Total variation regularization method for 3D rotational coronary angiography

Informatik aktuell

Volumn , Issue , 2011, Pages 434-438

  Wu, Haibo ^a   Rohkohl, Christopher ^a,b   Hornegger, Joachim ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIAC MOTION; CORONARY ANGIOGRAPHY; CORONARY ARTERIES; CORONARY ARTERY DISEASE; RECONSTRUCTION ALGORITHMS; RECONSTRUCTION PROBLEMS; TOTAL VARIATION REGULARIZATION; TREATMENT PLANNING;

ALGORITHMS; ANGIOCARDIOGRAPHY; DIAGNOSIS; IMAGE DENOISING; IMAGE PROCESSING;

THREE DIMENSIONAL;

EID: 84860378328     PISSN: 1431472X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-642-19335-4_89     Document Type: Conference Paper

References (9)

1. Hansis E, Schaefer D, Doessel O., et al Evaluation of iterative sparse object reconstruction from few projections for 3-D rotational coronary angiography. IEEE Trans Med Imag. 2008; 27(11): 1548-55.
2. Schaefer D, Borgert J, Rasche V., et al Motion-compensated and gated cone beam filtered back-projection for 3-D rotational X-ray angiography. IEEE Trans Med Imag. 2006; 25(7): 890-906.
3. Donoho D. Compressed sensing. IEEE Trans Inf Theory. 2006; 52: 1289-306.
4. Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory. 2006; 52: 489-509.
5. Sidky EY, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Phys Med Bio. 2008; 53: 4777-807.
6. Jia X, Lou Y, Li R., et al GPU-based fast cone beam CT reconstruction from under-sampled and noisy projection data via total variation. Med Phys. 2010; 37: 3441-7.
7. Rohkohl C, Lauritsch G, Keil A., et al CAVAREV: an open platform for evaluating 3D and 4D cardiac vasculature reconstruction. Med Phys. 2010; 55: 2905-15.
8. Chen G, Tang J, Leng S. Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. Phys Med Biol. 2008; 55: 660-3.
9. Li M, Kudo H, Hu J., et al Improved iterative algorithm for sparse object reconstruction and its performance evaluation with micro-CT data. IEEE Trans Nucl Sci. 2004; 51: 659-66.