High-contrast Objects Research Articles

Optical Brewster interfaces enabled object identification and 3D reconstruction

Efficient and accurate object identification and 3D reconstruction are crucial for processing image information in visual imaging. Here, we propose a novel scheme for all-optical 2D contour identification and 3D reconstruction based on optical Brewster interfaces. It is revealed that 2D amplitude and phase contours for high-contrast and low-contrast objects can be identified, which is attributed to the 1D and 2D light fields manipulated by the photonic spin Hall and the Brewster effects. The 3D model can be reconstructed by rotating or slicing the high-contrast objects and by inverting the thickness of the low-contrast objects. The study potentially opens up opportunities in applications such as intelligent driving and microscopic imaging.

Frequency-hopping along with resolution-turning for fast and enhanced reconstruction in ultrasound tomography

The distorted Born iterative (DBI) method is considered to obtain images with high-contrast and resolution. Besides satisfying the Born approximation condition, the frequency-hopping (FH) technique is necessary to gradually update the sound contrast from the first iteration and progress to the actual sound contrast of the imaged object in subsequent iterations. Inspired by the fact that the higher the frequency, the higher the resolution. Because low-frequency allows for low-resolution object imaging, hence for high-resolution imaging requirements, using low-frequency to possess a high-resolution image from the first iteration will be less efficient. For an effective reconstruction, the object’s resolution at low frequencies should be small. And similarly, with high frequencies, the object resolution should be larger. Therefore, in this paper, the FH, and the resolution-turning (RT) technique are proposed to obtain object images with high-contrast and -resolution. The convergence speed in the initial iterations is rapidly achieved by utilizing low frequency in the frequency-turning technique and low image resolution in the resolution-turning technique. It is crucial to ensure accurate object reconstruction for subsequent iterations. The desired spatial resolution is attained by employing high frequency and large image resolution. The resolution-turning distorted Born iterative (RT-DBI) and frequency-hopping distorted Born iterative (FH-DBI) solutions are thoroughly investigated to exploit their best performance. This makes sense because if it is not good to choose the number of iterations for the frequency f1 in FH-DBI and for the resolution of N1 × N1 in RT-DBI, then these solutions give even worse quality than traditional DBI. After that, the RT-FH-DBI integration was investigated in two sub-solutions. We found that the lower frequency f1 used both before and after the RT would get the best performance. Consequently, compared to the traditional DBI approaches, the normalized error and total runtime for the reconstruction process were dramatically decreased, at 83.6% and 18.6%, respectively. Besides fast and quality imaging, the proposed solution RT-FH-DBI is promised to produce high-contrast and high-resolution object images, aiming at object reconstruction at the biological tissue. The development of 3D imaging and experimental verification will be studied further.

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3mGy, while the lowest noise magnitude and highest d' were attained at 8.8mGy. SR-DLR at 4.3mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

Rapid Determination of Meteorolite Composition Based on X-ray Phase Contrast Imaging-Assisted Raman Spectroscopy

Returning extraterrestrial samples to Earth has become essential for future deep space exploration. Achieving a comprehensive evaluation of the physical and chemical properties of samples with minimal damage is key to analyzing extraterrestrial samples in the future, as well as to the future sampling and returning of heterogeneous solid samples. This article aims to reconstruct the three-dimensional internal structure of high-contrast objects, select sections of interest through internal structure and detail features, and then analyze the physical and chemical properties of the samples based on laser spectroscopy technology. This paper proposes a strategy based on Raman mapping and X-ray phase-contrast imaging technology to reconstruct the three-dimensional internal structure of a heterogeneous solid sample and detect the substance composition of the region of interest. This study takes meteorite samples as an example and uses X-ray phase-contrast imaging technology to distinguish and reconstruct the spatial distribution of different components in the meteorite, providing a three-dimensional visualization reference with a high spatial resolution for the spatial positioning of the region of interest. Raman spectroscopy, in combination with LIBS, was used to further identify the meteorite as pallasite and to achieve the spectral image fusion of high spatial and high spectral resolutions. The experimental results show that the unknown meteorite’s three-dimensional structure and its components’ spatial distribution can be evaluated based on Raman mapping combined with X-ray phase-contrast imaging technology. This article provides a highly valuable analytical strategy by which to analyze samples returned from deep space exploration.

