Publications 2024
[1] H. Assadi et al., “Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance,” European Radiology Experimental, vol. 8, no. 1, p. 77, Jul. 2024, doi: 10.1186/s41747-024-00477-7.
[2] H. Assadi et al., “Validation of Left Atrial Volume Correction for Single Plane Method on Four-Chamber Cine Cardiac MRI,” Tomography, vol. 10, no. 4, pp. 459–470, Apr. 2024, doi: 10.3390/tomography10040035.
[3] R. Bajaj et al., “Accuracy of uniform vs non-uniform shrinkage of intravascular imaging-based coronary plaque models for plaque structural stress estimations: Validation against histology,” in Diagnostic and Therapeutic Applications of Light in Cardiology 2024, SPIE, Mar. 2024, p. 1281905. doi: 10.1117/12.3007278.
[4] L. Beljaards, N. Pezzotti, C. Rao, M. Doneva, M. J. P. van Osch, and M. Staring, “AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models,” Medical Physics, Jan. 2024, doi: 10.1002/mp.16918.
[5] S. H. Bulow Rasmussen et al., “Coronary stent edge segments as determinant of clinical outcomes: An OCTOBER Trial Substudy,” European Heart Journal, vol. 45, no. Supplement_1, p. ehae666.2346, Oct. 2024, doi: 10.1093/eurheartj/ehae666.2346.
[6] S. H. Bulow Rasmussen et al., “Coronary stent edge segments as determinant of clinical outcomes: An OCTOBER Trial Substudy,” European Heart Journal, vol. 45, no. Supplement_1, p. ehae666.2346, Oct. 2024, doi: 10.1093/eurheartj/ehae666.2346.
[7] N. F. Chaves-de-Plaza et al., “Depth for Multi-Modal Contour Ensembles,” Jun. 2024.
[8] N. F. Chaves-de-Plaza, P. Mody, M. Staring, R. van Egmond, A. Vilanova, and K. Hildebrandt, “Inclusion Depth for Contour Ensembles,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–12, Jan. 2024, doi: 10.1109/TVCG.2024.3350076.
[9] B. D. de Vos, H. Sokooti, M. Staring, and I. Išgum, “Machine learning in image registration,” in Medical Image Analysis, A. F. Frangi, J. L. Prince, and M. Sonka, Eds., in The MICCAI Society book Series., Academic Press, 2024, pp. 501–515. doi: 10.1016/B978-0-12-813657-7.00031-5.
[10] T. H. Dijkhuis et al., “Semi-automatic standardized analysis method to objectively evaluate near-infrared fluorescent dyes in image-guided surgery,” Journal of Biomedical Optics, vol. 29, no. 2, p. 026001, Feb. 2024, doi: 10.1117/1.JBO.29.2.026001.
[11] S. el Mathari et al., “First use of a new extended reality tool for preoperative planning in coronary artery bypass surgery: A case-report,” Journal of Surgical Case Reports, vol. 2024, no. 6, p. rjae383, Jun. 2024, doi: 10.1093/jscr/rjae383.
[12] T. Hassanzadeh et al., “A deep learning-based comparative MRI model to detect inflammatory changes in rheumatoid arthritis,” Biomedical Signal Processing and Control, vol. 88, p. 105612, Feb. 2024, doi: 10.1016/j.bspc.2023.105612.
[13] X. He et al., “Efficacy of Coronary Calcium Score in Predicting Coronary Artery Morphology in Patients With Obstructive Coronary Artery Disease,” Journal of the Society for Cardiovascular Angiography & Interventions, vol. 3, no. 3, Part B, p. 101308, Mar. 2024, doi: 10.1016/j.jscai.2024.101308.
[14] J. Jia et al., “Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts,” Scientific Reports, vol. 14, no. 1, p. 26666, Nov. 2024, doi: 10.1038/s41598-024-78393-4.
[15] J. Jia et al., “Explainable fully automated CT scoring of interstitial lung disease for patients suspected of systemic sclerosis by cascaded regression neural networks and its comparison with experts,” Scientific Reports, vol. 14, no. 1, p. 26666, Nov. 2024, doi: 10.1038/s41598-024-78393-4.
[16] J. Jia et al., “Using 3D point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest CT,” Computers in Biology and Medicine, vol. 182, p. 109192, Nov. 2024, doi: 10.1016/j.compbiomed.2024.109192.
[17] C. Jungen et al., “Regional cardiac denervation predicts sustained ventricular arrhythmias in non-ischemic cardiomyopathy patients without LGE on CMR imaging,” European Heart Journal, vol. 45, no. Supplement_1, p. ehae666.671, Oct. 2024, doi: 10.1093/eurheartj/ehae666.671.
[18] Y. Kanzaki et al., “Impact of multiple ballooning on coronary lesions as assessed by optical coherence tomography and intravascular ultrasound,” Catheterization and Cardiovascular Interventions, p. ccd.31239, Sep. 2024, doi: 10.1002/ccd.31239.
