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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. Methods: The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms‐to‐DECT measurements map. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize the CNR. Results: It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non‐uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non‐uniqueness can lead to singular solutions during the inversion of sinograms‐to‐DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x‐ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53 keV. Conclusions: Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposed framework. [ABSTRACT FROM AUTHOR]

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. Methods: The challenge is based on a 2D breast CT simulation, where the simulated breast phantom consists of three tissue maps: adipose, fibroglandular, and calcification distributions. The phantom specification is stochastic so that multiple realizations can be generated for DL approaches. A dual‐energy scan is simulated where the x‐ray source potential of successive views alternates between 50 and 80 kilovolts (kV). A total of 512 views are generated, yielding 256 views for each source voltage. We generate 50 and 80 kV images by use of filtered back‐projection (FBP) on negative logarithm processed transmission data. For participants who develop a DL approach, 1000 cases are available. Each case consists of the three 512 × 512 tissue maps, 50 and 80‐kV transmission data sets and their corresponding FBP images. The goal of the DL network would then be to predict the material maps from either the transmission data, FBP images, or a combination of the two. For participants developing a physics‐based approach, all of the required modeling parameters are made available: geometry, spectra, and tissue attenuation curves. The provided information also allows for hybrid approaches where physics is exploited as well as information about the scanned object derived from the 1000 training cases. Final testing is performed by computation of root‐mean‐square error (RMSE) for predictions on the tissue maps from 100 new cases. Results: Test phase submission were received from 18 research groups. Of the 18 submissions, 17 were results obtained with algorithms that involved DL. Only the second place finishing team developed a physics‐based image reconstruction algorithm. Both the winning and second place teams had highly accurate results where the RMSE was nearly zero to single floating point precision. Results from the top 10 also achieved a high degree of accuracy; and as a result, this special report outlines the methodology developed by each of these groups. Conclusions: The DL‐spectral CT challenge successfully established a forum for developing image reconstruction algorithms that address an important inverse problem relevant for spectral CT. [ABSTRACT FROM AUTHOR]

Temperature‐independent properties are critical for high‐temperature thin‐film strain gauges (TFSGs). In this study, by controlling the electron scattering and tunneling effects in the TiB2/SiCN composites, the environmental interference of temperature fluctuations is successfully eliminated, and a temperature‐independent TFSG is fabricated. The effects of pyrolysis temperature and TiB2 content on the microstructural evolution and electrical properties of the ceramic films are studied. The temperature insensitivity is mainly attributed to the balance between the intrasheet resistance with a positive temperature coefficient of resistance (TCR) and the intersheet resistance with a negative TCR. This composite shows nearly constant resistance values over an ultrawide temperature range of 300–700 °C, with less than 0.05% deviation of the normalized resistance and TCR values as low as 1.6 ppm °C−1. In addition, the TiB2/SiCN films exhibited stable piezoresistive responses, with a gauge factor of 4.28, and the temperature‐independent strain response in the high‐temperature range is verified. [ABSTRACT FROM AUTHOR]

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. Purpose: This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon‐counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large‐scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects. Methods: Head CT data were acquired on an early prototype clinical PCCT system using an edge‐on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two‐step projection‐domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two‐step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft‐tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two‐step methods for a range of regularization constraint settings. Results: cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin, when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft‐tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values. Conclusions: The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large‐scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two‐step approach. [ABSTRACT FROM AUTHOR]

The vein–membrane structure inspired by insect wings is applied to the artificial structural design of materials, with the aim of enhancing the intrinsic low strain tolerance and bonding strength of Al2O3 coating. Al2O3 platelets with high aspect ratio are used as veins, forming a bioinspired vein–membrane structure along with the Al2O3 membrane. Further, Al2O3 platelets take "root" in the substrate, forming a bioinspired root–soil interlocking structure. The greatest highlight of this coating is that it can restrict the cracks to the submicron area enclosed by the vein. The multi‐bioinspired structure enabled the Al2O3 coating to sustain strains as high as 8%, which is 2 orders of magnitude higher than the reported failure strain of Al2O3 film. The bonding strength is also increased by at least five times. The vein–membrane structure opens a novel technological space for the artificial structural design of materials. [ABSTRACT FROM AUTHOR]

The thermal stability at high temperatures is the first design principle of high‐temperature thin‐film sensors. To improve the thermal stability of the thin‐film sensor, a TiB2‐modified SiCN double‐layer thin‐film resistor grid with a thickness of 18 μm is fabricated on an alumina substrate via direct writing. Owing to their in situ–generated SiO2–B2O3–TiO2 thermally grown oxide protective layer on the surface and TiB2 percolation paths inside, the produced films exhibit large high‐temperature oxidation resistance, electrical conductivity and high‐temperature‐resistance stability. In the thermal cycling test up to 800 °C, the TiB2‐modified SiCN films exhibit a negative‐temperature coefficient of resistance and excellent repeatability and stability. The resistance change rate at 800 °C for 1 h is only 1.1%. With the flexible manufacturing and high‐temperature stability of such ceramic films, this work on double‐layer ceramic films can be applied to in situ monitoring of hot components at high temperature. [ABSTRACT FROM AUTHOR]

