Dr. Sanne G. M. Van Velzen Profile

Dr. Sanne G. M. Van Velzen

at Univ Medical Ctr Utrecht

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Author

Publications (5)

SPIE Journal Paper | 15 May 2024

Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography

Nils Hampe, Sanne G. M. van Velzen, Jelmer M. Wolterink, Carlos Collet, José P. S. Henriques, Nils Planken, Ivana Išgum

JMI, Vol. 11, Issue 03, 034001, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.034001

KEYWORDS: Arteries, Anatomy, Image segmentation, Education and training, Neural networks, Machine learning, Veins, Image resolution, Feature extraction, Automatic tracking

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Proceedings Article | 2 April 2024 Presentation + Paper

Deep-learning-based prediction of fractional flow reserve after invasive coronary artery treatment

Nils Hampe, Sanne G. Van Velzen, Jean-Paul Aben, Rudolf L. van Herten, Carlos Collet, Ivana Išgum

Proceedings Volume 12926, 129260D (2024) https://doi.org/10.1117/12.3006761

KEYWORDS: Arteries, Deep learning, Computed tomography

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SPIE Journal Paper | 31 May 2022

Generative models for reproducible coronary calcium scoring

Sanne G. Van Velzen, Bob de Vos, Julia M. Noothout, Helena Verkooijen, Max Viergever, Ivana Išgum

JMI, Vol. 9, Issue 05, 052406, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.052406

KEYWORDS: Calcium, Heart, Image segmentation, Computed tomography, Arteries, Gallium nitride, Tissues, Chest, Chemical vapor deposition, Visualization

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Proceedings Article | 10 March 2020 Presentation + Paper

Coronary artery calcium scoring: Can we do better?

Sanne G. Van velzen, Bob de Vos, Helena Verkooijen, Tim Leiner, Max Viergever, Ivana Išgum

Proceedings Volume 11313, 113130G (2020) https://doi.org/10.1117/12.2549557

KEYWORDS: Calcium, Computed tomography, Arteries, Chest, Medical imaging, Radiology

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Coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) that can be quantified in CT scans showing the heart. CAC lesions are defined as lesions in the coronary arteries with image intensity above 130 HU. The use of a threshold may lead to under- or over-estimation of the amount of CAC and, hence, to incorrect cardiovascular categorization of patients. This is especially pronounced in CT scans without ECG-synchronization where lesions are more subject to cardiac motion and partial volume effects. To address this, we propose a method for quantification of CAC without a threshold. A set of 373 cardiac and 1181 chest CT scans was included to develop the method and a set of 21 scan-rescan pairs (42 scans) was included for final evaluation. Assuming that the attenuation of CAC is superimposed on the attenuation of the artery, we aimed to separate the CAC from the coronary arteries by employing a CycleGAN to generate a synthetic image without CAC from an image containing CAC and vice versa. By subtracting the synthetic image without CAC from the image with CAC, a CAC map is created. The CAC-map can subsequently be used to identify and quantify CAC. The ground truth, i.e. the true amount of CAC, can not be established, therefore, in this work the results generated by the method are compared with clinical calcium scoring in terms of reproducibility. The average relative difference between the calcium scores in scan-rescan pairs of scans was 50% with the proposed method and 86% for the conventional method. Moreover, the correlation between CAC pseudo masses in scan-rescan pairs was 0.92 with the proposed method and 0.89 with conventional calcium scoring. Our proposed method is able to identify and quantify CAC lesions in CT scans without using an intensity level thresholding. This might allow for more reproducible quantification of CAC in CT scans made without ECG synchronization, and, therefore, it might allow more accurate CVD risk prediction.

Proceedings Article | 15 March 2019 Presentation + Paper

Direct prediction of cardiovascular mortality from low-dose chest CT using deep learning

Sanne G. van Velzen, Majd Zreik, Nikolas Lessmann, Max Viergever, Pim de Jong, Helena Verkooijen, Ivana Išgum

Proceedings Volume 10949, 109490X (2019) https://doi.org/10.1117/12.2512400

KEYWORDS: Chest, Computed tomography, Heart, Lung cancer, Image analysis, Feature extraction, Neural networks

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