Search Results for author:
Found 24 papers, 6 papers with code
Plug-and-Play Half-Quadratic Splitting for Ptychography
Ptychography is a coherent diffraction imaging method that uses phase retrieval techniques to reconstruct complex-valued images.
Equidistribution-based training of Free Knot Splines and ReLU Neural Networks
More precisely, we combine the training of the ReLU NN with an equidistribution based loss to find the breakpoints of the ReLU functions, combined with preconditioning the ReLU NN approximation (to take an FKS form) to find the scalings of the ReLU functions, leads to a well-conditioned and reliable method of finding an accurate ReLU NN approximation to a target function.
Learning Governing Equations of Unobserved States in Dynamical Systems
In this work, we employ a hybrid neural ODE structure, where the system equations are governed by a combination of a neural network and domain-specific knowledge, together with symbolic regression (SR), to learn governing equations of partially-observed dynamical systems.
Investigating the use of publicly available natural videos to learn Dynamic MR image reconstruction
Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K).
Inverse Problems with Learned Forward Operators
The first one is completely agnostic to the forward operator and learns its restriction to the subspace spanned by the training data.
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction
Denoising diffusion models have emerged as the go-to generative framework for solving inverse problems in imaging.
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
A Learned Born Series for Highly-Scattering Media
A new method for solving the wave equation is presented, called the learned Born series (LBS), which is derived from a convergent Born Series but its components are found through training.
Unsupervised denoising for sparse multi-spectral computed tomography
However, these issues are especially exacerbated when sparse imaging scenarios are encountered due to a significant reduction in photon counts.}
FReSCO: Flow Reconstruction and Segmentation for low latency Cardiac Output monitoring using deep artifact suppression and segmentation
Conclusion: FReSCO was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.
Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data
Many interventional surgical procedures rely on medical imaging to visualise and track instruments.
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction.
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e. g., deep neural networks.
On Learned Operator Correction in Inverse Problems
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to obtain regularised reconstructions.
Rapid Whole-Heart CMR with Single Volume Super-resolution
Conclusion: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting.
Multi-Scale Learned Iterative Reconstruction
Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models.
Networks for Nonlinear Diffusion Problems in Imaging
By design, we obtain a nonlinear network architecture that is well suited for diffusion related problems in imaging.
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography.
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN.
Variational Gaussian Approximation for Poisson Data
In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior.
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up.
Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition
Tissue oxygenation and perfusion can be an indicator for organ viability during minimally invasive surgery, for example allowing real-time assessment of tissue perfusion and oxygen saturation.
Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.
Inference of Haemoglobin Concentration From Stereo RGB
We demonstrate by using the co-registered stereo image data from two cameras it is possible to get robust SO2 estimation as well.
