Abstract
Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we extend this line of work by introducing tissue-specific sparse deconvolution to preserve the subtle perfusion information in the low-contrast tissue classes by learning tissue-specific dictionaries for each tissue class, and restore the low-dose perfusion maps by joining the tissue segments reconstructed from the corresponding dictionaries. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method with the advantage of better differentiation between abnormal and normal tissue in these patients.
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References
Miles, K.A., Griffiths, M.R.: Perfusion CT: a worthwhile enhancement? British Journal of Radiology 76(904), 220–231 (2003)
Wintermark, M., Lev, M.: Fda investigates the safety of brain perfusion CT. American Journal of Neuroradiology 31(1), 2–3 (2010)
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)
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)
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)
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)
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)
Zhang, S., Zhan, Y., Metaxas, D.: Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis (2012)
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)
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)
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)
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)
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)
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Fang, R., Chen, T., Sanelli, P.C. (2013). Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_15
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DOI: https://doi.org/10.1007/978-3-642-40811-3_15
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