Publications 2020
[1] F. Muehlberg et al., “Comparability of compressed sensing-based gradient echo perfusion sequence SPARSE and conventional gradient echo sequence in assessment of myocardial ischemia,” European Journal of Radiology, vol. 131, p. 109213, Oct. 2020, doi: 10.1016/j.ejrad.2020.109213.
[2] J. Park et al., “Association of scar distribution with epicardial electrograms and surface ventricular tachycardia QRS duration in nonischemic cardiomyopathy,” J Cardiovasc Electrophysiol, vol. 31, no. 8, pp. 2032–2040, Aug. 2020, doi: 10.1111/jce.14618.
[3] R. Abou et al., “Left ventricular mechanical dispersion in ischaemic cardiomyopathy: Association with myocardial scar burden and prognostic implications,” European Heart Journal - Cardiovascular Imaging, p. jeaa187, Jul. 2020, doi: 10.1093/ehjci/jeaa187.
[4] S. Abdel-Kafi, M. de Ridder, M. de Riva, R. J. van der Geest, C. Rasch, and K. Zeppenfeld, “Integration of Electroanatomical Mapping With Imaging to Guide Radiotherapy of VT Substrates With High Accuracy,” JACC: Clinical Electrophysiology, vol. 6, no. 7, pp. 874–876, Jul. 2020, doi: 10.1016/j.jacep.2020.03.014.
[5] G. T. Archer et al., “Validation of four-dimensional flow cardiovascular magnetic resonance for aortic stenosis assessment,” Sci Rep, vol. 10, no. 1, p. 10569, Dec. 2020, doi: 10.1038/s41598-020-66659-6.
[6] N. Barker et al., “Age-associated changes in 4D flow CMR derived Tricuspid Valvular Flow and Right Ventricular Blood Flow Kinetic Energy,” Sci Rep, vol. 10, no. 1, p. 9908, Dec. 2020, doi: 10.1038/s41598-020-66958-y.
[7] E. H. M. Paiman et al., “Effect of Liraglutide on Cardiovascular Function and Myocardial Tissue Characteristics in Type 2 Diabetes Patients of South Asian Descent Living in the Netherlands: A Double‐Blind, Randomized, Placebo‐Controlled Trial,” J Magn Reson Imaging, vol. 51, no. 6, pp. 1679–1688, Jun. 2020, doi: 10.1002/jmri.27009.
[8] L. Toemen et al., “Fetal and infant growth patterns and left and right ventricular measures in childhood assessed by cardiac MRI,” Eur J Prev Cardiolog, vol. 27, no. 1, pp. 63–74, Jan. 2020, doi: 10.1177/2047487319866022.
[9] J. Venlet et al., “RV Tissue Heterogeneity on CT,” JACC: Clinical Electrophysiology, p. S2405500X20303583, May 2020, doi: 10.1016/j.jacep.2020.04.029.
[10] H. Everaars et al., “Comparison between quantitative cardiac magnetic resonance perfusion imaging and [15O]H2O positron emission tomography,” Eur J Nucl Med Mol Imaging, vol. 47, no. 7, pp. 1688–1697, Jul. 2020, doi: 10.1007/s00259-019-04641-9.
[11] H. Everaars et al., “Cardiac Magnetic Resonance for Evaluating Nonculprit Lesions After Myocardial Infarction,” JACC: Cardiovascular Imaging, vol. 13, no. 3, pp. 715–728, Mar. 2020, doi: 10.1016/j.jcmg.2019.07.019.
[12] H. Zhao et al., “A framework for pulmonary fissure segmentation in 3D CT images using a directional derivative of plate filter,” Signal Processing, vol. 173, p. 107602, Aug. 2020, doi: 10.1016/j.sigpro.2020.107602.
[13] B. D. de Vos, B. H. M. van der Velden, J. Sander, K. G. A. Gilhuijs, M. Staring, and I. Išgum, “Mutual information for unsupervised deep learning image registration,” in Medical Imaging 2020: Image Processing, International Society for Optics; Photonics, Mar. 2020, p. 113130R. doi: 10.1117/12.2549729.
[14] M. S. Elmahdy, T. Ahuja, U. A. van der Heide, and M. Staring, “Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT,” arXiv:2002.06927 [cs, eess], Feb. 2020, Accessed: Jun. 07, 2020. [Online]. Available: http://arxiv.org/abs/2002.06927
[15] N. Pezzotti et al., “GPGPU Linear Complexity t-SNE Optimization,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 1172–1181, Jan. 2020, doi: 10.1109/TVCG.2019.2934307.
[16] L. Beljaards, M. S. Elmahdy, F. Verbeek, and M. Staring, “A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy,” arXiv:2004.08122 [cs, eess], Apr. 2020, Accessed: Jun. 07, 2020. [Online]. Available: http://arxiv.org/abs/2004.08122
[17] M. Bulk et al., “Quantitative MRI and laser ablation-inductively coupled plasma-mass spectrometry imaging of iron in the frontal cortex of healthy controls and Alzheimer’s disease patients,” NeuroImage, vol. 215, p. 116808, Jul. 2020, doi: 10.1016/j.neuroimage.2020.116808.
