Abstract
We are focusing on the difficult task of predicting final lesion in stroke, a complex disease that leads to divergent imaging patterns related to the occluded artery level and the geometry of the patient’s vascular tree. We propose a framework in which convolutional neural networks are trained only from synthetic perfusion MRI - obtained from an existing physical simulator - and tested on real patients. We incorporate new levels of realism into this simulator, allowing to simulate the vascular tree of a given patient. We demonstrate that our approach is able to predict the final infarct of the tested patients only from simulated data. Among the various simulated databases generated, we show that simulations taking into account the vascular tree information give the best classification performances on the tested patients.
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References
Calamante, F., Gadian, D.G., Connelly, A.: Quantification of bolus-tracking MRI: improved characterization of the tissue residue function using Tikhonov regularization. Magn. Reson. Med. 50(6), 1237–1247 (2003)
Calamante, F., Yim, P.J., Cebral, J.R.: Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics. Neuroimage 19(2), 341–353 (2003)
Christensen, S., et al.: Inferring origin of vascular supply from tracer arrival timing patterns using bolus tracking MRI. JMRI 27(6), 1371–1381 (2008)
Davis, S., Fisher, M., Warach, S.: Magnetic Resonance Imaging in Stroke. Cambridge University Press, Cambridge (2003)
Frindel, C., Robini, M.C., Rousseau, D.: A 3-D spatio-temporal deconvolution approach for MR perfusion in the brain. Med. Image Anal. 18(1), 144–160 (2014)
Georgiou, L., Wilson, D.J., Sharma, N., Perren, T.J., Buckley, D.L.: A functional form for a representative individual arterial input function measured from a population using high temporal resolution DCE MRI. Magn. Reson. Med. 81(3), 1955–1963 (2019)
Giacalone, M., Frindel, C., Robini, M., Cervenansky, F., Grenier, E., Rousseau, D.: Robustness of spatio-temporal regularization in perfusion MRI deconvolution: an application to acute ischemic stroke. Magn. Reson. Med. 78(5), 1981–1990 (2017)
Giacalone, M., et al.: Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke. Med. Image Anal. 50, 117–126 (2018)
Hermitte, L., et al.: Very low cerebral blood volume predicts parenchymal hematoma in acute ischemic stroke. Stroke 44(8), 2318–2320 (2013)
Iqbal, S.: A comprehensive study of the anatomical variations of the circle of willis in adult human brains. J. Clin. Diagn. Res.: JCDR 7(11), 2423 (2013)
Kellner, E., et al.: Arterial input function measurements for bolus tracking perfusion imaging in the brain. Magn. Reson. Med. 69(3), 771–780 (2013)
Kudo, K., et al.: Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. Radiology 267(1), 201–211 (2013)
Livne, M., et al.: A PET-guided framework supports a multiple arterial input functions approach in DSC-MRI in acute stroke. JON 27(5), 486–492 (2017)
Madsen, M.T.: A simplified formulation of the gamma variate function. Phys. Med. Biol. 37(7), 1597 (1992)
Mahmood, F., Chen, R., Durr, N.J.: Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans. Med. Imaging 37(12), 2572–2581 (2018)
Mukherjee, D., Jani, N.D., Narvid, J., Shadden, S.C.: The role of circle of willis anatomy variations in cardio-embolic stroke: a patient-specific simulation based study. Ann. Biomed. Eng. 46(8), 1128–1145 (2018)
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. Magn. Reson. Med. 36(5), 715–725 (1996)
Pinto, A., et al.: Enhancing clinical MRI perfusion maps with data-driven maps of complementary nature for lesion outcome prediction. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 107–115. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_13
Rose, S.E., Janke, A.L., Griffin, M., Finnigan, S., Chalk, J.B.: Improved prediction of final infarct volume using bolus delay-corrected perfusion-weighted MRI: implications for the ischemic penumbra. Stroke 35(11), 2466–2471 (2004)
Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00536-8_1
Winzeck, S., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9, 679 (2018)
Zhong, L., Zhang, J.M., Su, B., Tan, R.S., Allen, J.C., Kassab, G.S.: Application of patient-specific computational fluid dynamics in coronary and intra-cardiac flow simulations: challenges and opportunities. Front. Physiol. 9, 742 (2018)
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Debs, N., Decroocq, M., Cho, TH., Rousseau, D., Frindel, C. (2019). Evaluation of the Realism of an MRI Simulator for Stroke Lesion Prediction Using Convolutional Neural Network. In: Burgos, N., Gooya, A., Svoboda, D. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2019. Lecture Notes in Computer Science(), vol 11827. Springer, Cham. https://doi.org/10.1007/978-3-030-32778-1_16
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DOI: https://doi.org/10.1007/978-3-030-32778-1_16
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