Non-unique water and contrast agent solutions in dual-energy CT.

The goal of this work is to study occurrences of non-unique solutions in dual-energy CT (DECT) for objects containing water and a contrast agent. Previous studies of the Jacobian of nonlinear systems identified that a vanishing Jacobian determinant indicates the existence of multiple solutions to the system. Vanishing Jacobian determinants are identified for DECT setups by simulating intensity data for practical thickness ranges of water and contrast agent.

A working memory model based on recurrent neural networks using reinforcement learning.

Numerous electrophysiological experiments have reported that the prefrontal cortex (PFC) is involved in the process of working memory. PFC neurons continue firing to maintain stimulus information in the delay period without external stimuli in working memory tasks. Further findings indicate that while the activity of single neurons exhibits strong temporal and spatial dynamics (heterogeneity), the activity of population neurons can encode spatiotemporal information of stimuli stably and reliably.

[Impact of fine particulate matter exposure on non-accidental mortality under different apparent temperature levels].

Objective: To assess the impact of exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM) on non-accidental mortality under different apparent temperature levels and to further explore the modification effect of apparent temperature.

Methods: This study used time-series design.

A directional total variation minimization algorithm for isotropic resolution in digital breast tomosynthesis.

An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity constraints. Iodinated contrast agent (ICA) imaging is performed by reconstructing images from dual-energy DBT data followed by weighted subtraction.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

Background: Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality.

Purpose: The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose.

Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function.

Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are prone to wipe out small, low-contrast features that are critical for screening and diagnosis. To remedy this issue, we introduce a novel training loss inspired by the model observer framework to enhance the detectability of weak signals in the reconstructions.

Simultaneous Activity and Attenuation Estimation in TOF-PET With TV-Constrained Nonconvex Optimization.

An alternating direction method of multipliers (ADMM) framework is developed for nonsmooth biconvex optimization for inverse problems in imaging. In particular, the simultaneous estimation of activity and attenuation (SAA) problem in time-of-flight positron emission tomography (TOF-PET) has such a structure when maximum likelihood estimation (MLE) is employed. The ADMM framework is applied to MLE for SAA in TOF-PET, resulting in the ADMM-SAA algorithm.

Accurate Reconstruction of Multiple Basis Images Directly From Dual Energy CT Data.

Objective: We develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT.

Methods: In medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array.

Prototyping optimization-based image reconstructions from limited-angular-range data in dual-energy CT.

Image reconstruction from data collected over full-angular range (FAR) in dual-energy CT (DECT) is well-studied. There exists interest in DECT with advanced scan configurations in which data are collected only over limited-angular ranges (LARs) for meeting unique workflow needs in certain practical imaging applications, and thus in the algorithm development for image reconstruction from such LAR data. The objective of the work is to investigate and prototype image reconstructions in DECT with LAR scans.

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.

Background: Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data.

4D-image reconstruction directly from limited-angular-range data in continuous-wave electron paramagnetic resonance imaging.

Objective: We investigate and develop optimization-based algorithms for accurate reconstruction of four-dimensional (4D)-spectral-spatial (SS) images directly from data collected over limited angular ranges (LARs) in continuous-wave (CW) electron paramagnetic resonance imaging (EPRI).

Methods: Basing on a discrete-to-discrete data model devised in CW EPRI employing the Zeeman-modulation (ZM) scheme for data acquisition, we first formulate the image reconstruction problem as a convex, constrained optimization program that includes a data fidelity term and also constraints on the individual directional total variations (DTVs) of the 4D-SS image. Subsequently, we develop a primal-dual-based DTV algorithm, simply referred to as the DTV algorithm, to solve the constrained optimization program for achieving image reconstruction from data collected in LAR scans in CW-ZM EPRI.

Simultaneous activity and attenuation estimation in TOF-PET with TV-constrained nonconvex optimization.

An alternating direction method of multipliers (ADMM) framework is developed for nonsmooth biconvex optimization for inverse problems in imaging. In particular, the simultaneous estimation of activity and attenuation (SAA) problem in time-of-flight positron emission tomography (TOF-PET) has such a structure when maximum likelihood estimation (MLE) is employed. The ADMM framework is applied to MLE for SAA in TOF-PET, resulting in the ADMM-SAA algorithm.

Report on the AAPM deep-learning spectral CT Grand Challenge.

Background: This Special Report summarizes the 2022 AAPM Grand Challenge on Deep-Learning spectral Computed Tomography (DL-spectral CT) image reconstruction.

Purpose: The purpose of the challenge is to develop the most accurate image reconstruction algorithm possible for solving the inverse problem associated with a fast kilovolt switching dual-energy CT scan using a three tissue-map decomposition. Participants could choose to use a deep-learning (DL), iterative, or a hybrid approach.

Long-Term Apparent Temperature, Extreme Temperature Exposure, and Depressive Symptoms: A Longitudinal Study in China.

