Higashida, R.T., Furlan, A.J.: Trial design and reporting standards for intra-arterial cerebral thrombolysis for acute ischemic stroke. stroke 34(8), 109–137 (2003)

Organization, W.H., et al.: World health statistics 2020 (2020)

Mathers, C.D., Boerma, T., Ma Fat, D.: Global and regional causes of death. British medical bulletin 92(1), 7–32 (2009)

Bivard, A., Spratt, N., Levi, C., Parsons, M.: Perfusion computer tomography: imaging and clinical validation in acute ischaemic stroke. Brain 134(11), 3408–3416 (2011)

Kamalian, S., Kamalian, S., Maas, M.B., Goldmacher, G.V., Payabvash, S., Akbar, A., Schaefer, P.W., Furie, K.L., Gonzalez, R.G., Lev, M.H.: Ct cerebral blood flow maps optimally correlate with admission diffusion-weighted imaging in acute stroke but thresholds vary by postprocessing platform. Stroke 42(7), 1923–1928 (2011)

Konstas, A., Goldmakher, G., Lee, T.-Y., Lev, M.: Theoretic basis and technical implementations of ct perfusion in acute ischemic stroke, part 1: theoretic basis. American Journal of Neuroradiology 30(4), 662–668 (2009)

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, 1–20 (2011)

Varghese, F., Bukhari, A.B., Malhotra, R., De, A.: Ihc profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PloS one 9(5), 96801 (2014)

Council, N.R., et al.: Health risks from exposure to low levels of ionizing radiation: Beir vii phase 2 (2006)

Soltanpour, M., Yousefnezhad, M., Greiner, R., Boulanger, P., Buck, B.: Using temporal gan to translate the current ctp scan to follow-up mri, for predicting final acute ischemic stroke lesions

Soltanpour, M., Greiner, R., Boulanger, P., Buck, B.: Improvement of automatic ischemic stroke lesion segmentation in ct perfusion maps using a learned deep neural network. Computers in Biology and Medicine 137, 104849 (2021)

Ibtehaz, N., Rahman, M.S.: Multiresunet: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural networks 121, 74–87 (2020)

Adebayo, O.D., Culpan, G.: Diagnostic accuracy of computed tomography perfusion in the prediction of haemorrhagic transformation and patient outcome in acute ischaemic stroke: a systematic review and meta-analysis. European stroke journal 5(1), 4–16 (2020)

Thijs, V.N., Somford, D.M., Bammer, R., Robberecht, W., Moseley, M.E., Albers, G.W.: Influence of arterial input function on hypoperfusion volumes measured with perfusion-weighted imaging. Stroke 35(1), 94–98 (2004)

Warach, S.J., Luby, M., Albers, G.W., Bammer, R., Bivard, A., Campbell, B.C., Derdeyn, C., Heit, J.J., Khatri, P., Lansberg, M.G., et al: Acute stroke imaging research roadmap iii imaging selection and outcomes in acute stroke reperfusion clinical trials: consensus recommendations and further research priorities. Stroke 47(5), 1389–1398 (2016)

Soltanpour, M., Greiner, R., Boulanger, P., Buck, B.: Ischemic stroke lesion prediction in ct perfusion scans using multiple parallel u-nets following by a pixel-level classifier. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 957–963 (2019). IEEE

Clerigues, A., Valverde, S., Bernal, J., Freixenet, J., Oliver, A., Llad, X.: Acute ischemic stroke lesion core segmentation in ct perfusion images using fully convolutional neural networks. Computers in biology and medicine 115, 103487 (2019)

de Vries, L., Emmer, B.J., Majoie, C.B., Marquering, H.A., Gavves, E.: Perfu-net: Baseline infarct estimation from ct perfusion source data for acute ischemic stroke. Medical Image Analysis 85, 102749 (2023)

Zhang, J., Shi, F., Chen, L., Xue, Z., Zhang, L., Qian, D.: Ischemic stroke segmentation from ct perfusion scans using cluster-representation learning. In: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology: Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings 3, pp. 67–76 (2020). Springer

