activity
20182023
most citedA Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation

59 citations · 98 across the 10 of their papers we have counts for

collaborators

6 papers

eess.IV20225 cited

Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling

Dieuwertje Alblas, Christoph Brune, Kak Khee Yeung +1

Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been construct…

physics.med-ph202231 cited

Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

Nathan Blanken, Jelmer M. Wolterink, Hervé Delingette +3

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles i…

eess.IV2021

Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors

Dieuwertje Alblas, Christoph Brune, Jelmer M. Wolterink

Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.…

cs.LG2019

Learned SVD: solving inverse problems via hybrid autoencoding

Yoeri E. Boink, Christoph Brune

Our world is full of physics-driven data where effective mappings between data manifolds are desired. There is an increasing demand for understanding combined model-based and data-…

eess.IV201959 cited

A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation

Yoeri E. Boink, Srirang Manohar, Christoph Brune

In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available when only the initial pressure is reconstructed. To obt…

math.OC2018

Directional Sinogram Inpainting for Limited Angle Tomography

Robert Tovey, Martin Benning, Christoph Brune +5

In this paper we propose a new joint model for the reconstruction of tomography data under limited angle sampling regimes. In many applications of Tomography, e.g. Electron Microsc…