Abstract
Objective
Rotational coronary X-ray imaging on C-arm systems provides a multitude of diagnostic projections from the vascular tree with a single contrast agent bolus. The acquisition trajectory is typically limited to a circular arc with a fixed caudo-cranial angulation. This may cause sub- optimal projection directions for specific vessel segments for all acquired views, e.g., those segments orthogonal to the axis of rotation. In this paper, a method is presented to calculate a patient-independent acquisition trajectory with respect to vessel foreshortening and overlap for multiple vessel segments of the coronary tree. This method can be applied to artery as well as vein anatomy.
Methods
Rotational coronary venograms of 14 patients have been used to generate three-dimensional mesh representations with a semi-automatic two view modeling algorithm. The venous tree is divided into seven different vessel segments. Foreshortening and overlap of every segment are calculated and combined for all patients in a measure called obstruction value. The weighted obstruction values of all vessel segments define a cost function for the entire two-dimensional angular range of the C-arm system. Viterbi’s algorithm is used to calculate an optimal trajectory with respect to this cost function. The method is validated by leave-one-out cross-validation on the 14 rotational venography data sets and on simulated venograms of a segmented computed tomography (CT) data set. Projection images with a foreshortening value below 10% and overlap below 20% are rated ‘optimal’.
Results
In 12 (85.7%) data sets, 43% more optimal images were acquired using the presented method compared to the standard circular arc trajectory. As well, in 13 (92.8%) data sets 38% more vessel segments can be optimally visualized in the acquired images. The test on the CT data set showed that the resulting average root-mean-square error of the extracted centerline points of the segmented CT data set compared to the error based on the views from the circular arc was reduced from 2.52 to 1.55 mm.
Conclusion
In a first test, the method proved to deliver improved image quality by reducing foreshortening and overlap of vessel segments and may therefore also improve the centerline extraction accuracy of the semi-automatic two view modeling method.
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Jongbloed MR, Lamb HJ, Bax JJ, Schuijf JD, de Roos A, van der Wall EE, Schalij MJ (2005) Noninvasive visualization of the cardiac venous system using multislice computed tomography. J Am Coll Cardiol 45: 749–753
Ghersin E, Litmanovich D, Dragu R, Rispler S, Lessick J, Ofer A, Brook OR, Gruberg L, Beyar R, Engel A (2005) 16-MDCT coronary angiography versus invasive coronary angiography in acute chest pain syndrome: A blinded prospective study. Am J Roentgenol 186: 177–184
Watanabe Y, Nagayama M, Amoh Y, Fujii M, Fuku Y, Okumura A, van Cauteren M, Stuber M, Dodo Y (2002) High-resolution selective three-dimensional magnetic resonance coronary angiography with navigator-echo technique: Segment-by-segment evaluation of coronary artery stenosis. J Magn Reson Imaging 16: 238–245
Kefer J, Coche E, Legros G, Pasquet A, Grandin C, van Beers BE, Vanoverschelde JL, Gerber BL (2005) Head-to-head comparison of three-dimensional navigator-gated magnetic resonance imaging and 16-slice computed tomography to detect coronary artery stenosis in patients. J Am Coll Cardiol 46: 92–100
Blendea D, Mansour M, Shah RV, Chung J, Nandigam V, Heist EK, Mela T, Reddy VY, Manzke R, McPherson CA, Ruskin JN, Singh JP (2007) Usefulness of high-speed rotational coronary venous angiography during cardiac resynchronization therapy. Am J Cardiol 100: 1561–1565
Baim DS, Grossman W (2000) Coronary angiography. In: Baim DS, Grossman W (eds) Grossman’s cardiac catheterization, angiography, and intervention. Williams and Wilkins, Lippincott, pp 211–270
Zir LM, Miller SW, Dinsmore RE, Gilbert JP, Harthorne JW (1976) Interobserver variability in coronary angiography. Am Heart Assoc 53: 627–632
White CW, Wright CB, Doty DB, Hiratzka L, Eastham CL, Harrison DG, Marcus ML (1984) Does visual interpolation of the coronary arteriogram predict the physiologic importance of coronary stenosis?. N Engl J Med 310: 819–824
Chen SJ, Carroll JD (2000) 3D reconstruction of coronary arterial tree to optimize angiographic visualization. IEEE Trans Med Imaging 19: 318–336
Messenger JC, Chen SYJ, Carroll JD, Burchenal JEB, Kioussopoulos K, Groves BM (2000) 3D coronary reconstruction from routine single-plane coronary angiograms: Clinical validation and quantitative analysis of the right coronary artery in 100 patients. Int J Cardiovasc Imaging 16: 413–427