Background: Calibration of photon‐counting detectors (PCDs) is necessary for quantitatively accurate spectral computed tomography (CT), but the calibration process can be complicated by nonlinear flux‐dependent physical factors such as pulse pile‐up. Purpose: This work develops a method for spectral sensitivity calibration of a PCD‐based spectral CT system that incorporates nonlinear flux dependence and can thus be employed at high photon flux. Methods: A calibration model for the spectral response and polynomial flux dependence is proposed, which incorporates prior x‐ray source spectrum and PCD models and that has a small set of parameters for adjusting to the spectral CT system of interest. The model parameters are determined by fitting transmission data from a known object of known composition: a step‐wedge phantom composed of different thicknesses of aluminum, a bone equivalent, and polymethyl methacrylate (PMMA), a soft‐tissue equivalent. This fitting employs Tikhonov regularization, and the regularization strength and the polynomial order for the intensity modeling are determined by bias and variance analysis. The spectral calibration and nonlinear intensity correction is validated on transmission measurements through a third material, Teflon, at different x‐ray photon flux levels. Results: The nonlinear intensity dependence is determined to be accurately accounted for with a third‐order polynomial. The calibrated spectral CT model accurately predicts Teflon transmission to within 1% for flux levels up to 50% of the detector maximum. Conclusions: The proposed PCD calibration method enables accurate physical modeling necessary for quantitative imaging in spectral CT. Furthermore, the model applies to high flux settings so that acquisition times will not be limited by restricting the spectral CT system to low flux levels. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the challenge is to find the deep‐learning (DL) technique for sparse‐view computed tomography (CT) image reconstruction that can yield the minimum root mean square error (RMSE) under ideal conditions, thereby addressing the question of whether or not DL can solve inverse problems in imaging. Methods: The challenge setup involves a 2D breast CT simulation, where the simulated breast phantom has random fibro‐glandular structure and high‐contrast specks. The phantom allows for arbitrarily large training sets to be generated with perfectly known truth. The training set consists of 4000 cases where each case consists of the truth image, 128‐view sinogram data, and the corresponding 128‐view filtered back‐projection (FBP) image. The networks are trained to predict the truth image from either the sinogram or FBP data. Geometry information is not provided. The participating algorithms are tested on a data set consisting of 100 new cases. Results: About 60 groups participated in the challenge at the validation phase, and 25 groups submitted test‐phase results along with reports on their DL methodology. The winning team improved reconstruction accuracy by two orders of magnitude over our previous convolutional neural network (CNN)‐based study on a similar test problem. Conclusions: The DL‐sparse‐view challenge provides a unique opportunity to examine the state‐of‐the‐art in DL techniques for solving the sparse‐view CT inverse problem. [ABSTRACT FROM AUTHOR]

Purpose: The constrained one‐step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon‐counting CT simulations. Methods: cOSSCIR directly estimates basis material maps from photon‐counting data using a physics‐based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon‐counting CT acquisitions of a virtual pelvic phantom with low‐contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two‐step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least‐squares optimization (MLE+TVmin$_{\text{min}}$). Images were also compared to a nonspectral TVmin$_{\text{min}}$ reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft‐tissue texture, while reducing metal artifacts, was quantitatively evaluated. Results: Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TVmin$_{\text{min}}$ algorithms to the correct basis maps in the presence of beam‐hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of −1 HU, compared to 2 HU error for the MLE+TVmin$_{\text{min}}$ algorithm and −154 HU error for the nonspectral TVmin$_{\text{min}}$+NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TVmin$_{\text{min}}$ algorithm, 41 HU for the MLE+TVmin$_{\text{min}}$+Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TVmin$_{\text{min}}$+NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft‐tissue texture when an appropriate regularization constraint value was selected. Conclusions: By directly inverting photon‐counting CT data into basis maps using an accurate physics‐based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two‐step method of decomposition followed by reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: Computed tomography (CT) scanning over limited‐angular ranges (LARs) is of practical interest in possible reduction of imaging dose and time and in design of nonstandard scans. This work aims to investigate image reconstruction for two nonoverlapping arcs of LARs, and to demonstrate that they may allow more accurate image reconstruction than may a single arc of LAR. Methods: We consider a configuration with two nonoverlapping arcs of LARs α1$\alpha _1$ and α2$\alpha _2$, whose centers are separated by 90∘$90^\circ$, and refer to it as a two‐orthogonal‐arc configuration. Data are generated from a chest phantom with two‐orthogonal‐arc configurations over total angular coverage ατ=α1+α2$\alpha _\tau =\alpha _1+\alpha _2$ ranging from 18∘$18^\circ$ to 180∘$180^\circ$, and images are reconstructed subsequently by use of the directional‐total‐variation (DTV) algorithm. For comparison, we also consider image reconstruction for a single‐arc configuration of angular range ατ$\alpha _\tau$. Quantitative metrics such as the normalized root‐mean‐square‐error (nRMSE) are used for evaluation of image reconstruction accuracy. Results: Visual inspection and quantitative analysis of images reconstructed reveal that a two‐orthogonal‐arc configuration generally yields more accurate image reconstruction than does its single‐arc counterpart. As total angular range ατ$\alpha _\tau$ increases, the DTV algorithm yields image reconstruction with enhanced accuracy, as expected. Also, if ατ$\alpha _\tau$ remains constant, the two‐orthogonal‐arc configuration with α1=α2$\alpha _1 = \alpha _2$ generally leads to image reconstruction more accurate than those of two‐orthogonal‐arc configurations with α1≠α2$\alpha _1 \ne \alpha _2$, as the nRMSE of the former can be lower than that of the latter for up to more than one order of magnitude. Conclusions: Appropriately designed two‐orthogonal‐arc configurations may be exploited for improving image‐reconstruction accuracy in CT imaging with reduced angular coverage. This study may yield insights into the design of innovative CT scans for lowering scan time and radiation dose, and/or for avoiding scan collision in, for example, C‐arm CT. [ABSTRACT FROM AUTHOR]

Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of metrics employed for evaluating nonlinear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to overregularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. The metric is demonstrated in the context of a breast CT simulation, where different equal‐dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a nonlinear algorithm based on total variation constrained least‐squares (TV‐LSQ). The resulting images are studied as a function of three parameters: number of views acquired, total variation constraint value, and number of iterations. The images are evaluated visually, with image RMSE, and with the proposed signal‐detection‐based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to overregularization with TV‐LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over‐regularized images. The proposed signal detection‐based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when nonlinear image reconstruction is used. [ABSTRACT FROM AUTHOR]

Purpose: We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x‐ray spectrum that can accurately model the x‐ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. Methods: Spectrum estimation is posed as an optimization problem with x‐ray spectrum as unknown variables, and a Kullback–Leibler (KL)‐divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated‐gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation–maximization (EM) method is also discussed. Results: In simulations, the proposed algorithm is seen to yield x‐ray spectra that closely match the ground truth and represent the attenuation process of x‐ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization‐based approach and EM. Conclusions: Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL‐divergence constraint can include a prior spectrum and appears to capture important features of x‐ray spectrum, allowing accurate and robust estimation of x‐ray spectrum in CT imaging. [ABSTRACT FROM AUTHOR]

Purpose: We seek to investigate an optimization‐based one‐step method for image reconstruction that explicitly compensates for nonlinear spectral response (i.e., the beam‐hardening effect) in dual‐energy CT, to investigate the feasibility of the one‐step method for enabling two dual‐energy partial‐angular‐scan configurations, referred to as the short‐ and half‐scan configurations, on standard CT scanners without involving additional hardware, and to investigate the potential of the short‐ and half‐scan configurations in reducing imaging dose and scan time in a single‐kVp‐switch full‐scan configuration in which two full rotations are made for collection of dual‐energy data. Methods: We use the one‐step method to reconstruct images directly from dual‐energy data through solving a nonconvex optimization program that specifies the images to be reconstructed in dual‐energy CT. Dual‐energy full‐scan data are generated from numerical phantoms and collected from physical phantoms with the standard single‐kVp‐switch full‐scan configuration, whereas dual‐energy short‐ and half‐scan data are extracted from the corresponding full‐scan data. Besides visual inspection and profile‐plot comparison, the reconstructed images are analyzed also in quantitative studies based upon tasks of linear‐attenuation‐coefficient and material‐concentration estimation and of material differentiation. Results: Following the performance of a computer‐simulation study to verify that the one‐step method can reconstruct numerically accurately basis and monochromatic images of numerical phantoms, we reconstruct basis and monochromatic images by using the one‐step method from real data of physical phantoms collected with the full‐, short‐, and half‐scan configurations. Subjective inspection based upon visualization and profile‐plot comparison reveals that monochromatic images, which are used often in practical applications, reconstructed from the full‐, short‐, and half‐scan data are largely visually comparable except for some differences in texture details. Moreover, quantitative studies based upon tasks of linear‐attenuation‐coefficient and material‐concentration estimation and of material differentiation indicate that the short‐ and half‐scan configurations yield results in close agreement with the ground‐truth information and that of the full‐scan configuration. Conclusions: The one‐step method considered can compensate effectively for the nonlinear spectral response in full‐ and partial‐angular‐scan dual‐energy CT. It can be exploited for enabling partial‐angular‐scan configurations on standard CT scanner without involving additional hardware. Visual inspection and quantitative studies reveal that, with the one‐step method, partial‐angular‐scan configurations considered can perform at a level comparable to that of the full‐scan configuration, thus suggesting the potential of the two partial‐angular‐scan configurations in reducing imaging dose and scan time in the standard single‐kVp‐switch full‐scan CT in which two full rotations are performed. The work also yields insights into the investigation and design of other nonstandard scan configurations of potential practical significance in dual‐energy CT. [ABSTRACT FROM AUTHOR]

