Division of Image Processing / LKEB

Department of Radiology, Leiden University Medical Center

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Publications 2023

[1] H. Assadi et al., “Automated 4D flow cardiac MRI pipeline to derive peak mitral inflow diastolic velocities using short-axis cine stack: Two centre validation study against echocardiographic pulse-wave doppler,” BMC Cardiovascular Disorders, vol. 23, no. 1, p. 24, Jan. 2023, doi: 10.1186/s12872-023-03052-x.

[2] V. J. M. Zeijen et al., “Association between renal sympathetic denervation and arterial stiffness: The ASORAS study,” Journal of Hypertension, p. 10.1097/HJH.0000000000003361, Jan. 2023, doi: 10.1097/HJH.0000000000003361.

[3] R. Li et al., “The Importance of Mitral Valve Prolapse Doming Volume in the Assessment of Left Ventricular Stroke Volume with Cardiac MRI,” Medical Sciences, vol. 11, no. 1, p. 13, Mar. 2023, doi: 10.3390/medsci11010013.

[4] T. V. Tran et al., “Prevalence of abnormal spirometry in individuals with a smoking history and no known obstructive lung disease,” Respiratory Medicine, p. 107126, Jan. 2023, doi: 10.1016/j.rmed.2023.107126.

[5] 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, Jan. 2023, pp. 476–484. doi: 10.1007/978-3-031-23443-9_45.

[6] S. Alabed et al., “33 Cardiac MRI thresholds for improvement in pulmonary arterial hypertension,” Heart, vol. 109, no. Suppl 1, pp. A25–A25, Jan. 2023, doi: 10.1136/heartjnl-2022-BSCMR.32.

[7] G. Spierenburg et al., “MRI of diffuse-type tenosynovial giant cell tumour in the knee: A guide for diagnosis and treatment response assessment,” Insights into Imaging, vol. 14, no. 1, p. 22, Feb. 2023, doi: 10.1186/s13244-023-01367-z.

[8] L. N. Andreasen et al., “Early healing after treatment of coronary lesions by thin strut everolimus, or thicker strut biolimus eluting bioabsorbable polymer stents: The SORT-OUT VIII OCT study,” Catheter Cardiovasc Interv, Feb. 2023, doi: 10.1002/ccd.30579.

[9] E. K. W. Poon et al., “An optical coherence tomography and endothelial shear stress study of a novel bioresorbable bypass graft,” Sci Rep, vol. 13, no. 1, p. 2941, Feb. 2023, doi: 10.1038/s41598-023-29573-1.

[10] O. Bijlstra et al., “Distribution of ICG around colorectal liver metastases: Towards real-time intraoperative quantitative tumor margin assessment,” European Journal of Surgical Oncology, vol. 49, no. 2, pp. e151–e152, Feb. 2023, doi: 10.1016/j.ejso.2022.11.423.

[11] G. Doolub et al., “Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease,” Medical Sciences, vol. 11, no. 1, p. 20, Mar. 2023, doi: 10.3390/medsci11010020.

[12] L. A. Hajjar et al., “Microvascular lung vessels obstructive thromboinflammatory syndrome in patients with COVID-19: Insights from lung intravascular optical coherence tomography,” Front Med (Lausanne), vol. 10, p. 1050531, Feb. 2023, doi: 10.3389/fmed.2023.1050531.

[13] N. Guo et al., “Immune subset-committed proliferating cells populate the human foetal intestine throughout the second trimester of gestation,” Nat Commun, vol. 14, no. 1, p. 1318, Mar. 2023, doi: 10.1038/s41467-023-37052-4.

[14] C. Martín-Isla et al., “Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge,” IEEE Journal of Biomedical and Health Informatics, pp. 1–14, 2023, doi: 10.1109/JBHI.2023.3267857.

[15] A. S. D. Sardjoe Mishre et al., “Image registration and mutual thresholding enable low interimage variability across dynamic MRI measurements of supraclavicular brown adipose tissue during mild cold exposure,” Magn Reson Med, May 2023, doi: 10.1002/mrm.29707.

[16] B. Yu et al., “Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear,” Knowledge-Based Systems, p. 110721, Jun. 2023, doi: 10.1016/j.knosys.2023.110721.

[17] Y. Zhang et al., “Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain,” Sci Rep, vol. 13, no. 1, p. 9567, Jun. 2023, doi: 10.1038/s41598-023-36638-8.

