Publications 2021
[1] M. K. Hassan et al., “An automatic framework to create patient-specific eye models from 3D MR-images for treatment selection in patients with uveal melanoma,” Advances in Radiation Oncology, p. 100697, Apr. 2021, doi: 10.1016/j.adro.2021.100697.
[2] M. T. Mills et al., “Feasibility and validation of trans-valvular flow derived by four-dimensional flow cardiovascular magnetic resonance imaging in patients with atrial fibrillation,” Wellcome Open Res, vol. 6, p. 73, Mar. 2021, doi: 10.12688/wellcomeopenres.16655.1.
[3] A. Keo et al., “Cingulate networks associated with gray matter loss in Parkinson’s disease show high expression of cholinergic genes in the healthy brain,” European Journal of Neuroscience, p. ejn.15216, Apr. 2021, doi: 10.1111/ejn.15216.
[4] Z. Meiszterics, T. Simor, R. J. van der Geest, N. Farkas, and B. Gaszner, “Evaluation of pulse wave velocity for predicting major adverse cardiovascular events in post-infarcted patients; comparison of oscillometric and MRI methods,” Rev Cardiovasc Med, vol. 22, no. 4, pp. 1701–1710, Dec. 2021, doi: 10.31083/j.rcm2204178.
[5] F. O. Mutluer et al., “Evaluation of intraventricular flow by multimodality imaging: A review and meta-analysis,” Cardiovasc Ultrasound, vol. 19, no. 1, p. 38, Dec. 2021, doi: 10.1186/s12947-021-00269-8.
[6] H. Ben‐Arzi, A. Das, C. Kelly, R. J. Geest, S. Plein, and E. Dall’Armellina, “Longitudinal Changes in Left Ventricular Blood Flow Kinetic Energy After Myocardial Infarction: Predictive Relevance for Cardiac Remodeling,” J Magn Reson Imaging, p. jmri.28015, Dec. 2021, doi: 10.1002/jmri.28015.
[7] T. Arts et al., “Non‐Invasive Assessment of Damping of Blood Flow Velocity Pulsatility in Cerebral Arteries With MRI,” J Magn Reson Imaging, p. jmri.27989, Nov. 2021, doi: 10.1002/jmri.27989.
[8] K. Koolstra, P. Börnert, B. P. F. Lelieveldt, A. Webb, and O. Dzyubachyk, “Stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries,” Magn Reson Mater Phy, Oct. 2021, doi: 10.1007/s10334-021-00963-8.
[9] Z. Meiszterics, T. Simor, R. J. Van Der Geest, N. Farkas, and B. Gaszner, “Evaluation of pulse wave velocity for predicting major advanced cardiovascular events in patients with chronic myocardial infarction,” European Heart Journal, vol. 42, no. Supplement_1, p. ehab724.2533, Oct. 2021, doi: 10.1093/eurheartj/ehab724.2533.
[10] A. Keo, O. Dzyubachyk, J. van der Grond, J. J. van Hilten, M. J. T. Reinders, and A. Mahfouz, “Transcriptomic Signatures Associated With Regional Cortical Thickness Changes in Parkinson’s Disease,” Frontiers in Neuroscience, vol. 15, p. 1286, 2021, doi: 10.3389/fnins.2021.733501.
[11] A. Demirkiran et al., “Left ventricular blood flow energetics after acute ST-segment elevation myocardial infarction associate with left ventricular remodeling,” European Heart Journal, vol. 42, no. Supplement_1, p. ehab724.0249, Oct. 2021, doi: 10.1093/eurheartj/ehab724.0249.
[12] A. Demirkiran et al., “Left ventricular four-dimensional blood flow energetics and vorticity in chronic myocardial infarction patients with/without left ventricular thrombus,” European Heart Journal - Cardiovascular Imaging, vol. 22, no. Supplement_2, Jun. 2021, doi: 10.1093/ehjci/jeab090.091.
[13] C. Grafton-Clarke et al., “Reproducibility of left ventricular blood flow kinetic energy measured by four-dimensional flow CMR,” BMC Res Notes, vol. 14, no. 1, p. 289, Jul. 2021, doi: 10.1186/s13104-021-05697-3.
[14] P. M. Johnson et al., “Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge,” in Machine Learning for Medical Image Reconstruction, vol. 12964, N. Haq, P. Johnson, A. Maier, T. Würfl, and J. Yoo, Eds., Cham: Springer International Publishing, 2021, pp. 25–34. doi: 10.1007/978-3-030-88552-6_3.
[15] T. E. Bakken et al., “Comparative cellular analysis of motor cortex in human, marmoset and mouse,” Nature, vol. 598, no. 7879, pp. 111–119, Oct. 2021, doi: 10.1038/s41586-021-03465-8.
[16] C. Grafton-Clarke et al., “Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions,” Wellcome Open Research, 6:253, Oct. 2021. doi: 10.12688/wellcomeopenres.17200.1.
[17] O. Dzyubachyk, R. I. Koning, A. A. Mulder, M. C. Avramut, F. G. Faas, and A. J. Koster, “Intensity Correction and Standardization for Electron Microscopy Data,” in Medical Imaging with Deep Learning, PMLR, Aug. 2021, pp. 148–157. Accessed: Sep. 13, 2021. [Online]. Available: https://proceedings.mlr.press/v143/dzyubachyk21a.html
[18] S. Alabed et al., “Fully automated CMR derived stroke volume correlates with right heart catheter measurements in patients with suspected pulmonary hypertension,” European Heart Journal - Cardiovascular Imaging, vol. 22, no. Supplement_2, p. jeab090.036, Jul. 2021, doi: 10.1093/ehjci/jeab090.036.
