Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

Medical Image Analysis

Volumn 17, Issue 4, 2013, Pages 417-428

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

^a School of Chemical and Molecular Engineering   (United States)

^b WEILL CORNELL MEDICAL COLLEGE   (United States)

Author keywords

Computed tomography perfusion; Deconvolution algorithm; Online dictionary learning; Radiation dosage; Sparse representation

Indexed keywords

COMPUTED TOMOGRAPHY; DECONVOLUTION ALGORITHM; ONLINE DICTIONARY LEARNING; RADIATION DOSAGE; SPARSE REPRESENTATION;

COMPUTERIZED TOMOGRAPHY; DECONVOLUTION; TISSUE;

E-LEARNING;

ADULT; ALGORITHM; ARTICLE; BRAIN BLOOD FLOW; BRAIN ISCHEMIA; BRAIN MAPPING; BRAIN PERFUSION; CASE REPORT; COMPUTED TOMOGRAPHY PERFUSION; COMPUTER ASSISTED TOMOGRAPHY; CONTRAST ENHANCEMENT; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; HEMODYNAMICS; HUMAN; HUMAN TISSUE; IMAGING SYSTEM; MALE; NEUROPATHOLOGY; ONLINE DICTIONARY LEARNING; ONLINE SYSTEM; PRIORITY JOURNAL; RADIATION DOSE; SPARSE PERFUSION DECONVOLUTION;

ARTIFICIAL INTELLIGENCE; BRAIN; CEREBRAL ANGIOGRAPHY; CEREBROVASCULAR CIRCULATION; CEREBROVASCULAR DISORDERS; DATABASES, FACTUAL; DICTIONARIES, MEDICAL; HUMANS; INFORMATION STORAGE AND RETRIEVAL; ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; PERFUSION IMAGING; RADIATION DOSAGE; RADIATION PROTECTION; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84875794414     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2013.02.005     Document Type: Article

References (57)

