Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

Medical Image Analysis

Volumn 18, Issue 6, 2014, Pages 866-880

  Fang, Ruogu ^a   Karlsson, Kolbeinn ^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

Blood brain barrier permeability; Patlak model; Radiation dose reduction; Sparse high dose induced prior

Indexed keywords

BLOOD; COMPUTERIZED TOMOGRAPHY;

BLOOD-BRAIN BARRIER; CLINICAL ANALYSIS; CLINICAL PRACTICES; HIGH DOSE; RADIATION DOSE REDUCTIONS; RADIATION EXPOSURE; RADIATION SAFETY; STRUCTURAL SIMILARITY;

COMPUTER SIMULATION;

ALGORITHM; ARTICLE; ARTIFACT REDUCTION; BLOOD BRAIN BARRIER; BRAIN BLOOD FLOW; BRAIN SCINTISCANNING; CEREBROVASCULAR ACCIDENT; CLINICAL ARTICLE; CLINICAL PRACTICE; COMPARTMENT MODEL; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; EXTRAVASCULAR SPACE; GOLD STANDARD; HUMAN; IMAGE PROCESSING; IMAGE QUALITY; NOISE REDUCTION; PATLAK MODEL; PERFUSION COMPUTED TOMOGRAPHY; PERMEABILITY BARRIER; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RADIATION DOSE; RADIATION DOSE REDUCTION; RADIATION EXPOSURE; RADIATION SAFETY; SIGNAL NOISE RATIO; THEORETICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; PROCEDURES; RADIOGRAPHY;

ALGORITHMS; BLOOD-BRAIN BARRIER; HUMANS; MODELS, THEORETICAL; RADIATION DOSAGE; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO; STROKE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84901690103     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2013.09.008     Document Type: Article

References (64)

