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researcher

J. D. Beenhouwer

5 papers hereh-index 12433 citations94 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 5 papers where every author was matched, so the position is known.

fields
  • eess.IV4
  • math.OC1

identity via Semantic Scholar / OpenAlex

most citedDeep Learning Based Computed Tomography Whys and Wherefores

8 citations · 12 across the 4 of their papers we have counts for

collaborators
Showing eess.IVShow all

4 papers · 1 filter

eess.IV2023

DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data

Jens Renders, Banafshe Shafieizargar, Marleen Verhoye +3

Longitudinal MRI is an important diagnostic imaging tool for evaluating the effects of treatment and monitoring disease progression. However, MRI, and particularly longitudinal MRI…

eess.IV2019

To Recurse or not to Recurse,a Low Dose CT Study

Shabab Bazrafkan, Vincent Van Nieuwenhove, Jan Sijbers

Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to…

eess.IV2019★ 3 cited

Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT

Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2

Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exp…

eess.IV2019★ 8 cited

Deep Learning Based Computed Tomography Whys and Wherefores

Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2

This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.

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