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Results: 42
Number of items: 42
  • van Velzen, S. G. M., Gal, R., Teske, A. J., van der Leij, F., van den Bongard, D. H. J. G., Viergever, M. A., Verkooijen, H. M., & Išgum, I. (2022). AI-Based Radiation Dose Quantification for Estimation of Heart Disease Risk in Breast Cancer Survivors After Radiation Therapy. International Journal of Radiation Oncology Biology Physics, 112(3), 621-632. https://doi.org/10.1016/j.ijrobp.2021.09.008
  • van Velzen, S. G. M., Bruns, S., Wolterink, J. M., Leiner, T., Viergever, M. A., Verkooijen, H. M., & Išgum, I. (2022). AI-Based Quantification of Planned Radiation Therapy Dose to Cardiac Structures and Coronary Arteries in Patients With Breast Cancer. International Journal of Radiation Oncology Biology Physics, 112(3), 611-620. https://doi.org/10.1016/j.ijrobp.2021.09.009
  • Open Access
    Bruns, S., Wolterink, J. M., van den Boogert, T. P. W., Runge, J. H., Bouma, B. J., Henriques, J. P., Baan, J., Viergever, M. A., Planken, R. N., & Išgum, I. (2022). Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT. Computers in Biology and Medicine, 142, Article 105191. https://doi.org/10.1016/j.compbiomed.2021.105191
  • Open Access
    Zoetmulder, R., Išgum, I., Gavves, E., & MR CLEAN Registry Investigators (2022). Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke. Diagnostics, 12(6), Article 1400. https://doi.org/10.3390/diagnostics12061400
  • Open Access
    Bruns, S. (2022). Automated segmentation of the heart in high-dimensional computed tomography. [Thesis, fully internal, Universiteit van Amsterdam].
  • de Vos, B. D., Lessmann, N., de Jong, P. A., & Išgum, I. (2021). Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT. Radiology. Cardiothoracic imaging, 3(2), Article e190219. https://doi.org/10.1148/ryct.2021190219
  • Wolterink, J. M., Mukhopadhyay, A., Leiner, T., Vogl, T. J., Bucher, A. M., & Išgum, I. (2021). Generative Adversarial Networks: A Primer for Radiologists. RadioGraphics, 41(3), 840-857. https://doi.org/10.1148/rg.2021200151
  • Gal, R., van Velzen, S. G. M., Hooning, M. J., Emaus, M. J., van der Leij, F., Gregorowitsch, M. L., Blezer, E. L. A., Gernaat, S. A. M., Lessmann, N., Sattler, M. G. A., Leiner, T., de Jong, P. A., Teske, A. J., Verloop, J., Penninkhof, J. J., Vaartjes, I., Meijer, H., van Tol-Geerdink, J. J., Pignol, J.-P., ... Verkooijen, H. M. (2021). Identification of Risk of Cardiovascular Disease by Automatic Quantification of Coronary Artery Calcifications on Radiotherapy Planning CT Scans in Patients With Breast Cancer. JAMA Oncology, 7(7), 1024-1032. https://doi.org/10.1001/jamaoncol.2021.1144
  • Open Access
    Slart, R. H. J. A., Williams, M. C., Juarez-Orozco, L. E., Rischpler, C., Dweck, M. R., Glaudemans, A. W. J. M., Gimelli, A., Georgoulias, P., Gheysens, O., Gaemperli, O., Habib, G., Hustinx, R., Cosyns, B., Verberne, H. J., Hyafil, F., Erba, P. A., Lubberink, M., Slomka, P., Išgum, I., ... Saraste, A. (2021). Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. European Journal of Nuclear Medicine and Molecular Imaging, 48(5), 1399-1413. https://doi.org/10.1007/s00259-021-05341-z
  • Lin, A., Kolossváry, M., Motwani, M., Išgum, I., Maurovich-Horvat, P., Slomka, P. J., & Dey, D. (2021). Artificial intelligence in cardiovascular CT: Current status and future implications. Journal of cardiovascular computed tomography, 15(6), 462-469. https://doi.org/10.1016/j.jcct.2021.03.006
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