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08/27/2023
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results...
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12/09/2022
A Learned Born Series for Highly-Scattering Media
A new method for solving the wave equation is presented, called the lear...
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11/02/2022
Unsupervised denoising for sparse multi-spectral computed tomography
Multi-energy computed tomography (CT) with photon counting detectors (PC...
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03/25/2022
FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation
Purpose: Real-time monitoring of cardiac output (CO) requires low latenc...
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02/15/2022
Phase-insensitive versus phase-sensitive ultrasound absorption tomography in the frequency domain
The sensitivity of phase-sensitive detectors, such as piezoelectric dete...
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11/18/2021
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Many interventional surgical procedures rely on medical imaging to visua...
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06/06/2021
Stochastic EM methods with Variance Reduction for Penalised PET Reconstructions
Expectation-maximization (EM) is a popular and well-established method f...
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11/17/2020
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown ou...
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07/20/2020
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Recent advances in reconstruction methods for inverse problems leverage ...
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05/28/2020
Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
A primary interest in dynamic inverse problems is to identify the underl...
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05/14/2020
On Learned Operator Correction
We discuss the possibility to learn a data-driven explicit model correct...
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12/22/2019
Rapid Whole-Heart CMR with Single Volume Super-resolution
Background: Three-dimensional, whole heart, balanced steady state free p...
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08/01/2019
Multi-Scale Learned Iterative Reconstruction
Model-based learned iterative reconstruction methods have recently been ...
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11/29/2018
Networks for Nonlinear Diffusion Problems in Imaging
A multitude of imaging and vision tasks have seen recently a major trans...
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10/18/2018
Expectation Propagation for Poisson Data
The Poisson distribution arises naturally when dealing with data involvi...
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07/09/2018
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
We present a framework for accelerated iterative reconstructions using a...
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06/11/2018
Statistics on functional data and covariance operators in linear inverse problems
We introduce a framework for the statistical analysis of functional data...
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03/14/2018
Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease
PURPOSE: Real-time assessment of ventricular volumes requires high accel...
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03/14/2018
Spatio-temporal Deep De-aliasing for Prospective Assessment of Real-time Ventricular Volumes
PURPOSE: Real-time assessment of ventricular volumes requires high accel...
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09/18/2017
Variational Gaussian Approximation for Poisson Data
The Poisson model is frequently employed to describe count data, but in ...
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08/31/2017
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have sh...
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06/22/2017
Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
Tissue oxygenation and perfusion can be an indicator for organ viability...
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05/19/2017
Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clini...
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07/11/2016
