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Publications 2022

[1] A. Das, C. Kelly, H. Ben-Arzi, R. J. van der Geest, S. Plein, and E. Dall’Armellina, “Acute intra-cavity 4D flow cardiovascular magnetic resonance predicts long-term adverse remodelling following ST-elevation myocardial infarction,” J Cardiovasc Magn Reson, vol. 24, no. 1, p. 64, Nov. 2022, doi: 10.1186/s12968-022-00889-7.

[2] A. S. D. Sardjoe Mishre et al., “The Infrared Thermography Toolbox: An Open-access Semi-automated Segmentation Tool for Extracting Skin Temperatures in the Thoracic Region including Supraclavicular Brown Adipose Tissue,” J Med Syst, vol. 46, no. 12, p. 89, Nov. 2022, doi: 10.1007/s10916-022-01871-7.

[3] J. Schlegel et al., “Biosensor Cell-Fit-HD4D for correlation of single-cell fate and microscale energy deposition in complex ion beams,” STAR Protocols, vol. 3, no. 4, p. 101798, Dec. 2022, doi: 10.1016/j.xpro.2022.101798.

[4] X. Dong et al., “Impact of body mass index and diabetes on myocardial fat content, interstitial fibrosis and function,” Int J Cardiovasc Imaging, Oct. 2022, doi: 10.1007/s10554-022-02723-8.

[5] S. Fremond et al., “2022-RA-648-ESGO Interpretable deep learning provides clues for prognostic refinement of the molecular endometrial cancer classification,” International Journal of Gynecologic Cancer, vol. 32, no. Suppl 2, Oct. 2022, doi: 10.1136/ijgc-2022-ESGO.221.

[6] A. Demirkiran et al., “Post-myocardial infarction late diastolic left ventricular blood flow energetics are independently associated with left ventricular remodeling,” European Heart Journal, vol. 43, no. Supplement_2, p. ehac544.1297, Oct. 2022, doi: 10.1093/eurheartj/ehac544.1297.

[7] B. Gaszner, T. Simor, R. J. Van Der Geest, N. Farkas, and Z. Meiszterics, “Increased arterial stiffness predict major adverse cardiovascular events in post-infarcted patients. Do parameters and methods matter?” European Heart Journal, vol. 43, no. Supplement_2, p. ehac544.2294, Oct. 2022, doi: 10.1093/eurheartj/ehac544.2294.

[8] R. P. Amier et al., “Cardiac dysfunction in relation to vascular brain injury, cognitive impairment and depressive symptoms; The Heart-Brain Connection Study,” European Heart Journal, vol. 43, no. Supplement_2, p. ehac544.2004, Oct. 2022, doi: 10.1093/eurheartj/ehac544.2004.

[9] F. Van Driest, R. J. Van Der Geest, J. Dijkstra, J. W. Jukema, A. J. H. A. Scholte, and A. Broersen, “Automatic quantification of plaque progression dynamics as assessed by serial coronary computed tomography angiography using scan-quality-based vessel specific thresholds,” European Heart Journal, vol. 43, no. Supplement_2, p. ehac544.214, Oct. 2022, doi: 10.1093/eurheartj/ehac544.214.

[10] A. Nakajima et al., “Biomarkers associated with coronary high-risk plaques,” J Thromb Thrombolysis, Oct. 2022, doi: 10.1007/s11239-022-02709-2.

[11] A. Popa et al., Visual Analysis of RIS Data for Endmember Selection. The Eurographics Association,

1. doi: 10.2312/gch.20221233.</span>

[12] A. Ramasamy et al., “OP1 A novel coronary computed tomography angiography deep-learning methodology for coronary atheroma assessment trained using near-infrared spectroscopy-intravascular ultrasound,” Heart, vol. 108, no. Suppl 2, pp. A1–A1, Sep. 2022, doi: 10.1136/heartjnl-2022-BSCI.1.

