58 citations · 147 across the 8 of their papers we have counts for
8 papers
Ultrasound Signal Processing: From Models to Deep Learning
Ben Luijten, Nishith Chennakeshava, Yonina C. Eldar +2
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms w…
Deep Proximal Learning for High-Resolution Plane Wave Compounding
Nishith Chennakeshava, Ben Luijten, Massimo Mischi +2
Plane Wave imaging enables many applications that require high frame rates, including localisation microscopy, shear wave elastography, and ultra-sensitive Doppler. To alleviate th…
A Model-Based Approach to Synthetic Data Set Generation for Patient-Ventilator Waveforms for Machine Learning and Educational Use
A. van Diepen, T. H. G. F. Bakkes, A. J. R. De Bie +4
Although mechanical ventilation is a lifesaving intervention in the ICU, it has harmful side-effects, such as barotrauma and volutrauma. These harms can occur due to asynchronies.…
Adaptive Ultrasound Beamforming using Deep Learning
Ben Luijten, Regev Cohen, Frederik J. de Bruijn +4
Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal…
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging
Iris A. M. Huijben, Bastiaan S. Veeling, Kees Janse +2
Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) co…
Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network
R. R. Wildeboer, R. J. G. van Sloun, C. K. Mannaerts +5
Shear-wave elastography (SWE) permits local estimation of tissue elasticity, an important imaging marker in biomedicine. This recently-developed, advanced technique assesses the sp…