[18] T. Kroes et al., “PIM: A visualization-oriented web application for monitoring and debugging of large-scale image processing studies,” in Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, International Society for Optics; Photonics, Mar. 2020, p. 1131808. doi: 10.1117/12.2541540.
[19] D. Lähnemann et al., “Eleven grand challenges in single-cell data science,” Genome Biology, vol. 21, no. 1, p. 31, Feb. 2020, doi: 10.1186/s13059-020-1926-6.
[20] B. Stoel, “Use of artificial intelligence in imaging in rheumatology – current status and future perspectives,” RMD Open, vol. 6, no. 1, p. e001063, Jan. 2020, doi: 10.1136/rmdopen-2019-001063.
[21] K. de Ruiter et al., “Helminth infections drive heterogeneity in human type 2 and regulatory cells,” Science Translational Medicine, vol. 12, no. 524, Jan. 2020, doi: 10.1126/scitranslmed.aaw3703.
[22] Q. Cao, A. Broersen, P. H. Kitslaar, M. Yuan, B. P. F. Lelieveldt, and J. Dijkstra, “Automatic coronary artery plaque thickness comparison between baseline and follow-up CCTA images,” Medical Physics, vol. 47, no. 3, pp. 1083–1093, 2020, doi: 10.1002/mp.13993.
[23] J. S. Suwandi et al., “Multidimensional analyses of proinsulin peptide-specific regulatory T cells induced by tolerogenic dendritic cells,” Journal of Autoimmunity, vol. 107, p. 102361, Feb. 2020, doi: 10.1016/j.jaut.2019.102361.
[24] T. Kobayashi et al., “Neointimal characteristics comparison between biodegradable-polymer and durable-polymer drug-eluting stents: 3-month follow-up optical coherence tomography light property analysis from the RESTORE registry,” Int J Cardiovasc Imaging, vol. 36, no. 2, pp. 205–215, Feb. 2020, doi: 10.1007/s10554-019-01718-2.
[25] M. Chu et al., “Effects of local hemodynamics and plaque characteristics on neointimal response following bioresorbable scaffolds implantation in coronary bifurcations,” Int J Cardiovasc Imaging, vol. 36, no. 2, pp. 241–249, Feb. 2020, doi: 10.1007/s10554-019-01721-7.
[26] M. Rafique et al., “EFFECT OF HIGH INTENSITY TRAINING ON CARDIAC ALLOGRAFT VASCULOPATHY ASSESSED WITH OPTICAL COHORENCE TOMOGRAPHY,” Journal of the American College of Cardiology, vol. 75, no. 11, Supplement 1, p. 1449, Mar. 2020, doi: 10.1016/S0735-1097(20)32076-3.
[27] K. Otsuka et al., “Polarimetric Signatures of Vascular Tissue Response Following Drug-Eluting Stent Implantation in Patients,” J Am Coll Cardiol, vol. 75, no. 11 Supplement 1, p. 1276, Mar. 2020, doi: 10.1016/S0735-1097(20)31903-3.
[28] P. Doradla et al., “Biomechanical Stress Profiling of Coronary Atherosclerosis: Identifying a Multifactorial Metric to Evaluate Plaque Rupture Risk,” J Am Coll Cardiol Img, vol. 13, no. 3, pp. 804–816, Mar. 2020, doi: 10.1016/j.jcmg.2019.01.033.
[29] K. Otsuka et al., “Intravascular Polarimetry in Patients With Coronary Artery Disease,” J Am Coll Cardiol Img, vol. 13, no. 3, pp. 790–801, Mar. 2020, doi: 10.1016/j.jcmg.2019.06.015.
[30] J. T. Senders et al., “An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning,” Neurosurgery, vol. 86, no. 2, pp. E184–E192, Feb. 2020, doi: 10.1093/neuros/nyz403.
[31] W. A. J. Birkhoff, L. van Manen, J. Dijkstra, M. L. De Kam, J. C. van Meurs, and A. F. Cohen, “Correction to: Retinal oximetry and fractal analysis of capillary maps in sickle cell disease patients and matched healthy volunteers,” Graefes Arch Clin Exp Ophthalmol, vol. 258, no. 1, pp. 219–220, Jan. 2020, doi: 10.1007/s00417-019-04512-x.
[32] I. Kumsars et al., “Randomised comparison of provisional side branch stenting versus a two-stent strategy for treatment of true coronary bifurcation lesions involving a large side branch: The Nordic-Baltic Bifurcation Study IV,” Open Heart, vol. 7, no. 1, p. e000947, 2020, doi: 10.1136/openhrt-2018-000947.
[33] K. Otsuka et al., “FIBROUS CAP COMPOSITION IN PATIENTS WITH ACUTE OR CHRONIC CORONARY SYNDROMES: INSIGHTS FROM INTRAVASCULAR POLARIMETRY,” Journal of the American College of Cardiology, vol. 75, no. 11, p. 40, Mar. 2020, doi: 10.1016/S0735-1097(20)30667-7.