[13] P. Mody, N. F. Chaves-de-Plaza, K. Hildebrandt, and M. Staring, “Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty,” in Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, C. H. Sudre, C. F. Baumgartner, A. Dalca, C. Qin, R. Tanno, K. Van Leemput, and W. M. Wells III, Eds., in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022, pp. 70–79. doi: 10.1007/978-3-031-16749-2_7.

[14] L.-H. Cheng, X. Sun, and R. J. van der Geest, “Contrastive Learning for Echocardiographic View Integration,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, and S. Li, Eds., in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022, pp. 340–349. doi: 10.1007/978-3-031-16440-8_33.

[15] L. Braunstorfer et al., “Non-contrast free-breathing whole-heart 3D cine cardiovascular magnetic resonance with a novel 3D radial leaf trajectory,” Magnetic Resonance Imaging, Sep. 2022, doi: 10.1016/j.mri.2022.09.003.

[16] X. Sun, L.-H. Cheng, S. Plein, P. Garg, and R. J. van der Geest, “Transformer Based Feature Fusion for Left Ventricle Segmentation in 4D Flow MRI,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, and S. Li, Eds., in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022, pp. 370–379. doi: 10.1007/978-3-031-16443-9_36.

[17] A. Demirkiran et al., “Association of left ventricular flow energetics with remodeling after myocardial infarction: New hemodynamic insights for left ventricular remodeling,” International Journal of Cardiology, Aug. 2022, doi: 10.1016/j.ijcard.2022.08.040.

[18] S. Alabed et al., “The quality of reporting in cardiac MRI artificial intelligence segmentation studies - a systematic review,” European Heart Journal - Cardiovascular Imaging, vol. 23, no. Supplement_2, p. jeac141.002, Sep. 2022, doi: 10.1093/ehjci/jeac141.002.

[19] H. Assadi et al., “The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review,” Medicina, vol. 58, no. 8, p. 1087, Aug. 2022, doi: 10.3390/medicina58081087.

[20] N. F. Chaves-de-Plaza, P. Mody, K. Hildebrandt, M. Staring, H. de Ridder, and R. van Egmond, “Towards Fast AI-Infused Human-Centered Contouring Workflows for Adaptive Proton Therapy in the Head and Neck.” arXiv, Aug. 2022. doi: 10.48550/arXiv.2208.04675.

[21] B. McConnell et al., “Acute vasodilator response testing in the adult Fontan circulation using non-invasive 4D Flow MRI: A proof-of-principle study,” Cardiol Young, pp. 1–8, Aug. 2022, doi: 10.1017/S1047951122002426.

[22] S. Alabed et al., “Quality of reporting in AI cardiac MRI segmentation studies – A systematic review and recommendations for future studies,” Front. Cardiovasc. Med., vol. 9, p. 956811, Jul. 2022, doi: 10.3389/fcvm.2022.956811.

[23] O. D. Bijlstra et al., “Integration of Three-Dimensional Liver Models in a Multimodal Image-Guided Robotic Liver Surgery Cockpit,” Life, vol. 12, no. 5, p. 667, May 2022, doi: 10.3390/life12050667.

[24] T. P. Brouwer et al., “Local and systemic immune profiles of human pancreatic ductal adenocarcinoma revealed by single-cell mass cytometry,” J Immunother Cancer, vol. 10, no. 7, p. e004638, Jul. 2022, doi: 10.1136/jitc-2022-004638.

[25] O. M. Neve et al., “Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium Contrast: A Multicenter, Multivendor Study,” Radiology: Artificial Intelligence, p. e210300, Jun. 2022, doi: 10.1148/ryai.210300.

[26] S. Alabed et al., “Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction,” Radiology, p. 212929, Jun. 2022, doi: 10.1148/radiol.212929.

