Search Results for author:
Found 11 papers, 6 papers with code
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.
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.
Real-Time Tilt Undersampling Optimization during Electron Tomography of Beam Sensitive Samples using Golden Ratio Scanning and RECAST3D
Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples.
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.
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.
Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning
Therefore the datasets can be used for image reconstruction, segmentation, automatic defect detection, and testing the effects of (as well as applying new methodologies for removing) label bias in machine learning.
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.
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.
