Registration-based multi-orientation tomography

We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and exp

Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%

CT-based data generation for foreign object detection on a single X-ray projection

Although X-ray imaging is used routinely in industry for high-throughput product quality control, its capability to detect internal defects has strong limitations. The main challenge stems from the superposition of multiple object features within a single X-ray view. Deep Convolutional neural networks can be trained by annotated datasets of X-ray images to detect foreign objects in real-time. However, this approach depends heavily on the availability of a large amount of data, strongly hampering the viability of industrial use with high variability between batches of products. We present a computationally efficient, CT-based approach for creating artificial single-view X-ray data based on just a few physically CT-scanned objects. By algorithmically modifying the CT-volume, a large variety of training examples is obtained. Our results show that applying the generative model to a single CT-scanned object results in image analysis accuracy that would otherwise be achieved with scans of tens of real-world samples. Our methodology leads to a strong reduction in training data needed, improved coverage of the combinations of base and foreign objects, and extensive generalizability to additional features. Once trained on just a single CT-scanned object, the resulting deep neural network can detect foreign objects in real-time with high accuracy

Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning

We present three parallel-beam tomographic datasets of 94 apples with internal defects along with defect label files. The datasets are prepared for development and testing of data-driven, learning-based image reconstruction, segmentation and post-processing methods. The three versions are a noiseless simulation; simulation with added Gaussian noise, and with scattering noise. The datasets are based on real 3D X-ray CT data and their subsequent volume reconstructions. The ground truth images, based on the volume reconstructions, are also available through this project. Apples contain various defects, which naturally introduce a label bias. We tackle this by formulating the bias as an optimization

Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed

vandriiashen /flexsim

This package can be used to reconstruct scanned objects, change the internal structure and reproject the augmented volume. The goal is to generate new X-ray projections similar but not identical to the acquired data

vandriiashen /aug_accuracy

This package contains a set of tools to train and test classification neural networks. The structure is inspired by msd_pytorch package. Data loader includes standard augmentation techniques implemented without transforms from torchvision. Model combines architectures from torchvision with a scaling layer for normalization. Tensorboard is used for logging

FOD CT Data: air pockets in avocado and stone in modelling clay

Summary: This submission contains X-ray CT data of avocado fruits and pieces of modelling clay containing pebble stones. Data for every object include binned pre-processed projections and volume segmentations. These datasets can be used for training and testing deep learning methods for foreign object detection.The data is made available as a part of the paper "CT-based data generation for foreign object detection on a single X-ray projection".</p

Development of the projection-based material decomposition algorithm for Multienergy CT

Current advances in the development of hybrid pixel detectors allow analyzing an energy spectrum of the incoming X-ray radiation with a keV resolution. This spectral information can be utilized to determine the material composition of the studied object based on the known material attenuation dependency on energy. The algorithm discussed and implemented in this article solves an optimization problem with a cost function based on Beer's Law. A successful application of this procedure to the real data requires an accurate model of the detector response. A Monte-Carlo simulation of the registration process in the Timepix3-based detector is performed for the generation of the detector signal corresponding to a monochromatic beam depending on the detector properties. The material decomposition algorithm is applied to simulated data with a response function similar to the real detector. The obtained results are in accordance with the expected values of material concentrations, and the variance mostly depends on a statistical error of the simulation and properties of the detector. An application of the implemented algorithm to experimental data is attempted but the results contain qualitative and quantitative errors. Shortcomings of the current implementation and possible improvements of the detector model and decomposition procedure are discussed

Apple CT Data: Simulated parallel-beam tomographic datasets

This submission is a supplementary material to the article [Coban 2020b]. As part of the manuscript, we release three simulated parallel-beam tomographic datasets of 94 apples with internal defects, the ground truth reconstructions and two defect label files