Purpose Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT. Methods Three metrics based on a 2D DBT simulation are investigated: (1) a root-mean-square-error ( RMSE) between the test phantom and reconstructed image, (2) a gradient RMSE where the comparison is made after taking a spatial gradient of both image and phantom, and (3) a region-of-interest ( ROI) Hotelling observer ( HO) for signal-known-exactly/background-known-exactly ( SKE/ BKE) and signal-known-exactly/background-known-statistically ( SKE/ BKS) detection tasks. Two simulation studies are performed using the aforementioned metrics, varying voxel aspect ratio, and regularization strength for two types of Tikhonov-regularized least-squares optimization. The RMSE metrics are applied to a 2D test phantom with resolution bar patterns at varying angles, and the ROI- HO metric is applied to two tasks relevant to DBT: lesion detection, modeled by use of a large, low-contrast signal, and microcalcification detection, modeled by use of a small, high-contrast signal. The RMSE metric trends are compared with visual assessment of the reconstructed bar-pattern phantom. The ROI- HO metric trends are compared with 3D reconstructed images from ACR phantom data acquired with a Hologic Selenia Dimensions DBT system. Results Sensitivity of the image RMSE to mean pixel value is found to limit its applicability to the assessment of DBT image reconstruction. The image gradient RMSE is insensitive to mean pixel value and appears to track better with subjective visualization of the reconstructed bar-pattern phantom. The ROI- HO metric shows an increasing trend with regularization strength for both forms of Tikhonov-regularized least-squares; however, this metric saturates at intermediate regularization strength indicating a point of diminishing returns for signal detection. Visualization with the reconstructed ACR phantom images appear to show a similar dependence with regularization strength. Conclusions From the limited studies presented it appears that image gradient RMSE trends correspond with visual assessment better than image RMSE for DBT image reconstruction. The ROI- HO metric for both detection tasks also appears to reflect visual trends in the ACR phantom reconstructions as a function of regularization strength. We point out, however, that the true utility of these metrics can only be assessed after amassing more data. [ABSTRACT FROM AUTHOR]

Purpose: The authors develop and investigate iterative image reconstruction algorithms based on data-discrepancy minimization with a total-variation (TV) constraint. The various algorithms are derived with different data-discrepancy measures reflecting the maximum likelihood (ML) principle. Simulations demonstrate the iterative algorithms and the resulting image statistical properties for low-dose CT data acquired with sparse projection view angle sampling. Of particular interest is to quantify improvement of image statistical properties by use of the ML data fidelity term. Methods: An incremental algorithm framework is developed for this purpose. The instances of the incremental algorithms are derived for solving optimization problems including a data fidelity objective function combined with a constraint on the image TV. For the data fidelity term the authors, compare application of the maximum likelihood principle, in the form of weighted least-squares (WLSQ) and Poisson-likelihood (PL), with the use of unweighted least-squares (LSQ). Results: The incremental algorithms are applied to projection data generated by a simulation modeling the breast computed tomography (bCT) imaging application. The only source of data inconsistency in the bCT projections is due to noise, and a Poisson distribution is assumed for the transmitted x-ray photon intensity. In the simulations involving the incremental algorithms an ensemble of images, reconstructed from 1000 noise realizations of the x-ray transmission data, is used to estimate the image statistical properties. The WLSQ and PL incremental algorithms are seen to reduce image variance as compared to that of LSQ without sacrificing image bias. The difference is also seen at few iterations--short of numerical convergence of the corresponding optimization problems. Conclusions: The proposed incremental algorithms prove effective and efficient for iterative image reconstruction in low-dose CT applications particularly with sparse-view projection data. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work is to develop and demonstrate a set of practical metrics for CT systems optimization. These metrics, based on the Hotelling observer (HO) figure of merit, are task-based. The authors therefore take the specific example of optimizing a dedicated breast CT system, including the reconstruction algorithm, for two relevant tasks, signal detection and Rayleigh discrimination. Methods: A dedicated breast CT system is simulated using specifications in the literature from an existing prototype. The authors optimize configuration and image reconstruction algorithm parameters for two tasks: the detection of simulated microcalcifications and the discrimination of two adjacent, high-contrast signals, known as the Rayleigh discrimination task. The effects on task performance of breast diameter, signal location, image grid size, projection view number, and reconstruction filter were all investigated. Two HO metrics were evaluated: the percentage of correct decisions in a two-alternative forced choice experiment (equivalent to area under the ROC curve or AUC), and the HO efficiency, defined as the squared ratio of HO signal-to-noise ratio (SNR) in the reconstructed image to HO SNR in the projection data. Results: The ease and efficiency of the HO metric computation allows a rapid high-resolution survey of many system parameters. Optimization of a range of system parameters using the HO results in images that subjectively appear optimal for the tasks investigated. Further, the results of assessment through the HO reproduce closely many existing results in the literature regarding the impact of parameter selection on image quality. Conclusions: This study demonstrates the utility of a task-based approach to system design, evaluation, and optimization. The methodology presented is equally applicable to determining the impact of a wide range of factors, including patient parameters, system and acquisition design, and the reconstruction algorithm. The results demonstrate the versatility of the proposed HO formalism by not only generating a set of parameters that are optimal for a given task but also by qualitatively reproducing many existing results from the breast CT literature. Meanwhile, the implementation of the proposed methodology is straightforward and entirely simulation-based. This is an attractive feature for many system optimization problems, where the goal is to analyze the individual system components such as the image reconstruction algorithm. Final assessment of the system as a whole should be based also on real data studies. [ABSTRACT FROM AUTHOR]