1. 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.
2. Badea C.T., Johnston S.M., Qi Y., Johnson G.A. 4D micro-CT for cardiac and perfusion applications with view under sampling. Physics in Medicine and Biology 2011, 56:3351.
3. Bisdas S., Hartel M., Cheong L., Koh T., Vogl T. Prediction of subsequent hemorrhage in acute ischemic stroke using permeability CT imaging and a distributed parameter tracer kinetic model. Journal of Neuroradiology 2007, 34:101-108.
4. 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.
5. 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.
6. 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.
7. 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.
8. Chen S., Donoho D., Saunders M. Atomic decomposition by basis pursuit. SIAM Review 2001, 43:129-159.
9. Cianfoni A., Cha S., Bradley W., Dillon W., Wintermark M. Quantitative measurement of blood-brain barrier permeability using perfusion-CT in extra-axial brain tumors. Journal of Neuroradiology 2006, 33:164-168.
10. Clark W.M., Albers G.W., Madden K.P., Hamilton S., et al. The rtPA (Alteplase) 0-to 6-hour acute stroke trial, part A (A0276g) Results of a double-blind, placebo-controlled, multicenter study. Stroke 2000, 31:811-816.
11. Dankbaar J., Hom J., Schneider T., Cheng S.C., Lau B., van der Schaaf I., Virmani S., Pohlman S., Dillon W., Wintermark M. Dynamic perfusion CT assessment of the blood-brain barrier permeability: first pass versus delayed acquisition. American Journal of Neuroradiology 2008, 29:1671-1676.
12. 1-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics 2006, 59:797-829.
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. Fang R., Chen T., Sanelli P.C. Sparsity-based deconvolution of low-dose brain perfusion CT in subarachnoid hemorrhage patients. 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2012 2012, 872-875. IEEE.
15. 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, 17:417-428.
16. 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.
17. Fleischmann D., Rubin G.D., Bankier A.A., Hittmair K. Improved uniformity of aortic enhancement with customized contrast medium injection protocols at CT angiography1. Radiology 2000, 214:363-371.
18. Florie J., van Gelder R.E., Schutter M.P., van Randen A., Venema H.W., de Jager S., van der Hulst V.P., Prent A., Bipat S., Bossuyt P.M., et al. Feasibility study of computed tomography colonography using limited bowel preparation at normal and low-dose levels study. European Radiology 2007, 17:3112-3122.
19. Frush D.P., Donnelly L.F., Rosen N.S. Computed tomography and radiation risks: what pediatric health care providers should know. Pediatrics 2003, 112:951-957.
20. Frush D.P., Slack C.C., Hollingsworth C.L., Bisset G.S., Donnelly L.F., Hsieh J., Lavin-Wensell T., Mayo J.R. Computer-simulated radiation dose reduction for abdominal multidetector CT of pediatric patients. American Journal of Roentgenology 2002, 179:1107-1113.
21. Hanai K., Horiuchi T., Sekiguchi J., Muramatsu Y., Kakinuma R., Moriyama N., Tuchiiya R., Niki N. Computer-simulation technique for low dose computed tomographic screening. Journal of Computer Assisted Tomography 2006, 30:955-961.
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. Huang J., Zhang S., Metaxas D. Efficient MR image reconstruction for compressed MR imaging. Medical Image Analysis 2011, 15:670-679.
24. Imanishi Y., Fukui A., Niimi H., Itoh D., Nozaki K., Nakaji S., Ishizuka K., Tabata H., Furuya Y., Uzura M., et al. Radiation-induced temporary hair loss as a radiation damage only occurring in patients who had the combination of MDCT and DSA. European Radiology 2005, 15:41-46.
25. Jaillard A., Cornu C., Durieux A., Moulin T., Boutitie F., Lees K.R., Hommel M. Hemorrhagic transformation in acute ischemic stroke: the MAST-E study. Stroke 1999, 30:1326-1332. on behalf of the MAST-E Group.
26. Jia X., Lou Y., Dong B., Tian Z., Jiang S. 4D computed tomography reconstruction from few-projection data via temporal non-local regularization. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2010 2010, 143-150. Springer.
27. 1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 2007, 1:606-617.
28. Lawrence I., Lin K. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989, 255-268.
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. Lin K., Kazmi K., Law M., Babb J., Peccerelli N., Pramanik B. Measuring elevated microvascular permeability and predicting hemorrhagic transformation in acute ischemic stroke using first-pass dynamic perfusion CT imaging. American Journal of Neuroradiology 2007, 28:1292-1298.
31. Lloyd-Jones D., Adams R., Carnethon M., De Simone G., Ferguson T.B., Flegal K., et al. Heart disease and stroke statistics-2009 update: a report from the american heart association statistics committee and stroke statistics subcommittee. Circulation 2009, 119:480-486.
32. Lopez-Yunez A.M., Bruno A., Williams L.S., Yilmaz E., Zurrú C., Biller J. Protocol violations in community-based rtPA stroke treatment are associated with symptomatic intracerebral hemorrhage. Stroke 2001, 32:12-16.
33. 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.
34. 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.
35. Ma J., Zhang H., Gao Y., Huang J., Liang Z., Feng Q., Chen W. Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior. Physics in Medicine and Biology 2012, 57:7519.
36. Mairal J., Elad M., Sapiro G. Sparse representation for color image restoration. IEEE Transactions on Image Processing 2008, 17:53-69.
37. Mallat S., Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing 1993, 41:3397-3415.
38. Massoumzadeh P., Don S., Hildebolt C.F., Bae K.T., Whiting B.R. Validation of CT dose-reduction simulation. Medical Physics 2009, 36:174.
39. Mendrik A.M., Vonken E.j., van Ginneken B., de Jong H.W., Riordan A., van Seeters T., Smit E.J., Viergever M.A., 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.
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. Patlak C.S., Blasberg R.G. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations: Journal of Cerebral Blood Flow & Metabolism 1985, 5:584-590.
42. Patlak C.S., Blasberg R.G., Fenstermacher J.D., et al. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Journal of Cerebral Blood Flow & Metabolism 1983, 3:1-7.
43. Protter M., Elad M. Image sequence denoising via sparse and redundant representations. IEEE Transactions on Image Processing 2009, 18:27-35.
44. 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 nonlinear diffusion filtering. Radiological Physics and Technology 2008, 1:62-74.
45. Schneider T., Hom J., Bredno J., Dankbaar J., Cheng S.C., Wintermark M. Delay correction for the assessment of blood-brain barrier permeability using first-pass dynamic perfusion CT. American Journal of Neuroradiology 2011, 32:E134-E138.
46. Shtok J., Elad M., Zibulevsky M. Sparsity-based sinogram denoising for low-dose computed tomography. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011 2011, 569-572. IEEE.
47. Su E.J., Fredriksson L., Geyer M., Folestad E., Cale J., Andrae J., Gao Y., Pietras K., Mann K., Yepes M., et al. Activation of PDGF-CC by tissue plasminogen activator impairs blood-brain barrier integrity during ischemic stroke. Nature Medicine 2008, 14:731-737.
48. Sullivan B.J., Chang H.C. A generalized landweber iteration for ill-conditioned signal restoration. 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991 (ICASSP-91) 1991, 1729-1732. IEEE.
49. Tack D., De Maertelaer V., Petit W., Scillia P., Muller P., Suess C., Gevenois P.A. Multi-detector row CT pulmonary angiography: comparison of standard-dose and simulated low-dose techniques. Radiology 2005, 236:318-325.
50. Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 1996, 267-288.
51. Veldkamp W.J., Kroft L.J., van Delft J.P.A., Geleijns J. A technique for simulating the effect of dose reduction on image quality in digital chest radiography. Journal of Digital Imaging 2009, 22:114-125.
52. Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 2004, 13:600-612.
53. Wiesmann M., Berg S., Bohner G., Klingebiel R., Schöpf V., Stoeckelhuber B., Yousry I., Linn J., Missler U. Dose reduction in dynamic perfusion CT of the brain: effects of the scan frequency on measurements of cerebral blood flow, cerebral blood volume, and mean transit time. European Radiology 2008, 18:2967-2974.
54. Wintermark M., Lev M. FDA investigates the safety of brain perfusion ct. American Journal of Neuroradiology 2010, 31:2-3.
55. Wintermark M., Maeder P., Verdun F.R., Thiran J.P., Valley J.F., Schnyder P., Meuli R. Using 80kVp versus 120kVp in perfusion CT measurement of regional cerebral blood flow. American Journal of Neuroradiology 2000, 21:1881-1884.
56. 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.
57. 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.
58. 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.
59. Yang J., Wright J., Huang T.S., Ma Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing 2010, 19:2861-2873.
60. Yu L., Liu X., Leng S., Kofler J.M., Ramirez-Giraldo J.C., Qu M., Christner J., Fletcher J.G., McCollough C.H. Radiation dose reduction in computed tomography: techniques and future perspective. Imaging 2009, 1:65-84.
61. Zauner, G., Reiter, M., Salaberger, D., Kastnerm, J., 2006. Denoising of computed tomography images using multiresolution based methods. In: Proceedings of ECNDT, Berlin.
62. Zhang S., Huang J., Li H., Metaxas D.N. Automatic image annotation and retrieval using group sparsity. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2012, 42:838-849.
63. Zhang S., Zhan Y., Dewan M., Huang J., Metaxas D.N., Zhou X. Towards robust and effective shape modeling: sparse shape composition. Medical Image Analysis 2012, 16:265-277.
64. Zhang S., Zhan Y., Metaxas D.N. Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis 2012, 16:1385-1396.