Zöllner

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 10/2018

Abstract

Purpose

Cone beam computed tomography (CBCT) systems offer physicians crucial 3D and 2D imaging capabilities during interventions. However, certain medical applications only require very specific information from the CBCTs (e.g., determination of the position of high-contrast objects). In diagnostics, tomosynthesis techniques can be used in these cases to minimize dose exposure. Therefore, integrating such techniques on CBCT systems could also be beneficial for interventions. In this paper, we investigate the performance of our implementation of circular tomosynthesis on a CBCT device.

Methods

The tomosynthesis scan trajectory is realized with step-and-shoot on a clinical C-arm device. The online calibration algorithm uses conventionally acquired 3D CBCT of the scanned object as prior knowledge to correct the imaging geometries. The online calibration algorithm was compared to an offline calibration to test its performance. A ball bearing phantom was used to evaluate the reconstructions with respect to geometric distortions. The evaluation was done for three different scenarios to test the robustness of our tomosynthesis implementation against object deviations (e.g., pen) and different object positioning.

Results

The circular tomosynthesis was tested on a ball bearing and an anthropomorphic phantom. The results show that the calibration is robust against isocenter shifts and object deviations in the CBCT. All reconstructions used 100 projections and displayed limited angle artifacts. The accuracy of the positions and shapes of high-contrast objects were, however, determined precisely. (The maximal center position deviation is 0.31 mm.)

Conclusion

For medical procedures that primarily determine the precise position of high-contrast objects, circular tomosynthesis could offer an approach to reduce dose exposure.

Yang K, Kwan ALC, Miller DF, Boone JM (2006) A geometric calibration method for cone beam CT systems. Med Phys 33(6Part1):1695–1706. https://doi.org/10.1118/1.2198187 CrossRefPubMedPubMedCentral

Claus BEH (2006) Geometry calibration phantom design for 3D imaging. In: Flynn MJ, Hsieh J (eds) Proceeding SPIE 6142, Medical imaging 2006: physics of medical imaging, 61422E. https://doi.org/10.1117/12.652342

Dementhon DF, Davis LS (1995) Model-based object pose in 25 lines of code. Int J Comput Vis 15(1–2):123–141. https://doi.org/10.1007/BF01450852 CrossRef

Iii JTD, Godfrey DJ (2003) Digital x-ray tomosynthesis: current state of the art and clinical potential. Phys Med Biol 48(19):R65. https://doi.org/10.1088/0031-9155/48/19/R01 CrossRef

Claus B, Chan H-P (2014) Digital breast tomosynthesis reconstruction with an adaptive voxel grid. In: Proceeding SPIE 9033, Medical imaging 2014: physics of medical imaging, 90335A. https://doi.org/10.1117/12.2043541

Gur D, Abrams GS, Chough DM, Ganott MA, Hakim CM, Perrin RL, Rathfon GY, Sumkin JH, Zuley ML, Bandos AI (2009) Digital breast tomosynthesis. Observer performance study. AJR Am J Roentgenol 193(2):586–591. https://doi.org/10.2214/AJR.08.2031 CrossRefPubMed

Langan DA, Claus BEH, Al Assad O, Trousset Y, Riddell C, Avignon G, Solomon SB, Lai H, Wang X (2015) Interventional C-arm tomosynthesis for vascular imaging: initial results. In: Hoeschen C, Kontos D, Flohr TG (eds). SPIE, p 94125N

Claus BE, Langan DA, Al Assad O, Wang X (2015) Circular tomosynthesis for neuro perfusion imaging on an interventional C-arm. In: Hoeschen C, Kontos D, Flohr TG (eds). SPIE, p 94122A

Nett BE, Zambelli J, Riddell C, Belanger B, Chen G-H (2007) Circular tomosynthesis implemented with a clinical interventional flat-panel based C-Arm: initial performance study. In: Proceeding SPIE 6510, Medical imaging 2007: physics of medical imaging, 65101N. https://doi.org/10.1117/12.713789

10.

Nett B, Tang J, Leng S, Chen G-H (2008) Tomosynthesis via total variation minimization reconstruction and prior image constrained compressed sensing (PICCS) on a C-arm System. In: Proceedings of SPIE—the international society for optical engineering, vol 6913: nihpa92672. https://doi.org/10.1117/12.771294

11.

Stevens GM, Birdwell RL, Beaulieu CF, Ikeda DM, Pelc NJ (2003) Circular tomosynthesis: potential in imaging of breast and upper cervical spine-preliminary phantom and in vitro study. Radiology 228(2):569–575. https://doi.org/10.1148/radiol.2282020295 CrossRefPubMed

12.

Chung K, Karstensen L, Siegfarth M, Schad LR, Zöllner FG (2017) Investigation of scatter radiation of non-circular X-ray scan trajectories. Fully3D 2017

13.