Movassaghi B, Rasche V, Grass M, Viergever M, Niessen W (2004) A quantitative analysis of 3D coronary modeling from two or more projections. IEEE Trans Med Imaging 23: 1517–1531
Garcia JA, Chen J, Hansgen A, Wink O, Movassaghi B, Messenger JC (2006) Rotational angiography (RA) and three-dimensional imaging (3-DRA): an available clinical tool. Int J Cardiovasc Imaging 23: 9–13
Blondel C, Malandain G, Vaillant R, Ayache N (2006) Reconstruction of coronary arteries from a single rotational X-ray projection sequence. IEEE Trans Med Imaging 25: 653–663
Jandt U, Schäfer D, Rasche V, Grass M (2007) Automatic generation of 3D coronary artery centerlines using rotational X-ray angiography. In: Hsieh J, Flynn MJ (eds) Medical imaging: physics of medical imaging. Proceedings of SPIE, vol 6510. SPIE, Bellingham, p 65104Y
Grass M, Koppe R, Klotz E, Proksa R, Kuhn MH, Aerts H, Op de Beek J, Kemkers R (1999) Three-dimensional reconstruction of high contrast objects using C-arm image intensifier projection data. Comput Med Imaging Graph 23: 311–321
Rasche V, Movassaghi B, Grass M, Schäfer D, Kühl HP, Günther RW, Bücker A (2006) Three-dimensional X-ray coronary angiography in the porcine model: a feasibility study. Acad Radiol 13: 644–651
Wink O, Kemkers R, Chen SYJ, Carroll JD (2003) Intra-procedural coronary intervention planning using hybrid 3-dimensional reconstruction techniques. Acad Radiol 10: 1433–1441
Kitslaar PH, Marquering HA, Jukema WJ, Koning G, Nieber M, Vossepoel AM, Bax JJ, Reiber JHC (2008) Automated determination of optimal angiographic viewing angles for coronary artery bifurcations from CTA data. In: Miga MI, Cleary KR (eds) Medical imaging: visualization, image-guided procedures, and modeling. Proceedings of SPIE, vol 6918. SPIE, San Diego, p 69181J
Dumay ACM, Reiber JHC, Gerbrands JJ (1984) Determination of optimal angiographic viewing angles: basic principles and evaluation study. IEEE Trans Med Imaging 13: 313–324
Green NE, Chen SYJ, Hansgen AR, Messenger JC, Groves BM, Carroll JD (2005) Angiographic views used for percutaneous coronary interventions: A three-dimensional analysis of physician-determined vs. computer-generated views. Catheter Cardiovasc Intervent 64: 451–459
Klein A, Movassaghi M, Garcia J, Hansgen A, Chen SYJ, Casserly I, Carroll JD (2007) Optimal angiographic views based on 3D reconstructed models. J Am Coll Cardiol 49(9)(Suppl B):296A
Garcia JA, Movassaghi B, Casserly IP, Klein AJ, James Chen SY, Messenger JC, Hansgen A, Wink O, Groves BM, Carroll JD (2008) Determination of optimal viewing regions for X-ray coronary angiography based on a quantitative analysis of 3D reconstructed models. Int J Cardiovasc Imaging. doi:10.1007/s10554-008-9402-5
Mansour M, Reddy VY, Singh J, Mela T, Rasche V, Ruskin J (2005) Three-dimensional reconstruction of the coronary sinus using rotation angiography. J Cardiovasc Electrophysiol 16: 675–676
Koppe R, Klotz E, Op de Beek J, Aerts H (1995) 3D vessel reconstruction based on rotational angiography. Proc CARS 1995: 101–107
Suurmond R, Wink O, Chen SYJ, Carroll JD (2005) Three- dimensional coronary angiography. In: Amini AA, Manduca A (eds) Medical imaging: physiology, function, and structure from medical images. Proceedings of SPIE, vol 5746. SPIE, San Diego, p 205
Blendea D, Shah RV, Auricchio A, Nandigam V, Orencole M, Heist EK, Reddy VY, McPherson CA, Ruskin JN, Singh JP (2007) Variability of coronary venous anatomy in patients undergoing cardiac resynchronization therapy: A high-speed rotational venography study. Heart Rhythm 4: 1155–1162
Meisel E, Pfeiffer D, Engelmann L, Tebbenjohanns J, Schubert B, Hahn S, Fleck E, Butter C (2001) Investigation of coronary venous anatomy by retrograde venography in patients with malignant ventricular tachycardia. Circulation 104: 442–447
Auricchio A, Klein H, Tockman B, Sack S, Stellbrink C, Neuzner J, Kramer A, Ding J, Pochet T, Maarse A, Spinelli J (1999) Transvenous biventricular pacing for heart failure: can the obstacles be overcome?. Am J Cardiol 83: 136D–142D
Viterbi AJ (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13: 260–269
Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, Englewood Cliffs
Albertsen AEIE, Nielsen JC, Pedersen AK, Hansen PS, Jensen HK, Mortensen PT (2005) Left ventricular lead performance in cardiac resynchronization therapy: impact of lead localization and complications. Pacing Clin Electrophysiol 28: 483–488
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Bi, J., Grass, M. & Schäfer, D. Optimization of acquisition trajectories for 3D rotational coronary venography. Int J CARS 5, 19–28 (2010). https://doi.org/10.1007/s11548-009-0398-7
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DOI: https://doi.org/10.1007/s11548-009-0398-7