Categorization is a function of the brain that serves to group together items and events in our environments. Here we review the following important issues related to category representation and generalization: namely, where categories are presented in the brain, and how the brain utilizes categorical membership to generate new information. Accumulated experimental evidence shows that the prefrontal cortex (PFC) plays a critical role in category formation and generalization. We propose that prefrontal neurons abstract the commonality beyond individual stimuli, and categorize these based on their common meaning by ignoring their physical properties and learning to represent the boundaries between behaviorally significant categories. We also claim that a subgroup of prefrontal neurons simultaneously receives the category-related information and specific property information (e.g. reward) associated with an exemplar, to form a category-based representation of that property, and propagates it among stimuli of the same category, possibly reflecting a neural basis for category generalization in the PFC. These results suggest that the PFC is involved in representing abstract rules, and generating new information on the basis of previously acquired knowledge. [ABSTRACT FROM AUTHOR]

Purpose: The authors developed an iterative image-reconstruction algorithm for application to low-intensity computed tomography projection data, which is based on constrained, total-variation (TV) minimization. The algorithm design focuses on recovering structure on length scales comparable to a detector bin width. Methods: Recovering the resolution on the scale of a detector bin requires that pixel size be much smaller than the bin width. The resulting image array contains many more pixels than data, and this undersampling is overcome with a combination of Fourier upsampling of each projection and the use of constrained, TV minimization, as suggested by compressive sensing. The presented pseudocode for solving constrained, TV minimization is designed to yield an accurate solution to this optimization problem within 100 iterations. Results: The proposed image-reconstruction algorithm is applied to a low-intensity scan of a rabbit with a thin wire to test the resolution. The proposed algorithm is compared to filtered backprojection (FBP). Conclusions: The algorithm may have some advantage over FBP in that the resulting noise level is lowered at equivalent contrast levels of the wire. [ABSTRACT FROM AUTHOR]

Indoor air pollutants were monitored in 138 households in urban Xi'an that were recently decorated, and the concentrations of formaldehyde, total volatile organic compounds (TVOC), amines, benzene, methylbenzol, xylene, and radon were monitored in the bedrooms, sitting rooms, dinning rooms, kitchens, bathrooms, and studies of the houses. The most seriously elevated pollutants were TVOC and formaldehyde, whose rates exceeded standards by 69.8% and 60.8%, respectively. Ammonia was 13.2% above the standard, and xylene was 8.7% above the standard. The level of radon in 100% of the rooms was below the standard. Only for formaldehyde were there statistically significant differences in concentrations in the sitting room, bedroom, and study ( P < 0.05). Concentrations of TVOC, amines, and xylene were positively related to the concentration of formaldehyde in the same room. The concentrations of the major pollutants in summer were highest and had a statistically significant difference with those in other seasons. TVOC and formaldehyde were the most serious pollutants indoors after decorating in an urban area of Xi'an. The concentrations of formaldehyde in the sitting rooms were different than in other types of rooms. Formaldehyde can be a representative of the levels of pollutants indoors; rooms with a higher concentration of formaldehyde tend to have higher levels of other pollutants. In addition, pollutants in the decorated rooms have a strong positive dependence on the monitoring seasons. Summer was the peak time of indoor pollutant levels. Households should pay extra attention to ventilation and take other effective measures to avoid health problems caused by indoor air pollutants. [ABSTRACT FROM AUTHOR]

