Tissue-specific sparse deconvolution for low-dose CT perfusion

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8149 LNCS, Issue PART 1, 2013, Pages 114-121

  Fang, Ruogu ^a   Chen, Tsuhan ^a   Sanelli, Pina C ^b  

^a School of Operations Research and Industrial Engineering   (United States)

^b WEILL CORNELL MEDICAL COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CEREBRAL PERFUSION; CEREBROVASCULAR DISEASE; DIAGNOSTIC ACCURACY; ISCHEMIC PENUMBRA; JOINT SPATIO-TEMPORAL; NORMAL TISSUE; PERFUSION-CT; TISSUE SPECIFICS;

DIAGNOSIS; RESTORATION;

TISSUE;

ALGORITHM; ARTICLE; BLOOD FLOW VELOCITY; BRAIN; BRAIN ANGIOGRAPHY; BRAIN CIRCULATION; BRAIN ISCHEMIA; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; METHODOLOGY; PATHOPHYSIOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SCINTIGRAPHY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BLOOD FLOW VELOCITY; BRAIN; BRAIN ISCHEMIA; CEREBRAL ANGIOGRAPHY; CEREBROVASCULAR CIRCULATION; HUMANS; PERFUSION IMAGING; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84894610678     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40811-3_15     Document Type: Conference Paper

References (13)

1. Miles, K.A., Griffiths, M.R.: Perfusion CT: a worthwhile enhancement? British Journal of Radiology 76(904), 220-231 (2003)
2. Wintermark,M., Lev, M.: Fda investigates the safety of brain perfusion CT. American Journal of Neuroradiology 31(1), 2-3 (2010)
3. Fang, R., Chen, T., Sanelli, P.C.: Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries. In: Ayache, N., Delingette, H., Golland, P.,Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 272-280. Springer, Heidelberg (2012)
4. Fang, R., Chen, T., Sanelli, P.C.: Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. Medical Image Analysis (2013)
5. He, L., Orten, B., Do, S.,Karl,W., Kambadakone, A., Sahani, D., Pien, H.: A spatio-temporal deconvolution method to improve perfusion CT quantification. IEEE Transactions on Medical Imaging 29(5), 1182-1191 (2010)
6. Calamante, F., Gadian, D., Connelly, A.: Quantification of bolus-tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization. Magnetic Resonance in Medicine 50(6), 1237-1247 (2003)
7. Yang, J.,Wright, J.,Huang, T.,Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1-8. IEEE (2008)
8. Zhang, S., Zhan, Y., Metaxas, D.: Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis (2012)
9. Hoeffner, E., Case, I., Jain, R., Gujar, S., Shah, G., Deveikis, J., Carlos, R., Thompson, B., Harrigan, M., Mukherji, S.: Cerebral perfusion CT: Technique and clinical applications. Radiology 231(3), 632-644 (2004)
10. Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Transactions on Medical Imaging 18(10), 897-908 (1999)
11. Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689-696. ACM (2009)
12. Britten, A., Crotty, M., Kiremidjian, H., Grundy, A., Adam, E.: The addition of computer simulated noise to investigate radiation dose and image quality in images with spatial correlation of statistical noise: an example application to X-ray CT of the brain. British Journal of Radiology 77(916), 323-328 (2004)
13. Wittsack, H., Wohlschläger, A., Ritzl, E., Kleiser, R., Cohnen, M., Seitz, R., Mödder, U.: CT-perfusion imaging of the human brain: advanced deconvolution analysis using circulant singular value decomposition. Computerized Medical Imaging and Graphics 32(1), 67-77 (2008)