[18] C.-Y. Liu et al., “Sex-specific associations in multiparametric 3 T MRI measurements in adult livers,” Abdom Radiol, Jun. 2023, doi: 10.1007/s00261-023-03981-3.

[19] M. Hawrylycz et al., “A guide to the BRAIN Initiative Cell Census Network data ecosystem,” PLOS Biology, vol. 21, no. 6, p. e3002133, Jun. 2023, doi: 10.1371/journal.pbio.3002133.

[20] F. Razzi et al., “Plaque burden is associated with minimal intimal coverage following drug-eluting stent implantation in an adult familial hypercholesterolemia swine model,” Sci Rep, vol. 13, no. 1, p. 10683, Jul. 2023, doi: 10.1038/s41598-023-37690-0.

[21] M. M. Bissell et al., “4D Flow cardiovascular magnetic resonance consensus statement: 2023 update,” Journal of Cardiovascular Magnetic Resonance, vol. 25, no. 1, p. 40, Jul. 2023, doi: 10.1186/s12968-023-00942-z.

[22] P. Hendriks et al., “Intraprocedural assessment of ablation margins using computed tomography co-registration in hepatocellular carcinoma treatment with percutaneous ablation: IAMCOMPLETE study,” Diagnostic and Interventional Imaging, Jul. 2023, doi: 10.1016/j.diii.2023.07.002.

[23] O. M. Neve et al., “Automated 2-Dimensional Measurement of Vestibular Schwannoma: Validity and Accuracy of an Artificial Intelligence Algorithm,” Otolaryngol Head Neck Surg, Aug. 2023, doi: 10.1002/ohn.470.

[24] X. Huang et al., “POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation,” Medical Image Analysis, vol. 89, p. 102922, Oct. 2023, doi: 10.1016/j.media.2023.102922.

[25] S. Tayebi Arasteh et al., “Automated segmentation of 3D cine cardiovascular magnetic resonance imaging,” Front. Cardiovasc. Med., vol. 10, p. 1167500, Sep. 2023, doi: 10.3389/fcvm.2023.1167500.

[26] X. Zhao et al., “Age- and sex-specific reference values of biventricular flow components and kinetic energy by 4D flow cardiovascular magnetic resonance in healthy subjects,” J Cardiovasc Magn Reson, vol. 25, no. 1, pp. 1–14, Dec. 2023, doi: 10.1186/s12968-023-00960-x.

[27] S. Alabed et al., “P10 Defining cardiac MRI change thresholds based on how a patient feels, functions and survives post pulmonary arterial hypertension treatment,” Heart, vol. 109, no. Suppl 4, pp. A5–A6, Oct. 2023, doi: 10.1136/heartjnl-2023-BSCI.14.

[28] E. K. W. Poon et al., “Angiography and optical coherence tomography derived shear stress: Are they equivalent in my opinion?” Int J Cardiovasc Imaging, Sep. 2023, doi: 10.1007/s10554-023-02949-0.

[29] S. Basu et al., Cytosplore Simian Viewer: Visual Exploration for Multi-Species Single-Cell RNA Sequencing Data. The Eurographics Association, 2023. doi: 10.2312/vcbm.20231219.

[30] A. Vieth, B. Lelieveldt, E. Eisemann, A. Vilanova, and T. Höllt, Interactions for Seamlessly Coupled Exploration of High-Dimensional Images and Hierarchical Embeddings. The Eurographics Association, 2023. doi: 10.2312/vmv.20231227.

[31] Ş. Vădineanu, D. M. Pelt, O. Dzyubachyk, and K. J. Batenburg, “Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations,” in Medical Image Learning with Limited and Noisy Data, Z. Xue, S. Antani, G. Zamzmi, F. Yang, S. Rajaraman, S. X. Huang, M. G. Linguraru, and Z. Liang, Eds., in Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2023, pp. 3–13. doi: 10.1007/978-3-031-44917-8_1.

[32] R. Bajaj et al., “TCT-635 Performance of Near-Infrared Spectroscopy-Intravascular Ultrasound and Optical Coherence Tomography for Coronary Artery Plaque Structural Stress Analysis: Validation Against Histology,” Journal of the American College of Cardiology, vol. 82, no. 17_Supplement, pp. B255–B256, Oct. 2023, doi: 10.1016/j.jacc.2023.09.646.

[33] X. Zhao et al., “Aortic diastolic flow displacement from phase-contrast CMR is associated with exercise capacity in healthy adults,” European Heart Journal, vol. 44, no. Supplement_2, p. ehad655.216, Nov. 2023, doi: 10.1093/eurheartj/ehad655.216.

