Search Results for author:
Found 17 papers, 6 papers with code
Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging
We acquired a data collection designed for benchmarking data-driven plane wave imaging approaches using a realistic breast mimicking phantom and an ultrasound calibration phantom.
Single-shot Tomography of Discrete Dynamic Objects
This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time.
Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning
The approach learns efficient non-greedy policies to solve a given class of OED problems through extensive offline training rather than solving a given OED problem directly via numerical optimization.
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks.
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography
The state-of-the-art approach for deformation estimation uses (semi-)manually labelled marker locations in projection data to fit the parameters of a polynomial deformation model.
Single Plane-Wave Imaging using Physics-Based Deep Learning
We compare our proposed data-to-image network with an image-to-image network in simulated data experiments, mimicking a medical ultrasound application.
Deep Learning for Multi-View Ultrasonic Image Fusion
These different modes give rise to multiple DAS images reflecting different geometric information about the scatterers and the challenge is to either fuse them into one image or to directly extract higher-level information regarding the materials of the medium, e. g., a segmentation map.
Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image versus Data Domain
We derive a one-to-one map between wavefront directions in image and data spaces in PAT which suggests near equivalence between the recovery of the initial pressure and PAT data from compressed/subsampled measurements when assuming sparsity in Curvelet frame.
Deep data compression for approximate ultrasonic image formation
This demonstrates the great potential of deep ultrasonic data compression tailored for a specific image formation method.
Fast ultrasonic imaging using end-to-end deep learning
Ultrasonic imaging algorithms used in many clinical and industrial applications consist of three steps: A data pre-processing, an image formation and an image post-processing step.
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction.
A Multi-channel DART Algorithm
Discrete tomography is concerned with objects that consist of a small number of materials, which makes it possible to compute accurate reconstructions from highly limited projection data.
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.
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation
Further, we provide numerical examples that demonstrate the potential of our method for training deep neural networks.
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.
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.