[19] S. Alabed et al., “High interstudy repeatability of automatic deep learnt biventricular CMR measurements,” European Heart Journal - Cardiovascular Imaging, vol. 22, no. Supplement_2, p. jeab090.035, Jul. 2021, doi: 10.1093/ehjci/jeab090.035.
[20] M. E. Ijsselsteijn, A. Somarakis, B. P. F. Lelieveldt, T. Höllt, and N. F. C. C. de Miranda, “Semi‐automated background removal limits data loss and normalises imaging mass cytometry data,” Cytometry, p. cyto.a.24480, Jun. 2021, doi: 10.1002/cyto.a.24480.
[21] S. E. E. C. Bauduin et al., “Potential associations between immune signaling genes, deactivated microglia, and oligodendrocytes and cortical grey matter loss in patients with long-term remitted Cushing’s Disease,” Psychoneuroendocrinology, p. 105334, Jun. 2021, doi: 10.1016/j.psyneuen.2021.105334.
[22] A. Somarakis et al., “Visual Analysis of Tissue Images at Cellular Level,” EuroVis 2021 - Dirk Bartz Prize, pp. 5 pages, 2021, doi: 10.2312/EVM.20211074.
[23] B. D. Aevermann et al., “A machine learning method for the discovery of minimum marker gene combinations for cell-type identification from single-cell RNA sequencing,” Genome Res., p. gr.275569.121, Jun. 2021, doi: 10.1101/gr.275569.121.
[24] C. Jungen et al., “Sympathetic innervation pattern in NICM patients with ventricular tachycardia -anteroseptal versus inferolateral substrates-,” EP Europace, vol. 23, no. Supplement_3, p. euab116.340, May 2021, doi: 10.1093/europace/euab116.340.
[25] H. Chen et al., “Global cardiac sympathetic denervation is associated with diffuse myocardial fibrosis in non-ischemic cardiomyopathy,” EP Europace, vol. 23, no. Supplement_3, p. euab116.112, May 2021, doi: 10.1093/europace/euab116.112.
[26] R. Amier et al., “Cardiac Biomarkers and Left Ventricular Function in relation to Vascular Brain Injury and Cognitive Functioning,” Journal of the American College of Cardiology, vol. 77, no. 18, p. 3020, May 2021, doi: 10.1016/S0735-1097(21)04375-8.
[27] M. E. Mahdiui et al., “Myocardial Work, an Echocardiographic Measure of Post Myocardial Infarct Scar on Contrast-Enhanced Cardiac Magnetic Resonance,” The American Journal of Cardiology, p. S0002914921003623, May 2021, doi: 10.1016/j.amjcard.2021.04.009.
[28] M. S. Elmahdy et al., “Joint Registration and Segmentation via Multi-Task Learning for Adaptive Radiotherapy of Prostate Cancer,” arXiv:2105.01844 [cs, eess], May 2021, Accessed: May 11, 2021. [Online]. Available: http://arxiv.org/abs/2105.01844
[29] S. E. de Jong et al., “Systems analysis and controlled malaria infection in Europeans and Africans elucidate naturally acquired immunity,” Nature Immunology, pp. 1–12, Apr. 2021, doi: 10.1038/s41590-021-00911-7.
[30] H. Sokooti, S. Yousefi, M. S. Elmahdy, B. P. F. Lelieveldt, and M. Staring, “Hierarchical Prediction of Registration Misalignment using a Convolutional LSTM: Application to Chest CT Scans,” IEEE Access, pp. 1–1, Apr. 2021, doi: 10.1109/ACCESS.2021.3074124.
[31] S. Yousefi et al., “Esophageal Tumor Segmentation in CT Images using Dilated Dense Attention Unet (DDAUnet),” arXiv:2012.03242 [cs, eess], Mar. 2021, Accessed: Apr. 28, 2021. [Online]. Available: http://arxiv.org/abs/2012.03242
[32] Z. Zhou, Y. Wang, B. P. F. Lelieveldt, and Q. Tao, “Deep Recursive Embedding for High-Dimensional Data,” arXiv:2104.05171 [cs], Apr. 2021, Accessed: Apr. 20, 2021. [Online]. Available: http://arxiv.org/abs/2104.05171
[33] B. Kenkhuis et al., “Iron loading is a prominent feature of activated microglia in Alzheimer’s disease patients,” acta neuropathol commun, vol. 9, no. 1, p. 27, Dec. 2021, doi: 10.1186/s40478-021-01126-5.
[34] O. V. Ivashchenko, Z. Zhai, B. C. Stoel, and T. J. M. Ruers, “Optimization of hepatic vasculature segmentation from contrast-enhanced MRI, exploring two 3D Unet modifications and various loss functions,” in Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, International Society for Optics; Photonics, Feb. 2021, p. 115980J. doi: 10.1117/12.2574267.
[35] E. Fleury et al., “Three‐dimensional MRI‐based treatment planning approach for non‐invasive ocular proton therapy,” Med. Phys., p. mp.14665, Jan. 2021, doi: 10.1002/mp.14665.