CT dose reduction factors in the thousands using X-ray phase contrast

Phase-contrast X-ray imaging can improve the visibility of weakly absorbing objects (e.g. soft tissues) by an order of magnitude or more compared to conventional radiographs. Previously, it has been shown that combining phase retrieval with computed tomography (CT) can increase the signal-to-noise ratio (SNR) by up to two orders of magnitude over conventional CT at the same radiation dose, without loss of image quality. Our experiments reveal that as radiation dose decreases, the relative improvement in SNR increases. We discovered this enhancement can be traded for a reduction in dose greater than the square of the gain in SNR. Upon reducing the dose 300 fold, the phase-retrieved SNR was still almost 10 times larger than the absorption contrast data. This reveals the potential for dose reduction factors in the tens of thousands without loss in image quality, which would have a profound impact on medical and industrial imaging applications

FABRICATION, MEASUREMENTS, AND MODELING OF SEMICONDUCTOR RADIATION DETECTORS FOR IMAGING AND DETECTOR RESPONSE FUNCTIONS

In the first part of this dissertation, we cover the development of a diamond semiconductor alpha-tagging sensor for associated particle imaging to solve challenges with currently employed scintillators. The alpha-tagging sensor is a double-sided strip detector made from polycrystalline CVD diamond. The performance goals of the alpha-tagging sensor are 700-picosecond timing resolution and 0.5 mm spatial resolution. A literature review summarizes the methodology, goals, and challenges in associated particle imaging. The history and current state of alpha-tagging sensors, followed by the properties of diamond semiconductors are discussed to close the literature review. The materials and methods used to calibrate the detector readout, fabricate the sensor, perform simulations, take measurements, and conduct data analysis are discussed. The results of our simulations and measurements are described with challenges and interpretations. The first part of the dissertation is concluded with potential solutions to challenges with our diamond alpha-tagging sensor design, recommendations of work to help further verify or refute diamonds viability for alpha tagging in associated particle imaging. In the second part of this dissertation, we cover the development of a high-purity germanium detector response function for the Los Alamos National Laboratory Detector Response Function Toolkit. The goal is to accurately model the pulse-height spectra measured by semiconductor radiation detectors. The literature review provides information on high-purity germanium radiation detectors and semiconductor charge transport kinematics. The components of the electronic readout and their effect on radiation measurements are discussed. The literature review ends with a discussion on different methods for building detector response functions. In the methods section, we explain our methodology for building detector response functions. This includes models of radiation transport, electrostatics, charge transport, and electronic readout components. Within the methods section, there are results from individual components to demonstrate their functionality. The results section is reserved for demonstrating the use of the detector response function as a whole. We provide the modeled pulse-height spectra for different radiation sources and user input parameters. These are compared to experimentally measured datasets. The second part of the dissertation concludes with a discussion of the benefits, drawbacks, and future improvements that could be made

Edge Detection on Eddy Current Image to Increase Defect Characterization

As the nuclear yard ages, the control of steam generator tubes (SGT) must deal with new problems. In fact new defects appear, especially in the area of the tube sheet, of the tube support or at the U-bend area. Eddy current testing using Rotating Probe Coil (absolute mode) gives a better resolution. These measurements allow smaller defects to be detected along different orientations, especially defects along the circumference in the rolling transition. Signals collected during the exploration of the tube internal wall with this coil contain the useful information which is represented in the form of a cartography (or image) for each of the signal complex components

Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects

Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods have made huge leaps, they still confront a lot of challenges such as defect scale variation, complex background, low contrast, and so on. To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder-decoder network. JAFFNet mainly incorporates a joint attention-guided feature fusion (JAFF) module into decoding stages to adaptively fuse low-level and high-level features. The JAFF module learns to emphasize defect features and suppress background noise during feature fusion, which is beneficial for detecting low-contrast defects. In addition, JAFFNet introduces a dense receptive field (DRF) module following the encoder to capture features with rich context information, which helps detect defects of different scales. The JAFF module mainly utilizes a learned joint channel-spatial attention map provided by high-level semantic features to guide feature fusion. The attention map makes the model pay more attention to defect features. The DRF module utilizes a sequence of multi-receptive-field (MRF) units with each taking as inputs all the preceding MRF feature maps and the original input. The obtained DRF features capture rich context information with a large range of receptive fields. Extensive experiments conducted on SD-saliency-900, Magnetic tile, and DAGM 2007 indicate that our method achieves promising performance in comparison with other state-of-the-art methods. Meanwhile, our method reaches a real-time defect detection speed of 66 FPS