1. Aharon M., Elad M., Bruckstein A. K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 2006, 54:4311-4322.
2. Andersen I., Szymkowiak A., Rasmussen C., Hanson L., Marstrand J., Larsson H., Hansen L. Perfusion quantification using gaussian process deconvolution. Magnetic Resonance in Medicine 2002, 48:351-361.
3. 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 2004, 77:323-328.
4. 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 2003, 50:1237-1247.
5. Candès E., Romberg J., Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 2006, 52:489-509.
6. Cenic A., Nabavi D.G., Craen R.A., Gelb A.W., Lee T.Y. Dynamic CT measurement of cerebral blood flow: a validation study. American Journal of Neuroradiology 1999, 20:63-73.
7. Cenic A., Nabavi D.G., Craen R.A., Gelb A.W., Lee T.Y. A CT method to measure hemodynamics in brain tumors: validation and application of cerebral blood flow maps. American Journal of Neuroradiology 2000, 21:462-470.
8. Chen S., Billings S., Luo W. Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control 1989, 50:1873-1896.
9. Chen S., Donoho D., Saunders M. Atomic decomposition by basis pursuit. SIAM Review 2001, 43:129-159.
10. Donoho D. Compressed sensing. IEEE Transactions on Information Theory 2006, 52:1289-1306.
11. Donoho D. For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics 2006, 59:797-829.
12. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. The Annals of Statistics 2004, 32:407-499.
13. Elad M., Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing 2006, 15:3736-3745.
14. Engan K., Aase S., Hakon Husoy J. Method of optimal directions for frame design. Proceedings of 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing 1999, 2443-2446. IEEE.
15. Eslami R., Radha H. Translation-invariant contourlet transform and its application to image denoising. IEEE Transactions on Image Processing 2006, 15:3362-3374.
16. Fang R., Chen T., Sanelli P. Sparsity-based deconvolution of low-dose brain perfusion CT in subarachnoid hemorrhage patients. 9th IEEE International Symposium on Biomedical Imaging (ISBI) 2012, 872-875. IEEE.
17. Fang R., Chen T., Sanelli P. Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, 2012:272-280.
18. Fieselmann A., Kowarschik M., Ganguly A., Hornegger J., Fahrig R. Deconvolution-based CT and MR brain perfusion measurement: theoretical model revisited and practical implementation details. Journal of Biomedical Imaging 2011, 2011:14.
19. Figueiredo M., Nowak R., Wright S. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing 2007, 1:586-597.
20. Freeman W., Jones T., Pasztor E. Example-based super-resolution. IEEE Computer Graphics and Applications 2002, 22:56-65.
21. Harrigan M., Leonardo J., Gibbons K., Guterman L., Hopkins L. CT perfusion cerebral blood flow imaging in neurological critical care. Neurocritical Care 2005, 2:352-366.
22. 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 2010, 29:1182-1191.
23. 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 applications1. Radiology 2004, 231:632-644.
24. Huang J., Zhang S., Metaxas D. Efficient MR image reconstruction for compressed MR imaging. Medical Image Analysis 2011, 15:670-679.
25. Kershaw L., Cheng H. Temporal resolution and snr requirements for accurate DCE-MRI data analysis using the aath model. Magnetic Resonance in Medicine 2010, 64:1772-1780.
26. Kim S., Koh K., Lustig M., Boyd S., Gorinevsky D. An interior-point method for large-scale L1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 2007, 1:606-617.
27. König M. Brain perfusion CT in acute stroke: current status. European Journal of Radiology 2003, 45:S11-S22.
28. Lawrence K., Lee T. An adiabatic approximation to the tissue homogeneity model for water exchange in the brain: I. Theoretical derivation. Journal of Cerebral Blood Flow & Metabolism 1998, 18:1365-1377.
29. Lin J.W., Sciacca R.R., Chou R.L., Laine A.F., Bergmann S.R. Quantification of myocardial perfusion in human subjects using 82rb and wavelet-based noise reduction. Journal of Nuclear Medicine 2001, 42:201-208.
30. Lustig M., Donoho D., Pauly J. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine 2007, 58:1182-1195.
31. Ma J., Huang J., Feng Q., Zhang H., Lu H., Liang Z., Chen W. Low-dose computed tomography image restoration using previous normal-dose scan. Medical Physics 2011, 38:5713.
32. Mairal J., Bach F., Ponce J., Sapiro G. Online dictionary learning for sparse coding. Proceedings of the 26th Annual International Conference on Machine Learning 2009, 689-696. ACM.
33. Mairal J., Bach F., Ponce J., Sapiro G., Zisserman A. Non-local sparse models for image restoration. IEEE 12th International Conference on Computer Vision 2009, 2272-2279. IEEE.
34. Mairal J., Elad M., Sapiro G. Sparse representation for color image restoration. IEEE Transactions on Image Processing 2008, 17:53-69.
35. Mallat S., Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 1993, 41:3397-3415.
36. Matalon B., Elad M., Zibulevsky M. Improved denoising of images using modelling of a redundant contourlet transform. Optics & Photonics 2005 2005, 59141Y. International Society for Optics and Photonics.
37. Mendrik A., Vonken E., van Ginneken B., de Jong H., Riordan A., van Seeters T., Smit E., Viergever M., Prokop M. TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps. Physics in Medicine and Biology 2011, 56:3857.
38. Miles K.A., Griffiths M.R. Perfusion CT: a worthwhile enhancement?. British Journal of Radiology 2003, 76:220-231.
39. Mouridsen K., Friston K., Hjort N., Gyldensted L., Østergaard L., Kiebel S. Bayesian estimation of cerebral perfusion using a physiological model of microvasculature. NeuroImage 2006, 33:570-579.
40. Nathan P., Edward D., Thomas R., Dan K., Christopher M., Regan B., Paul C., John H. Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method. Journal of Cardiovascular Magnetic Resonance 2008, 10.
41. Østergaard L., Sorensen A.G., Kwong K.K., Weisskoff R.M., Gyldensted C., Rosen B.R. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: Experimental comparison and preliminary results. Magnetic Resonance in Medicine 1996, 36:726-736.
42. Østergaard L., Weisskoff R.M., Chesler D.A., Gyldensted C., Rosen B.R. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis. Magnetic Resonance in Medicine 1996, 36:715-725.
43. Portilla J., Strela V., Wainwright M., Simoncelli E. Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Transactions on Image Processing 2003, 12:1338-1351.
44. Protter M., Elad M. Image sequence denoising via sparse and redundant representations. IEEE Transactions on Image Processing 2009, 18:27-35.
45. Roth S., Black M. Fields of experts: a framework for learning image priors. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005) 2005, 860-867. IEEE.
46. Saito N., Kudo K., Sasaki T., Uesugi M., Koshino K., Miyamoto M., Suzuki S. Realization of reliable cerebral-blood-flow maps from low-dose CT perfusion images by statistical noise reduction using non-linear diffusion filtering. Radiological Physics and Technology 2008, 1:62-74.
47. Starck J., Candès E., Donoho D. The curvelet transform for image denoising. IEEE Transactions on Image Processing 2002, 11:670-684.
48. Wintermark M., Lev M. Fda investigates the safety of brain perfusion ct. American Journal of Neuroradiology 2010, 31:2-3.
49. 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 2008, 32:67-77.
50. Wong K., Tam C., Ng M., Wong S., Young G. Improved residue function and reduced flow dependence in MR perfusion using least-absolute-deviation regularization. Magnetic Resonance in Medicine 2009, 61:418-428.
51. Wright J., Yang A., Ganesh A., Sastry S., Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31:210-227.
52. Wu O., stergaard L., Weisskoff R.M., Benner T., Rosen B.R., Sorensen A.G. Tracer arrival timing-insensitive technique for estimating flow in mr perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magnetic Resonance in Medicine 2003, 50:164-174.
53. Yang J., Wright J., Huang T., Ma Y. Image super-resolution as sparse representation of raw image patches. IEEE Conference on Computer Vision and Pattern Recognition, 2008 (CVPR 2008) 2008, 1-8. IEEE.
54. Zhang, S., Huang, J., Li, H., Metaxas, D., 2012a. Automatic image annotation and retrieval using group sparsity. Systems, Man, and Cybernetics, Part B: Cybernetics. IEEE Transactions on 42, 838-849.
55. Zhang S., Zhan Y., Dewan M., Huang J., Metaxas D., Zhou X. Towards robust and effective shape modeling: sparse shape composition. Medical Image Analysis 2012, 16:265-277.
56. Zhang S., Zhan Y., Metaxas D. Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis 2012.
57. Zhu S., Mumford D. Prior learning and gibbs reaction-diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19:1236-1250.