[19] R. Li et al., “Automated Quantification of Simple and Complex Aortic Flow Using 2D Phase Contrast MRI,” Medicina, vol. 60, no. 10, p. 1618, Oct. 2024, doi: 10.3390/medicina60101618.
[20] T. Newman et al., “Functional status is associated with mean right atrial pressure derived from cardiac MRI,” European Heart Journal, vol. 45, no. Supplement_1, p. ehae666.245, Oct. 2024, doi: 10.1093/eurheartj/ehae666.245.
[21] K. P. H. Nies et al., “Signal intensity and volume of carotid intraplaque hemorrhage on MRI and the risk of ipsilateral cerebrovascular events: The Plaque At RISK (PARISK) study,” Journal of Cardiovascular Magnetic Resonance, vol. 0, no. 0, Jun. 2024, doi: 10.1016/j.jocmr.2024.101049.
[22] S. Omara, Y. Kimura, A. P. Wijnmaalen, R. Van Der Geest, and K. Zeppenfeld, “Volume-weighted unipolar voltage identifies diffuse fibrosis in patients with non-ischemic cardiomyopathy,” EP Europace, vol. 26, no. Supplement_1, p. euae102.695, May 2024, doi: 10.1093/europace/euae102.695.
[23] R. Parasa et al., “Evaluation of the performance of deep learning-based methods in assessing plaque pathology in standalone optical and hybrid optical-based imaging,” in Diagnostic and Therapeutic Applications of Light in Cardiology 2024, SPIE, Mar. 2024, p. 1281902. doi: 10.1117/12.3000554.
[24] E. K. W. Poon et al., “Two Facets of Shear Stress Post Drug Coating Balloon: Angiography Versus Optical Coherence Tomography Fusion Approach,” Circulation: Cardiovascular Imaging, p. e016279, Mar. 2024, doi: 10.1161/CIRCIMAGING.123.016279.
[25] A. Ramasamy et al., “Computed tomography versus near-infrared spectroscopy for the assessment of coronary atherosclerosis,” EuroIntervention: Journal of EuroPCR in Collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology, vol. 20, no. 23, pp. e1465–e1475, Dec. 2024, doi: 10.4244/EIJ-D-24-00096.
[26] M. J. P. Rooijakkers et al., “Assessment of paravalvular regurgitation after transcatheter aortic valve replacement using 2D multi-venc and 4D flow CMR,” European Heart Journal. Cardiovascular Imaging, p. jeae035, Feb. 2024, doi: 10.1093/ehjci/jeae035.
[27] J. Simon et al., “Association of Left Atrial Appendage Morphology and Function With Stroke and Transient Ischemic Attack in Atrial Fibrillation Patients,” American Journal of Cardiology, vol. 0, no. 0, Mar. 2024, doi: 10.1016/j.amjcard.2024.03.025.
[28] B. C. Stoel, M. Staring, M. Reijnierse, and A. H. M. van der Helm-van Mil, “Deep learning in rheumatological image interpretation,” Nature Reviews Rheumatology, pp. 1–14, Feb. 2024, doi: 10.1038/s41584-023-01074-5.
[29] X. Sun, L.-H. Cheng, S. Plein, P. Garg, and R. J. van der Geest, “Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI,” Journal of Cardiovascular Magnetic Resonance, p. 100003, Jan. 2024, doi: 10.1016/j.jocmr.2023.100003.
[30] F. Y. van Driest et al., “Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms,” European Heart Journal - Imaging Methods and Practice, p. qyae069, Jul. 2024, doi: 10.1093/ehjimp/qyae069.
[31] S. Volinsky-Fremond et al., “Prediction of recurrence risk in endometrial cancer with multimodal deep learning,” Nature Medicine, pp. 1–12, May 2024, doi: 10.1038/s41591-024-02993-w.
[32] H. Wei et al., “Data-driven respiratory motion correction of dual-gated cardiac PET/CT for coronary plaque imaging,” in 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), Tampa, FL, USA: IEEE, Oct. 2024, pp. 1–1. doi: 10.1109/NSS/MIC/RTSD57108.2024.10656463.
[33] N. A. L. Yap et al., “Implications of coronary calcification on the assessment of plaque pathology: A comparison of computed tomography and multimodality intravascular imaging,” European Radiology, Aug. 2024, doi: 10.1007/s00330-024-10996-x.
[34] X. Zhang et al., “Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images,” International Journal of Computer Assisted Radiology and Surgery, Mar. 2024, doi: 10.1007/s11548-024-03086-2.
[35] F. Zhang et al., “Detectability of intracranial vessel wall atherosclerosis using black-blood spectral CT: A phantom and clinical study,” European Radiology Experimental, vol. 8, no. 1, p. 78, Jul. 2024, doi: 10.1186/s41747-024-00473-x.
[36] X. Zhou, Y. CHEN, R. Van Der Geest, P. Hu, and M.-Y. Ng, “Editorial: Advanced Quantitative Indexes in Cardiovascular Magnetic Resonance Imaging,” Frontiers in Cardiovascular Medicine, vol. 11, Feb. 2024.