[34] N. Pezzotti et al., “An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction: Application to the 2019 fastMRI Challenge,” arXiv:2004.07339 [cs, eess], Apr. 2020, Accessed: Aug. 31, 2020. [Online]. Available: http://arxiv.org/abs/2004.07339
[35] A. Keo et al., “Transcriptomic signatures of brain regional vulnerability to Parkinson’s disease,” Commun Biol, vol. 3, no. 1, p. 101, Dec. 2020, doi: 10.1038/s42003-020-0804-9.
[36] Q. Tao, B. P. F. Lelieveldt, and R. J. van der Geest, “Deep Learning for Quantitative Cardiac MRI,” American Journal of Roentgenology, vol. 214, no. 3, pp. 529–535, Mar. 2020, doi: 10.2214/AJR.19.21927.
[37] R. Yuste et al., “A community-based transcriptomics classification and nomenclature of neocortical cell types,” Nat Neurosci, Aug. 2020, doi: 10.1038/s41593-020-0685-8.
[38] A. Somarakis et al., “Visual cohort comparison for spatial single-cell omics-data,” arXiv:2006.05175 [cs, q-bio], Jul. 2020, Accessed: Aug. 31, 2020. [Online]. Available: http://arxiv.org/abs/2006.05175
[39] T. E. Bakken et al., “Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse,” bioRxiv, p. 2020.03.31.016972, Apr. 2020, doi: 10.1101/2020.03.31.016972.
[40] T. Abdelaal, P. de Raadt, B. P. F. Lelieveldt, M. J. T. Reinders, and A. Mahfouz, “SCHNEL: Scalable clustering of high dimensional single-cell data,” Bioinformatics, preprint, Mar. 2020. doi: 10.1101/2020.03.30.015925.
[41] V. Thondapu et al., “High Spatial Endothelial Shear Stress Gradient Independently Predicts Site of Acute Coronary Plaque Rupture and Erosion,” Cardiovascular Research, p. cvaa251, Aug. 2020, doi: 10.1093/cvr/cvaa251.
[42] H. Y. Santema, J. Stolk, M. Los, B. C. Stoel, R. Tsonaka, and I. T. Merth, “Prediction of lung function and lung density of young adults who had bronchopulmonary dysplasia,” ERJ Open Res, vol. 6, no. 4, pp. 00157–2020, Oct. 2020, doi: 10.1183/23120541.00157-2020.
[43] K. J. Nahon et al., “The effect of mirabegron on energy expenditure and brown adipose tissue in healthy lean South <span style=”font-variant:small-caps;”>Asian and Europid</span> men,” Diabetes Obes Metab, p. dom.14120, Jul. 2020, doi: 10.1111/dom.14120.
[44] Razzi Francesca et al., “Abstract 13819: Is an Adult Familial Hypercholesterolemia, Swine Model Suited to Test Safety and Efficacy of Drug-eluting Coronary Stents?” Circulation, vol. 142, no. Suppl_3, pp. A13819–A13819, Nov. 2020, doi: 10.1161/circ.142.suppl_3.13819.
[45] G. Abreu-Vieira et al., “Human Brown Adipose Tissue Estimated With Magnetic Resonance Imaging Undergoes Changes in Composition After Cold Exposure: An in vivo MRI Study in Healthy Volunteers,” Front. Endocrinol., vol. 10, p. 898, Jan. 2020, doi: 10.3389/fendo.2019.00898.
[46] 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,” Bioinformatics, preprint, Jun. 2020. doi: 10.1101/2020.06.19.158808.
[47] I. A. Mulder et al., “Increased Mortality and Vascular Phenotype in a Knock-In Mouse Model of Retinal Vasculopathy With Cerebral Leukoencephalopathy and Systemic Manifestations,” Stroke, vol. 51, no. 1, pp. 300–307, Jan. 2020, doi: 10.1161/STROKEAHA.119.025176.
[48] M. Niklas et al., “The biomedical sensor Cell-Fit-HD 4D, reveals individual tumor cell fate in response to microscopic ion deposition,” Cancer Biology, preprint, Mar.
- doi: 10.1101/2020.03.12.987347.</span>
[49] P. J. Castaldi et al., “Machine Learning Characterization of COPD Subtypes,” Chest, vol. 157, no. 5, pp. 1147–1157, May 2020, doi: 10.1016/j.chest.2019.11.039.
[50] A. L. Young et al., “Disease Progression Modeling in Chronic Obstructive Pulmonary Disease,” Am J Respir Crit Care Med, vol. 201, no. 3, pp. 294–302, Feb. 2020, doi: 10.1164/rccm.201908-1600OC.
[51] M. S. Elmahdy, T. Ahuja, U. A. van der Heide, and M. Staring, “Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT,” in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Apr. 2020, pp. 577–580. doi: 10.1109/ISBI45749.2020.9098702.