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Jelmer M. Wolterink

Researcher at University of Twente

Publications -  101
Citations -  5394

Jelmer M. Wolterink is an academic researcher from University of Twente. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 23, co-authored 82 publications receiving 3312 citations. Previous affiliations of Jelmer M. Wolterink include University of Amsterdam & Utrecht University.

Papers
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Journal ArticleDOI

Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography

TL;DR: In this article, a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images, was presented.
Book ChapterDOI

Automatic Coronary Calcium Scoring in Cardiac CT Angiography Using Convolutional Neural Networks

TL;DR: In this article, the authors presented a pattern recognition method that automatically identifies and quantifies coronary artery calcification in CCTA in 50 patients equally distributed over five cardiovascular risk categories.
Journal ArticleDOI

Submillisievert coronary calcium quantification using model-based iterative reconstruction: A within-patient analysis

TL;DR: Radiation dose for coronary calcium scoring can be safely reduced to 0.4mSv using both HIR and MIR, while FBP is not feasible at these dose levels due to excessive noise.
Journal ArticleDOI

Etidronate halts systemic arterial calcification in pseudoxanthoma elasticum.

TL;DR: Etidronate treatment halts systemic arterial calcification in PXE and further research must assess the long term safety and efficacy of etidronates on clinical outcomes in PxE.
Posted Content

Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography

TL;DR: A method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images, demonstrates the feasibility of automatic non-Invasive detection and could potentially reduce the number of patients that unnecessarily undergo ICA.