[27] P. Mody, N. Chaves-de-Plaza, K. Hildebrandt, R. van Egmond, H. de Ridder, and M. Staring, “Comparing Bayesian models for organ contouring in head and neck radiotherapy,” in Medical Imaging 2022: Image Processing, I. Išgum and O. Colliot, Eds., San Diego, United States: SPIE, Apr. 2022, p. 13. doi: 10.1117/12.2611083.

[28] H. Assadi et al., “Mitral regurgitation quantified by CMR 4D-flow is associated with microvascular obstruction post reperfused ST-segment elevation myocardial infarction,” BMC Res Notes, vol. 15, no. 1, p. 181, Dec. 2022, doi: 10.1186/s13104-022-06063-7.

[29] S. Alabed et al., “Machine Learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension,” European Heart Journal - Digital Health, p. ztac022, May 2022, doi: 10.1093/ehjdh/ztac022.

[30] E. B. Turkbey et al., “Left Ventricular Structure, Tissue Composition, and Aortic Distensibility in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Intervention and Complications,” The American Journal of Cardiology, p. S0002914922003423, Apr. 2022, doi: 10.1016/j.amjcard.2022.03.036.

[31] X. Sun, L.-H. Cheng, and R. J. van der Geest, “Self- and Cross-attention based Transformer for left ventricle segmentation in 4D flow MRI,” Apr. 2022, Accessed: May 02,

1. [Online]. Available:

https://openreview.net/forum?id=gDocX1Js4zN</span>

[32] M. Araki et al., “Optical coherence tomography in coronary atherosclerosis assessment and intervention,” Nat Rev Cardiol, Apr. 2022, doi: 10.1038/s41569-022-00687-9.

[33] B. P. Hoppe, B. C. Stoel, and P. E. Postmus, “Natural Course of Cysts in Birt-Hogg-Dubé Syndrome,” Am J Respir Crit Care Med, pp. rccm.202106–1382IM, Apr. 2022, doi: 10.1164/rccm.202106-1382IM.

[34] Q. Tao and R. J. van der Geest, “Artificial Intelligence-Based Evaluation of Functional Cardiac Magnetic Resonance Imaging,” in Artificial Intelligence in Cardiothoracic Imaging, C. N. De Cecco, M. van Assen, and T. Leiner, Eds., Cham: Springer International Publishing, 2022, pp. 321–331. doi: 10.1007/978-3-030-92087-6_33.

[35] J. Huang et al., “TCTAP A-050 Calcified Plaque Detected on Optical Coherence Tomography With Deep Learning and Cross-Validated With Optical and Ultrasonic Signals: A Complementary Appraisal and Preamble to the Use of Combined IVUS-OCT Catheter,” Journal of the American College of Cardiology, vol. 79, no. 15_Supplement, pp. S31–S32, Apr. 2022, doi: 10.1016/j.jacc.2022.03.073.

[36] F. Alandejani et al., “Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements,” J Cardiovasc Magn Reson, vol. 24, no. 1, p. 25, Dec. 2022, doi: 10.1186/s12968-022-00855-3.

[37] Y. Li, M. S. Elmahdy, M. S. K. Lew, and M. Staring, “Transformation-consistent semi-supervised learning for prostate CT radiotherapy,” in Medical Imaging 2022: Computer-Aided Diagnosis, K. M. Iftekharuddin, K. Drukker, M. A. Mazurowski, H. Lu, C. Muramatsu, and R. K. Samala, Eds., San Diego, United States: SPIE, Apr. 2022, p. 76. doi: 10.1117/12.2604968.

[38] W. M. Brink, S. Yousefi, P. Bhatnagar, R. F. Remis, M. Staring, and A. G. Webb, “Personalized local <span style=”font-variant:small-caps;”>SAR</span> prediction for parallel transmit neuroimaging at <span style=”font-variant:small-caps;”>7T</span> from a single <span style=”font-variant:small-caps;”>T1</span> ‐weighted dataset,” Magnetic Resonance in Med, p. mrm.29215, Mar. 2022, doi: 10.1002/mrm.29215.