Temperature is increasingly understood to impact mental health. However, evidence of the long-term effect of temperature exposure on the risk of depressive symptoms is still scarce. Based on the China Health and Retirement Longitudinal Study (CHARLS), this study estimated associations between long-term apparent temperature, extreme temperature, and depressive symptoms in middle-aged and older adults.

Thin-Film Platinum Resistance Temperature Detector with a SiCN/Yttria-Stabilized Zirconia Protective Layer by Direct Ink Writing for High-Temperature Applications.

In situ temperature monitoring of curved high-temperature components in extreme environments is challenging for a variety of applications in fields such as aero engines and gas turbines. Recently, extrusion-based direct ink writing (DIW) has been utilized to fabricate platinum (Pt) resistance temperature detectors (RTDs). However, the current Pt RTD prepared by DIW technology suffers from a limited temperature range and poor high-temperature stability.

Accurate Image Reconstruction in Dual-Energy CT with Limited-Angular-Range Data Using a Two-Step Method.

Dual-energy CT (DECT) with scans over limited-angular ranges (LARs) may allow reductions in scan time and radiation dose and avoidance of possible collision between the moving parts of a scanner and the imaged object. The beam-hardening (BH) and LAR effects are two sources of image artifacts in DECT with LAR data. In this work, we investigate a two-step method to correct for both BH and LAR artifacts in order to yield accurate image reconstruction in DECT with LAR data.

Abnormal Graphitization Behavior in Near-Surface/Interface Region of Polymer-Derived Ceramics.

The in situ free carbon generated in polymer-derived ceramics (PDCs) plays a crucial role in their unique microstructure and resultant properties. This study advances a new phenomenon of graphitization of PDCs. Specifically, whether in micro-/nanoscale films or millimeter-scale bulks, the surface/interface radically changes the fate of carbon and the evolution of PDC nanodomains, promotes the graphitization of carbon, and evolves a free carbon enriched layer in the near-surface/interface region.

Category learning in a recurrent neural network with reinforcement learning.

It is known that humans and animals can learn and utilize category information quickly and efficiently to adapt to changing environments, and several brain areas are involved in learning and encoding category information. However, it is unclear that how the brain system learns and forms categorical representations from the view of neural circuits. In order to investigate this issue from the network level, we combine a recurrent neural network with reinforcement learning to construct a deep reinforcement learning model to demonstrate how the category is learned and represented in the network.

An Extended Primal-Dual Algorithm Framework for Nonconvex Problems: Application to Image Reconstruction in Spectral CT.

Using the convexity of each component of the forward operator, we propose an extended primal-dual algorithm framework for solving a kind of nonconvex and probably nonsmooth optimization problems in spectral CT image reconstruction. Following the proposed algorithm framework, we present six different iterative schemes or algorithms, and then establish the relationship to some existing algorithms. Under appropriate conditions, we prove the convergence of these schemes for the general case.

Additive-Manufactured Platinum Thin-Film Strain Gauges for Structural Microstrain Testing at Elevated Temperatures.

This paper investigates the feasibility and performance of the fabrication of platinum high-temperature thin-film strain sensors on nickel-based alloy substrates by additive manufacturing. The insulating layer was made of a dielectric paste by screen printing process. A 1.

ZrB/SiCN Thin-Film Strain Gauges for In-Situ Strain Detection of Hot Components.

The in-situ strain/stress detection of hot components in harsh environments remains a challenging task. In this study, ZrB/SiCN thin-film strain gauges were fabricated on alumina substrates by direct writing. The effects of ZrB content on the electrical conductivity and strain sensitivity of ZrB/SiCN composites were investigated, and based on these, thin film strain gauges with high electrical conductivity (1.

A SiCN Thin Film Thermistor Based on DVB Modified Polymer-Derived Ceramics.

Carbon-rich SiCN ceramics were prepared by divinylbenzene (DVB)-modified polysilazane (PSN2), and a high-conductivity SiCN thin film sensor suitable for medium-low temperature sensing was fabricated. The modified liquid precursors were patterned by direct ink writing to produce SiCN resistive grids with line widths of several hundreds of micrometers and thicknesses of several micrometers. The introduction of DVB not only increases the critical thickness of SiCN ceramics several times, but also significantly improves the conductivity of SiCN, making it meet the conductivity requirements of sensing applications in the mid-low temperature range.

The establishment of Air Quality Health Index in China: A comparative analysis of methodological approaches.

Background: The Air Quality Index (AQI) has been criticized because it does not adequately account for the health effect of multi-pollutants. Although the developed Air Quality Health Index (AQHI) is a more effective communication tool, little is known about the best method to construct AQHI on long time and large spatial scales.

Objectives: To further evaluate the validity of existing approaches to the establishment of AQHI on both long time and larger spatial scales.