Solomon EG, Wilfley BP, van Lysel MS, Joseph AW, Heanue JA (1999) Scanning-beam digital x-ray (SBDX) system for cardiac angiography. In: Proceeding SPIE 3659, Medical imaging 1999: physics of medical imaging. https://doi.org/10.1117/12.349499

14.

Speidel MA, Wilfley BP, Star-Lack JM, Heanue JA, van Lysel MS (2006) Scanning-beam digital x-ray (SBDX) technology for interventional and diagnostic cardiac angiography. Med Phys 33(8):2714–2727. https://doi.org/10.1118/1.2208736 CrossRefPubMed

15.

Slagowski JM, Dunkerley DAP, Hatt CR, Speidel MA (2016) A geometric calibration method for inverse geometry computed tomography using P-matrices. In: Proceedings of SPIE—the international society for optical engineering, p 9783. https://doi.org/10.1117/12.2216565

16.

Ouadah S, Stayman JW, Gang GJ, Ehtiati T, Siewerdsen JH (2016) Self-calibration of cone-beam CT geometry using 3D–2D image registration. Phys Med Biol 61(7):2613–2632. https://doi.org/10.1088/0031-9155/61/7/2613 CrossRefPubMedPubMedCentral

17.

Muders J, Hesser J (2014) Stable and robust geometric self-calibration for cone-beam CT using mutual information. IEEE Trans Nucl Sci 61(1):202–217. https://doi.org/10.1109/TNS.2013.2293969 CrossRef

18.

Panetta D, Belcari N, Del Guerra A, Moehrs S (2008) An optimization-based method for geometrical calibration in cone-beam CT without dedicated phantoms. Phys Med Biol 53(14):3841–3861. https://doi.org/10.1088/0031-9155/53/14/009 CrossRefPubMed

19.

Meng Y, Gong H, Yang X (2013) Online geometric calibration of cone-beam computed tomography for arbitrary imaging objects. IEEE Trans Med Imaging 32(2):278–288. https://doi.org/10.1109/TMI.2012.2224360 CrossRefPubMed

20.

Chen G-H, Leng S, Mistretta CA (2005) A novel extension of the parallel-beam projection-slice theorem to divergent fan-beam and cone-beam projections. Med Phys 32(3):654–665. https://doi.org/10.1118/1.1861792 CrossRefPubMed

21.

van Aarle W, Palenstijn WJ, de Beenhouwer J, Altantzis T, Bals S, Batenburg KJ, Sijbers J (2015) The ASTRA Toolbox: a platform for advanced algorithm development in electron tomography. Ultramicroscopy 157:35–47. https://doi.org/10.1016/j.ultramic.2015.05.002 CrossRefPubMed

22.

van Aarle W, Palenstijn WJ, Cant J, Janssens E, Bleichrodt F, Dabravolski A, de Beenhouwer J, Joost Batenburg K, Sijbers J (2016) Fast and flexible X-ray tomography using the ASTRA toolbox. Opt Exp 24(22):25129–25147. https://doi.org/10.1364/OE.24.025129 CrossRef

23.

Gang GJ, Stayman JW, Ehtiati T, Siewerdsen JH (2015) Task-driven image acquisition and reconstruction in cone-beam CT. Phys Med Biol 60(8):3129–3150. https://doi.org/10.1088/0031-9155/60/8/3129 CrossRefPubMedPubMedCentral

24.

Chen G-H, Tang J, Leng S (2008) Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. Med Phys 35(2):660. https://doi.org/10.1118/1.2836423 CrossRefPubMedPubMedCentral

25.

Lauzier PT, Tang J, Chen G-H (2012) Prior image constrained compressed sensing: implementation and performance evaluation. Med Phys 39(1):66. https://doi.org/10.1118/1.3666946 CrossRefPubMed

26.

Schnurr A-K, Chung K, Schad LR, Zöllner FG (2018) Abstract. Deep residual learning for limited angle artefact correction. In: Maier A, Deserno TM, Handels H et al (eds) Bildverarbeitung für die Medizin 2018. Springer, Berlin, p 280CrossRef

Title: Tomosynthesis implementation with adaptive online calibration on clinical C-arm systems
Authors: Khanlian Chung
Lothar R. Schad
Frank G. Zöllner
Publication date: 01-10-2018
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 10/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1782-y

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Tomosynthesis implementation with adaptive online calibration on clinical C-arm systems

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 10/2018

Myocardium segmentation from DE MRI with guided random walks and sparse shape representation

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

Robust extraction for low-contrast liver tumors using modified adaptive likelihood estimation

Probe versus microscope: a comparison of different methods for image-to-patient registration

Correction to: RF-ablation pattern shaping employing switching channels of dual bipolar needle electrodes: ex vivo results

Towards computer-assisted TTTS: Laser ablation detection for workflow segmentation from fetoscopic video