Amador, K., Wilms, M., Winder, A., Fiehler, J., Forkert, N.: Stroke lesion outcome prediction based on 4d ct perfusion data using temporal convolutional networks. In: Medical Imaging with Deep Learning, pp. 22–33 (2021). PMLR

Soltanpour, M., Faez, K., Sharifian, S., Pourahmadi, V.: Enhance evoked potentials detection using rbf neural networks: Application to brain-computer interface. In: 2016 2nd International Conference of Signal Processing and Intelligent Systems (ICSPIS), pp. 1–6 (2016). IEEE

Bertels, J., Robben, D., Vandermeulen, D., Suetens, P.: Contra-lateral information cnn for core lesion segmentation based on native ctp in acute stroke. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pp. 263–270 (2019). Springer

Robben, D., Boers, A.M., Marquering, H.A., Langezaal, L.L., Roos, Y.B., van Oostenbrugge, R.J., van Zwam, W.H., Dippel, D.W., Majoie, C.B., van der Lugt, A., et al: Prediction of final infarct volume from native ct perfusion and treatment parameters using deep learning. Medical image analysis 59, 101589 (2020)

Giacalone, M., Rasti, P., Debs, N., Frindel, C., Cho, T.-H., Grenier, E., Rousseau, D.: Local spatio-temporal encoding of raw perfusion mri for the prediction of final lesion in stroke. Medical image analysis 50, 117–126 (2018)

Wittsack, H.-J., Ritzl, A., Fink, G.R., Wenserski, F., Siebler, M., Seitz, R.J., Moödder, U., Freund, H.-J.: Mr imaging in acute stroke: diffusion-weighted and perfusion imaging parameters for predicting infarct size. Radiology 222(2), 397–403 (2002)

Wang, G., Song, T., Dong, Q., Cui, M., Huang, N., Zhang, S.: Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Medical Image Analysis 65, 101787 (2020)

Hakim, A., Christensen, S., Winzeck, S., Lansberg, M.G., Parsons, M.W., Lucas, C., Robben, D., Wiest, R., Reyes, M., Zaharchuk, G.: Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the isles challenge. Stroke 52(7), 2328–2337 (2021)

Pantano, P., Caramia, F., Bozzao, L., Dieler, C., von Kummer, R.: Delayed increase in infarct volume after cerebral ischemia: correlations with thrombolytic treatment and clinical outcome. Stroke 30(3), 502–507 (1999)

Pearce, M.S., Salotti, J.A., Little, M.P., McHugh, K., Lee, C., Kim, K.P., Howe, N.L., Ronckers, C.M., Rajaraman, P., Craft, A.W., et al: Radiation exposure from ct scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. The Lancet 380(9840), 499–505 (2012)

Francone, M., Gimelli, A., Budde, R.P., Caro-Dominguez, P., Einstein, A.J., Gutberlet, M., Maurovich-Horvat, P., Miller, O., Nagy, E., Natale, L., et al: Radiation safety for cardiovascular computed tomography imaging in paediatric cardiology: a joint expert consensus document of the eacvi, escr, aepc, and espr. European Heart Journal-Cardiovascular Imaging 23(8), 279–289 (2022)

Sodickson, A., Baeyens, P.F., Andriole, K.P., Prevedello, L.M., Nawfel, R.D., Hanson, R., Khorasani, R.: Recurrent ct, cumulative radiation exposure, and associated radiation-induced cancer risks from ct of adults. Radiology 251(1), 175–184 (2009)

Hall, E., Brenner, D.: Cancer risks from diagnostic radiology. The British journal of radiology 81(965), 362–378 (2008)

ISLES Challenge 2018. ISLES. Accessed: October 25, 2023. https://www.isles-challenge.org/ISLES2018/

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM 63(11), 139–144 (2020)

Grachev, A.M., Ignatov, D.I., Savchenko, A.V.: Compression of recurrent neural networks for efficient language modeling. Applied Soft Computing 79, 354–362 (2019)

L.que, L., Outtas, M., Liu, H., Zhang, L.: Comparative study of the methodologies used for subjective medical image quality assessment. Physics in Medicine & Biology 66(15), 15–02 (2021)