Search Results for author:
Found 20 papers, 10 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.
X-ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study
We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.
Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders
Our workflow allows collecting large datasets of accurately aligned photo-CT image pairs, which can help distinguish internal disorders with a similar appearance on CT. With slight modifications, a similar workflow can be applied to other fruits or MRI instead of CT scans.
Maximum-likelihood estimation in ptychography in the presence of Poisson-Gaussian noise statistics
Optical measurements often exhibit mixed Poisson-Gaussian noise statistics, which hampers image quality, particularly under low signal-to-noise ratio (SNR) conditions.
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.
Quantifying the effect of X-ray scattering for data generation in real-time defect detection
X-ray scattering is known to be computationally expensive to simulate, and this effect can heavily influence the accuracy of a generated X-ray image.
Amortized Normalizing Flows for Transcranial Ultrasound with Uncertainty Quantification
Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image.
Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast
Motion artifacts often spoil the radiological interpretation of MR images, and in the most severe cases the scan needs be repeated, with additional costs for the provider.
A tomographic workflow to enable deep learning for X-ray based foreign object detection
In this work, we propose a Computed Tomography (CT) based method for producing training data for supervised learning of foreign object detection, with minimal labour requirements.
ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography
However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem.
Photoacoustic imaging with conditional priors from normalizing flows
For many ill-posed inverse problems, such as photoacoustic imaging, the uncertainty of the solution is highly affected by measurement noise and data incompleteness (due, for example, to limited aperture).
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.
Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry
A novel thickness correction model is introduced as a pre-processing technique for DEXA data.
CoShaRP: A Convex Program for Single-shot Tomographic Shape Sensing
We introduce single-shot X-ray tomography that aims to estimate the target image from a single cone-beam projection measurement.
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.
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.
A Convex Formulation for Binary Tomography
It is a relaxation in the sense that it can only be guaranteed to give a feasible solution; not necessarily the optimal one.
Non-smooth Variable Projection
Variable projection solves structured optimization problems by completely minimizing over a subset of the variables while iterating over the remaining variables.