Helical scanning configuration has been used widely in diagnostic cone-beam computed tomography (CBCT) for acquiring data sufficient for exact image reconstruction over an extended volume. In image-guided radiation therapy (IGRT) and other applications of CBCT, it can be difficult, if not impossible, to implement mechanically a multiple-turn helical trajectory on the imaging systems due to hardware constraints. However, imaging systems in these applications often allow for the implementation of a reverse helical trajectory in which the rotation direction changes between two consecutive turns. Because the reverse helical trajectory satisfies Tuy’s condition, when projections of the imaged object are nontruncated, it yields data sufficient for exact image reconstruction within the reverse helix volume. The recently developed chord-based algorithms such as the backprojection filtration (BPF) algorithm can readily be applied to reconstructing images on chords of a reverse helical trajectory, and they can thus reconstruct an image within a volume covered by the chords. Conversely, the chord-based algorithms cannot reconstruct images within regions that are not intersected by chords. In a reverse helix volume, as shown below, chordless regions exist in which no images can thus be reconstructed by use of the chord-based algorithms. In this work, based upon Pack–Noo’s formula, a shift-invariant filtered backprojection (FBP) algorithm is derived for exact image reconstruction within the reverse helix volume, including the chordless region. Numerical studies have also been conducted to demonstrate the chordless region in a reverse helix volume and to validate the FBP algorithm for image reconstruction within the chordless region. Results of the numerical studies confirm that the FBP algorithm can exactly reconstruct an image within the entire reverse helix volume, including the chordless region. It is relatively straightforward to extend the FBP algorithm to reconstruct images for general trajectories, including reverse helical trajectories with variable pitch, tilted axis, and/or additional segments between turns. [ABSTRACT FROM AUTHOR]

In coronary calcium scoring, motion artifacts affecting calcified plaques are commonly characterized using descriptive terms, which incorporate an element of subjectivity in their interpretations. Quantitative indices may improve the objective characterization of these motion artifacts. In this paper, an automated method for generating 12 quantitative indices, i.e., features that characterize the motion artifacts affecting calcified plaques, is presented. This method consists of using the rapid phase-correlated region-of-interest (ROI) tracking algorithm for reconstructing ROI images of calcified plaques automatically from the projection data obtained during a cardiac scan, and applying methods for extracting features from these images. The 12 features include two dynamic, six morphological, and four intensity-based features. The two dynamic features are three-dimensional (3D) velocity and 3D acceleration. The six morphological features include edge-based volume, threshold-based volume, sphericity, irregularity, average margin gradient, and variance of margin gradient. The four intensity-based features are maximum intensity, mean intensity, minimum intensity, and standard deviation of intensity. The 12 features were extracted from 54 reconstructed sets of simulated four-dimensional images from the dynamic NCAT phantom involving six calcified plaques under nine heart rate/multi-sector gating combinations. In order to determine how well the 12 features correlated with a plaque motion index, which was derived from the trajectory of the plaque, partial correlation coefficients adjusted for heart rate, number of gated sectors, and mean feature values of the six plaques were calculated for all 12 features. Features exhibiting stronger correlations (|r|∈[0.60,1.00]) with the motion index were 3D velocity, maximum intensity, and standard deviation of intensity. Features demonstrating stronger correlations (|r|∈[0.60,1.00]) with other features mostly involved intensity-based features. Edge-based volume/irregularity and average margin gradient/variance of margin gradient were the only two feature pairs out of 12 with stronger correlations that did not involve intensity-based features. Automatically extracted features of the motion artifacts affecting calcified plaques in cardiac computed tomography images potentially can be used to develop models for predicting image assessability with respect to motion artifacts. [ABSTRACT FROM AUTHOR]