[34] K. Ninomiya et al., “Optical coherence tomography assessment of dissection following Drug-Coated Balloon treatment in de-novo small vessel disease,” European Heart Journal, vol. 44, no. Supplement_2, p. ehad655.2198, Nov. 2023, doi: 10.1093/eurheartj/ehad655.2198.

[35] N. Kotoku et al., “Preprocedural physiological assessment of coronary disease patterns to predict haemodynamic outcomes post-PCI,” EuroIntervention, pp. EIJ-D-23-00516, Nov. 2023, doi: 10.4244/eij-d-23-00516.

[36] C. Li et al., “SpaceWalker enables interactive gradient exploration for spatial transcriptomics data,” Cell Rep Methods, p. 100645, Nov. 2023, doi: 10.1016/j.crmeth.2023.100645.

[37] N. L. Jorstad et al., “Comparative transcriptomics reveals human-specific cortical features,” Science, vol. 382, no. 6667, p. eade9516, Oct. 2023, doi: 10.1126/science.ade9516.

[38] M. Kassem et al., “The association between antiplatelet therapy and changes in intraplaque hemorrhage in patients with mild to moderate symptomatic carotid stenosis: A longitudinal MRI study,” Cerebrovasc Dis, Nov. 2023, doi: 10.1159/000535274.

[39] M. Kassem et al., “Application of mask images of contrast-enhanced MR angiography to detect carotid intraplaque hemorrhage in patients with moderate to severe symptomatic and asymptomatic carotid stenosis,” European Journal of Radiology, vol. 168, Nov. 2023, doi: 10.1016/j.ejrad.2023.111145.

[40] J. Wu et al., “Increased incidence of napkin-ring sign plaques on cervicocerebral computed tomography angiography associated with the risk of acute ischemic stroke occurrence,” Eur Radiol, Nov. 2023, doi: 10.1007/s00330-023-10404-w.

[41] E. C. Kuijper et al., “Antisense oligonucleotide-mediated disruption of HTT caspase-6 cleavage site ameliorates the phenotype of YAC128 Huntington disease mice,” Neurobiology of Disease, p. 106368, Nov. 2023, doi: 10.1016/j.nbd.2023.106368.

[42] F. Y. van Driest, A. Broersen, R. J. van der Geest, J. Wouter Jukema, A. J. H. A. Scholte, and J. Dijkstra, “Automatic Quantification of Local Plaque Thickness Differences as Assessed by Serial Coronary Computed Tomography Angiography Using Scan-Quality-Based Vessel-Specific Thresholds,” Cardiol Ther, Dec. 2023, doi: 10.1007/s40119-023-00341-6.

[43] S. Park et al., “A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion,” Sci Rep, vol. 13, no. 1, p. 22992, Dec. 2023, doi: 10.1038/s41598-023-50483-9.

[44] M. Çap et al., “Efficacy of human experts and an automated segmentation algorithm in quantifying disease pathology in coronary computed tomography angiography: A head-to-head comparison with intravascular ultrasound imaging,” Journal of Cardiovascular Computed Tomography, Dec. 2023, doi: 10.1016/j.jcct.2023.12.007.

[45] H. Assadi et al., “Cardiac MR modelling of systolic and diastolic blood pressure,” Open Heart, vol. 10, no. 2, p. e002484, Dec. 2023, doi: 10.1136/openhrt-2023-002484.

[46] J. Jia et al., “Automatic Pulmonary Function Estimation From Chest CT Scans Using Deep Regression Neural Networks: The Relation Between Structure and Function in Systemic Sclerosis,” IEEE Access, vol. PP, pp. 1–1, Jan. 2023, doi: 10.1109/ACCESS.2023.3337639.

[47] M. M. Bissell et al., “4D Flow cardiovascular magnetic resonance consensus statement: 2023 update,” J Cardiovasc Magn Reson, vol. 25, no. 1, p. 40, Jul. 2023, doi: 10.1186/s12968-023-00942-z.

[48] M. Kassem, Intraplaque hemorrhage on carotid mri in stroke patients: On the road towards clinical application. Maastricht: Maastricht University, 2023. doi: 10.26481/dis.20231212mk.

[49] X. Sun, L.-H. Cheng, S. Plein, P. Garg, M. H. Moghari, and R. J. van der Geest, “Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging,” Int J Cardiovasc Imaging, vol. 39, no. 5, pp. 1045–1053, May 2023, doi: 10.1007/s10554-023-02804-2.