[39] M. Niklas et al., “Biosensor for deconvolution of individual cell fate in response to ion beam irradiation,” Cell Reports Methods, p. 100169, Feb. 2022, doi: 10.1016/j.crmeth.2022.100169.

[40] M. Sohani, R. J. van der Geest, A. Maier, A. J. Powell, and M. H. Moghari, “Improved cardiac T1 mapping accuracy and precision with a new hybrid MOLLI and SASHA technique: MOSHA,” Magnetic Resonance Imaging, p. S0730725X22000297, Feb. 2022, doi: 10.1016/j.mri.2022.02.004.

[41] J. Malimban et al., “Deep learning-based segmentation of the thorax in mouse micro-CT scans,” Sci Rep, vol. 12, no. 1, p. 1822, Feb. 2022, doi: 10.1038/s41598-022-05868-7.

[42] F. Y. van Driest et al., “Utilizing (serial) coronary computed tomography angiography (CCTA) to predict plaque progression and major adverse cardiac events (MACE): Results, merits and challenges,” Eur Radiol, Jan. 2022, doi: 10.1007/s00330-021-08393-9.

[43] P. C. Habets et al., “Transcriptional and cell type profiles of cortical brain regions showing ultradian cortisol rhythm dependent responses to emotional face stimulation,” Neuroscience, preprint, Jan. 2022. doi: 10.1101/2022.01.05.475032.

[44] X. Zhao et al., “Ventricular flow analysis and its association with exertional capacity in repaired tetralogy of Fallot: 4D flow cardiovascular magnetic resonance study,” J Cardiovasc Magn Reson, vol. 24, no. 1, p. 4, Dec. 2022, doi: 10.1186/s12968-021-00832-2.

[45] X. Zhao et al., “Right ventricular energetic biomarkers from 4D Flow CMR are associated with exertional capacity in pulmonary arterial hypertension,” Journal of Cardiovascular Magnetic Resonance, vol. 24, no. 1, p. 61, Dec. 2022, doi: 10.1186/s12968-022-00896-8.

[46] S. Fremond et al., “Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: A combined analysis of the PORTEC randomised trials and clinical cohorts,” The Lancet Digital Health, Dec. 2022, doi: 10.1016/S2589-7500(22)00210-2.

[47] J. Venlet et al., “The transmural activation interval: A new mapping tool to identify ventricular tachycardia substrates in right ventricular cardiomyopathy,” EP Europace, p. euac220, Dec. 2022, doi: 10.1093/europace/euac220.

[48] J. Huang et al., “Plaque burden estimated from optical coherence tomography with deep learning: In vivo validation using co-registered intravascular ultrasound,” Catheterization and Cardiovascular Interventions, vol. n/a, no. n/a, Dec. 2022, doi: 10.1002/ccd.30525.

[49] K. Koolstra, M. Staring, P. de Bruin, and M. J. P. van Osch, “Subject-specific optimization of background suppression for arterial spin labeling magnetic resonance imaging using a feedback loop on the scanner,” NMR Biomed, vol. 35, no. 9, p. e4746, Sep. 2022, doi: 10.1002/nbm.4746.

[50] S. Omara et al., “Assessing the field of view of multisize electrodes in ischemic cardiomyopathy by validating against ex-vivo high resolution cardiac magnetic resonance,” EP Europace, vol. 24, no. Supplement_1, p. euac053.344, May 2022, doi: 10.1093/europace/euac053.344.

[51] X. Sun, L.-H. Cheng, and R. J. van der Geest, “Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification,” in Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers, O. Camara, E. Puyol-Antón, C. Qin, M. Sermesant, A. Suinesiaputra, S. Wang, and A. Young, Eds., in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022, pp. 476–484. doi: 10.1007/978-3-031-23443-9_45.

[52] C. M. W. Goedmakers et al., “Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods,” Brain Spine, vol. 2, p. 101666, 2022, doi: 10.1016/j.bas.2022.101666.