An improved experimental procedure of separating a composite thermoluminescence glow curve into its components

We present an improved experimental procedure of separating a composite thermoluminescence glow curve into its components. Careful monitoring of the isothermal cleaning process using the initial rise method ensures the complete thermal removal of TL peaks. Digital subtraction of two experimental TL glow curves yields individual experimental TL glow peaks. Several standard methods (initial rise and whole glow curve) are used to obtain the energy values and frequency factors of the traps. The method has been used successfully to analyze the well-known composite TL glow curve of the dosimetric material LiF (TLD-100). The limitations of the method are illustrated by analyzing the highly complex TL glow curve of a UV irradiated synthetic calcite consisting of at least 6 TL peaks. Although the method works best for TL glow curves described by first order kinetics, it should also be applicable to more general kinetics.     

Novel methodology for 3D reconstruction of carotid arteries and plaque characterization based upon magnetic resonance imaging carotid angiography data

In this study, we present a novel methodology that allows reliable segmentation of the magnetic resonance images (MRIs) for accurate fully automated three-dimensional (3D) reconstruction of the carotid arteries and semiautomated characterization of plaque type. Our approach uses active contours to detect the luminal borders in the time-of-flight images and the outer vessel wall borders in the T(1)-weighted images. The methodology incorporates the connecting components theory for the automated identification of the bifurcation region and a knowledge-based algorithm for the accurate characterization of the plaque components. The proposed segmentation method was validated in randomly selected MRI frames analyzed offline by two expert observers. The interobserver variability of the method for the lumen and outer vessel wall was -1.60%±6.70% and 0.56%±6.28%, respectively, while the Williams Index for all metrics was close to unity. The methodology implemented to identify the composition of the plaque was also validated in 591 images acquired from 24 patients. The obtained Cohen's k was 0.68 (0.60-0.76) for lipid plaques, while the time needed to process an MRI sequence for 3D reconstruction was only 30 s. The obtained results indicate that the proposed methodology allows reliable and automated detection of the luminal and vessel wall borders and fast and accurate characterization of plaque type in carotid MRI sequences. These features render the currently presented methodology a useful tool in the clinical and research arena.     

Reconstruction of thermally quenched glow curves in quartz

The experimentally measured thermoluminescence (TL) glow curves of quartz samples are influenced by the presence of the thermal quenching effect, which involves a variation of the luminescence efficiency as a function of temperature. The real shape of the thermally unquenched TL glow curves is completely unknown. In the present work an attempt is made to reconstruct these unquenched glow curves from the quenched experimental data, and for two different types of quartz samples. The reconstruction is based on the values of the thermal quenching parameter W (activation energy) and C (a dimensionless constant), which are known from recent experimental work on these two samples. A computerized glow-curve deconvolution (CGCD) analysis was performed twice for both the reconstructed and the experimental TL glow curves. Special attention was paid to check for consistency between the results of these two independent CGCD analyses. The investigation showed that the reconstruction attempt was successful, and it is concluded that the analysis of reconstructed TL glow curves can provide improved values of the kinetic parameters E, s for the glow peaks of quartz. This also leads to a better evaluation of the half-lives of electron trapping levels used for dosimetry and luminescence dating.     

Explainable AI: A Review of Machine Learning Interpretability Methods

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.     

Multispectral imaging using multiple-bandpass filters

We present a method for multispectral imaging. This method uses color CCD cameras with a multiple-bandpass filter, which modifies the spectral response of the cameras used and enables concurrent acquisition of multiple images at defined spectral bands. We experimentally demonstrate methodological feasibility using two color CCD cameras and a polychroic mirror to simultaneously capture eight spectral bands. We discuss how the method developed is well suited for multispectral applications of transient phenomena or for real-time measurements.     

Complete-angle projection diffuse optical tomography by use of early photons

We present the first, to our knowledge, experimental images of complex-shaped phantoms embedded in diffuse media by use of optical tomography. Imaging is based on a complete-angle projection tomographic technique that utilizes transmitted early photons. Results are contrasted with measurements obtained at later gates as well as pseudocontinuous-wave data. The scanning system developed employs noncontact illumination and detection technologies that allow for high spatial sampling of transmitted photons. Combining this system with complete-angle illumination is found to be an important strategy toward improved imaging performance, resulting in a better-posed inversion problem. The appropriateness of reconstruction algorithms similar to those employed in x-ray computed tomography are showcased, and suggestions for model improvements are provided.     

Multispectral optoacoustic tomography by means of normalized spectral ratio

Quantification of biomarkers using multispectral optoacoustic tomography can be challenging due to photon fluence variations with depth and spatially heterogeneous tissue optical properties. Herein we introduce a spectral ratio approach that accounts for photon fluence variations. The performance and imaging improvement achieved with the proposed method is showcased both numerically and experimentally in phantoms and mice.