An automated method for evaluating the image quality of calcified plaques with respect to motion artifacts in noncontrast-enhanced cardiac computed tomography (CT) images is introduced. This method involves using linear regression (LR) and artificial neural network (ANN) regression models for predicting two patient-specific, region-of-interest-specific, reconstruction-specific and temporal phase-specific image quality indices. The first is a plaque motion index, which is derived from the actual trajectory of the calcified plaque and is represented on a continuous scale. The second is an assessability index, which reflects the degree to which a calcified plaque is affected by motion artifacts, and is represented on an ordinal five-point scale. Two sets of assessability indices were provided independently by two radiologists experienced in evaluating cardiac CT images. Inputs for the regression models were selected from 12 features characterizing the dynamic, morphological, and intensity-based properties of the calcified plaques. Whereas LR-velocity (LR-V) used only a single feature (three-dimensional velocity), the LR-multiple (LR-M) and ANN regression models used the same subset of these 12 features selected through stepwise regression. The regression models were parameterized and evaluated using a database of simulated calcified plaque images from the dynamic NCAT phantom involving nine heart rate/multi-sector gating combinations and 40 cardiac phases covering two cardiac cycles. Six calcified plaques were used for the plaque motion indices and three calcified plaques were used for both sets of assessability indices. In one configuration, images from the second cardiac cycle were used for feature selection and regression model parameterization, whereas images from the first cardiac cycle were used for testing. With this configuration, repeated measures concordance correlation coefficients (CCCs) and associated 95% confidence intervals for the LR-V, LR-M, and ANN were 0.817 [0.785, 0.848], 0.894 [0.869, 0.916], and 0.917 [0.892, 0.936] for the plaque motion indices. For the two sets of assessability indices, CCC values for the ANN model were 0.843 [0.791, 0.877] and 0.793 [0.747, 0.828]. These two CCC values were statistically greater than the CCC value of 0.689 [0.648, 0.727], which was obtained by comparing the two sets of assessability indices with each other. These preliminary results suggest that the variabilities of assessability indices provided by regression models can lie within the variabilities of the indices assigned by independent observers. Thus, the potential exists for using regression models and assessability indices for determining optimal phases for cardiac CT image interpretation. [ABSTRACT FROM AUTHOR]

It is well known that cone-beam data acquired with a circular orbit are insufficient for exact image reconstruction. Despite this, because a cone-beam scanning configuration with a circular orbit is easy to implement in practice, it has been widely employed for data acquisition in, e.g., micro-CT and CT imaging in radiation therapy. The algorithm developed by Feldkamp, Davis, and Kress (FDK) and its modifications, such as the Tent–FDK (T-FDK) algorithm, have been used for image reconstruction from circular cone-beam data. In this work, we present an algorithm with spatially shift-variant filtration for image reconstruction in circular cone-beam CT. We performed computer-simulation studies to compare the proposed and existing algorithms. Numerical results in these studies demonstrated that the proposed algorithm has resolution properties comparable to, and noise properties better than, the FDK algorithm. As compared to the T-FDK algorithm, our proposed algorithm reconstructs images with an improved in-plane spatial resolution. © 2005 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 14, 213–221, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20026 [ABSTRACT FROM AUTHOR]

It is widely believed that measurements from a full angular range of 2π are generally required to exactly reconstruct a complex-valued refractive index distribution in diffraction tomography (DT). In this work, we developed a new class of minimal-scan reconstruction algorithms for DT that utilizes measurements only over the angular range 0 ≤ φ ≤ 3π/2 to perform an exact reconstruction. These algorithms, referred to as minimal-scan estimate-combination (MS-E-C) reconstruction algorithms, effectively operate by transforming the DT reconstruction problem into a conventional x-ray CT reconstruction problem that requires inversion of the Radon transform. We performed computer simulations to compare the noise and numerical properties of the MS-E-C algorithms against existing filtered backpropagation-based algorithms. © 2002 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 84–91, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10014 [ABSTRACT FROM AUTHOR]

In diffraction tomography (DT), the measured scattered data are unavoidably contaminated by noise. Because the detectability of an object in a noisy image relies strongly on the signal-to-noise ratio, it is important in certain applications to reduce the statistical variation in the reconstructed image. Recently, we revealed the existence of statistically complementary information inherent in the scattered data and proposed a linear strategy that makes use of this information to achieve a bias-free reduction of the image variance in two-dimensional (2D) DT. This strategy leads to the development of an infinite class of estimation methods, that from the measured scattered data, can estimate the Radon transform of the scattering object function. From the estimated Radon transforms, one can readily reconstruct the object function by using a variety of existing reconstruction algorithms. The estimation methods in the class are mathematically equivalent, but they respond to noise differently. We investigated the noise properties of these estimation methods by use of computer simulation studies. The results of our simulation studies demonstrate quantitatively that it is possible to achieve a bias-free variance reduction in the reconstructed scattering object by utilizing complementary statistical information that is inherent in the scattered data. © 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 10, 437–446, 1999 [ABSTRACT FROM AUTHOR]

Purpose: Iterative image reconstruction (IIR) algorithms in computed tomography (CT) are based on algorithms for solving a particular optimization problem. Design of the IIR algorithm, therefore, is aided by knowledge of the solution to the optimization problem on which it is based. Often times, however, it is impractical to achieve accurate solution to the optimization of interest, which complicates design of IIR algorithms. This issue is particularly acute for CT with a limited angular-range scan, which leads to poorly conditioned system matrices and difficult to solve optimization problems. In this paper, we develop IIR algorithms which solve a certain type of optimization called convex feasibility. The convex feasibility approach can provide alternatives to unconstrained optimization approaches and at the same time allow for rapidly convergent algorithms for their solution-thereby facilitating the IIR algorithm design process. Methods: An accelerated version of the Chambolle-Pock (CP) algorithm is adapted to various convex feasibility problems of potential interest to IIR in CT. One of the proposed problems is seen to be equivalent to least-squares minimization, and two other problems provide alternatives to penalized, least-squares minimization. Results: The accelerated CP algorithms are demonstrated on a simulation of circular fan-beam CT with a limited scanning arc of 144°. The CP algorithms are seen in the empirical results to converge to the solution of their respective convex feasibility problems. Conclusions: Formulation of convex feasibility problems can provide a useful alternative to unconstrained optimization when designing IIR algorithms for CT. The approach is amenable to recent methods for accelerating first-order algorithms which may be particularly useful for CT with limited angular-range scanning. The present paper demonstrates the methodology, and future work will illustrate its utility in actual CT application. [ABSTRACT FROM AUTHOR]

Purpose: A human observer study was performed for a signal detection task for the case of fan-beam x-ray computed tomography. Hotelling observer (HO) performance was calculated for the same detection task without the use of efficient channels. By considering the full image covariance produced by the filtered backprojection (FBP) algorithm and avoiding the use of channels in the computation of HO performance, the authors establish an absolute upper bound on signal detectability. Therefore, this study serves as a baseline for relating human and ideal observer performance in the case of fan-beam CT. Methods: Eight human observers participated in a two-alternative forced choice experiment where the signal of interest was a small simulated ellipsoid in the presence of independent, identically distributed Gaussian detector noise. Theoretical performance of the HO, which is equivalent to the ideal observer in this case (see Sec. 13.2.12 in Barrett and Myers [Foundations of Image Science (Wiley, Hoboken, NJ, 2004)], was also computed and compared to the performance of the human observers. In addition to a reference FBP implementation, two FBP implementations with inherent loss of HO signal detectability (e.g., by apodizing the ramp filter) were also investigated. Each of these latter two implementations takes the form of a discrete-to-discrete linear operator (i.e., a matrix), which has a nontrivial null-space resulting in the loss of detectability. Results: Estimated observer detectability index (d⁁A) values for the human observers and SNR values for the HO were obtained. While Hanning filtering in the FBP implementation with a cutoff frequency of 1/4 of the Nyquist frequency reduces HO SNR (due to the reconstruction matrix's nontrivial null-space), this filtering was shown to consistently improve human observer performance. By contrast, increasing the image pixel size was seen to have a comparable effect on both the HO and the human observers, degrading performance. Conclusions: These results, which characterize HO and human observer performance for a signal detection task in fan-beam FBP noise, form a basis for applying model observer metrics to fan-beam CT when knowledge of the full image-domain noise statistics is important. Further, by calculating HO performance without relying on channels, these results are particularly relevant when an information theoretic approach is considered, e.g., in optimization of the image reconstruction algorithm with respect to preservation of signal detectability. Finally, the HO (which is here equivalent to the ideal observer) provides an absolute upper bound on detection performance, and our results therefore provide insight into the performance of human observers relative to the optimum for two different cases wherein ideal observer performance is compromised through degradation of the data quality. In one case (regularization), human performance is improved to practically ideal performance, and in the other (larger pixel size), ideal and human observer performance are approximately degraded equivalently. [ABSTRACT FROM AUTHOR]

Bone morphogenetic protein 9 (BMP-9) is a member of the transforming growth factor (TGF)-beta/BMP superfamily, and we have demonstrated that it is one of the most potent BMPs to induce osteoblast differentiation of mesenchymal stem cells (MSCs). Here, we sought to investigate if canonical Wnt/beta-catenin signalling plays an important role in BMP-9-induced osteogenic differentiation of MSCs. Wnt3A and BMP-9 enhanced each other's ability to induce alkaline phosphatase (ALP) in MSCs and mouse embryonic fibroblasts (MEFs). Wnt antagonist FrzB was shown to inhibit BMP-9-induced ALP activity more effectively than Dkk1, whereas a secreted form of LPR-5 or low-density lipoprotein receptor-related protein (LRP)-6 exerted no inhibitory effect on BMP-9-induced ALP activity. beta-Catenin knockdown in MSCs and MEFs diminished BMP-9-induced ALP activity, and led to a decrease in BMP-9-induced osteocalcin reporter activity and BMP-9-induced expression of late osteogenic markers. Furthermore, beta-catenin knockdown or FrzB overexpression inhibited BMP-9-induced mineralization in vitro and ectopic bone formation in vivo, resulting in immature osteogenesis and the formation of chondrogenic matrix. Chromatin immunoprecipitation (ChIP) analysis indicated that BMP-9 induced recruitment of both Runx2 and beta-catenin to the osteocalcin promoter. Thus, we have demonstrated that canonical Wnt signalling, possibly through interactions between beta-catenin and Runx2, plays an important role in BMP-9-induced osteogenic differentiation of MSCs.