Spectral Retrieval with JWST Photometric data: a Case Study for HIP 65426 b

Half of the JWST high-contrast imaging objects will only have photometric data as of Cycle 2. However, to better understand their atmospheric chemistry that informs formation origin, spectroscopic data are preferred. Using HIP 65426 b, we investigate to what extent planet properties and atmospheric chemical abundance can be retrieved with only JWST photometric data points (2.5–15.5 μm) in conjunction with ground-based archival low-resolution spectral data (1.0–2.3 μm). We find that the data is consistent with an atmosphere with solar metallicity and C/O ratios at 0.40 and 0.55. We rule out 10× solar metallicity and an atmosphere with C/O = 1.0. We also find strong evidence of silicate clouds but no sign of an enshrouding featureless dust extinction. This work offers guidance and cautionary tales on analyzing data in the absence of medium-to-high-resolution spectral data.

Infrared visibility and infrared visibility range: the practical signif cance and method for measuring

New physical quantities are presented – infrared visibility and infrared visibility range. The relevance and demand for measurements of these quantities is shown. Their physical meaning is revealed, formula ratios for the calculation are given, and the practical signifi cance. The presented calculation ratios are obtained on the basis of the transformation of the real thermal portrait of the object into an equivalent thermal geometric model. It is shown that the numerical values of the infrared visibility index for various real objects can vary over a wide range: from zero for absolutely non-thermal contrast objects to values of several units for high-contrast objects, i.e. within 0.5–2.5. The ratio for the infrared visibility range is obtained in based on the Lambert-Bouguer-Beer law and Fechner's law, used in the theory of sensory sensitivity. A detailed description and procedure for measuring the indicated physical quantities is presented, examples are given, and the measurement uncertainty is estimated. The introduced values are aimed at ensuring the unity of the assessment of thermal protection of various man-made and biological objects and allow developing preventive measures for their thermal protection at the stage of commissioning.

Spatiotemporal neural dynamics of object recognition under uncertainty in humans.

While there is a wealth of knowledge about core object recognition-our ability to recognize clear, high-contrast object images-how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla (7T) fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved.

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

On a Low-Frequency and Contrast-Stabilized Full-Wave Volume Integral Equation Solver for Lossy Media

In this article, we present a new regularized electric flux volume integral equation (D-VIE) for modeling high-contrast conductive dielectric objects in a broad frequency range. This new formulation is particularly suitable for modeling biological tissues at low frequencies, as it is required by brain epileptogenic area imaging, but also at higher ones, as it is required by several applications, including, but not limited to, deep brain stimulation (DBS). When modeling inhomogeneous objects with high complex permittivities at low frequencies, the traditional D-VIE is ill-conditioned and suffers from numerical instabilities that result in slower convergence and less accurate solutions. In this work, we address these shortcomings by leveraging a new set of volume quasi-Helmholtz projectors. Their scaling by the material permittivity matrix allows for the rebalancing of the equation when applied to inhomogeneous scatterers and, thereby, makes the proposed method accurate and stable even for high complex permittivity objects until arbitrarily low frequencies. Numerical results, canonical and realistic, corroborate the theory and confirm the stability and the accuracy of this new method both in the quasi-static regime and at higher frequencies.

Multimodule Deep Learning Scheme for Elastic Wave Inversion of Inhomogeneous Objects With High Contrasts

In elastic wave inverse scattering problems, the material-property reconstruction, e.g. distributions of mass density, compressional wave speed, and shear wave speed from a limited set of measurement data, has attracted considerable research interest. However, simultaneous inversion of multiple parameters endures high computation costs, and reconstructed compressional and shear speeds may become unrealistic if no physical constraint is imposed. Meanwhile, because large objects with high contrasts over the background medium tend to induce high nonlinearity during the inversion process, it is difficult to obtain high-quality high-contrast material properties. To overcome such difficulties, we have developed a multi-module deep learning scheme with physical constraint for multi-parameter elastic wave inversion of high-contrast objects in inhomogeneous media. This scheme consists of (1) a preliminary imaging module (PIM), in which a deep residual network (ResNet) is employed to convert the scattered field data into the preliminary inversion images, (2) an image-enhancement module (IEM), in which a U-Net is employed to further enhance the image quality, and (3) a convolutional neural network (CNN) that is employed as the physical constraint module (PCM) to allow elastic wave parameters to satisfy actual physical constraint. Numerical examples have demonstrated that the proposed scheme not only accurately achieves multi-parameter elastic wave inversion, but also has good generalizability. Finally, the scheme can be applied to complex objects with high contrasts in both noise-free and noisy environments.

Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study.

We assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging. A Catphan® 600 phantom was scanned by DL-Spectral CT at various radiation doses. We reconstructed the VMIs obtained at 50, 70, and 100keV. SECT (120kVp) images were acquired at the same radiation doses. The standard deviations of the CT number and noise power spectrum (NPS) were calculated for noise characterization. We evaluated the spatial resolution by determining the 10% task-based transfer function (TTF) level, and we assessed the task-based detectability index (d'). Regardless of the radiation dose, the noise was the lowest at 70keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120kVp images. The spatial resolution was higher for 70keV VMI compared to the other VMIs, except for high-contrast objects. The d' of 70keV VMI was the highest among the VMI and 120kVp images at all radiation doses and contrast settings. The d' of the 70keV VMIs at the minimum dose was higher than that at the maximum dose in any other image. The physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.

Motion adaptation improves acuity (but perceived size doesn't matter).

Recognition acuity—the minimum size of a high-contrast object that allows us to recognize it—is limited by optical and neural elements of the eye and by processing within the visual cortex. The perceived size of objects can be changed by motion-adaptation. Viewing receding or looming motion makes subsequently viewed stimuli appear to grow or shrink, respectively. It has been reported that resulting changes in perceived size impact recognition acuity. We set out to determine if such acuity changes are reliable and what drives this phenomenon. We measured the effect of adaptation to receding and looming motion on acuity for crowded tumbling-T stimuli (). We quantified the role of crowding, individuals’ susceptibility to motion-adaptation, and potentially confounding effects of pupil size and eye movements. Adaptation to receding motion made targets appear larger and improved acuity (–0.037 logMAR). Although adaptation to looming motion made targets appear smaller, it induced not the expected decrease in acuity but a modest acuity improvement (–0.018 logMAR). Further, each observer's magnitude of acuity change was not correlated with their individual perceived-size change following adaptation. Finally, we found no evidence that adaptation-induced acuity gains were related to crowding, fixation stability, or pupil size. Adaptation to motion modestly enhances visual acuity, but unintuitively, this is dissociated from perceived size. Ruling out fixation and pupillary behavior, we suggest that motion adaptation may improve acuity via incidental effects on sensitivity—akin to those arising from blur adaptation—which shift sensitivity to higher spatial frequency-tuned channels.

Automated image quality assessment of mammography phantoms: a systematic review.

Computerized image analysis is a viable technique for evaluating image quality as a complement to human observers. To systematically review the image analysis software used in the assessment of 2D image quality using mammography phantoms. A systematic search of multiple databases was performed from inception to July 2020 for articles that incorporated computerized analysis of 2D images of physical mammography phantoms to determine image quality. A total of 26 studies were included, 12 were carried out using direct digital imaging and 14 using screen film mammography. The ACR phantom (model-156) was the most frequently evaluated phantom, possibly due to the lack of accepted standard software. In comparison to the inter-observer variations, the computerized image analysis was more consistent in scoring test objects. The template matching method was found to be one of the most reliable algorithms, especially for high-contrast test objects, while several algorithms found low-contrast test objects to be harder to distinguish due to the smaller contrast variations between test objects and their backgrounds. This was particularly true for small object sizes. Image analysis software was in agreement with human observers but demonstrated higher consistency and reproducibility of quality evaluation. Additionally, using computerized analysis, several quantitative metrics such as contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) could be used to complement the conventional scoring method. Implementing a computerized approach for monitoring image quality over time would be crucial to detect any deteriorating mammography system before clinical images are impacted.

Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT

To evaluate the image properties of lung-specialized deep-learning-based reconstruction (DLR) and its applicability in ultralow-dose CT (ULDCT) relative to hybrid- (HIR) and model-based iterative-reconstructions (MBIR). An anthropomorphic chest phantom was scanned on a 320-row scanner at 50-mA (low-dose-CT 1 [LDCT-1]), 25-mA (LDCT-2), and 10-mA (ULDCT). LDCT were reconstructed with HIR; ULDCT images were reconstructed with HIR (ULDCT-HIR), MBIR (ULDCT-MBIR), and DLR (ULDCT-DLR). Image noise and contrast-to-noise ratio (CNR) were quantified. With the LDCT images as reference standards, ULDCT image qualities were subjectively scored on a 5-point scale (1=substantially inferior to LDCT-2, 3=comparable to LDCT-2, 5=comparable to LDCT-1). For task-based image quality analyses, a physical evaluation phantom was scanned at seven doses to achieve the noise levels equivalent to chest phantom; noise power spectrum (NPS) and task-based transfer function (TTF) were evaluated. Clinical ULDCT (10-mA) images obtained in 14 nonobese patients were reconstructed with HIR, MBIR, and DLR; the subjective acceptability was ranked. Image noise was lower and CNR was higher in ULDCT-DLR and ULDCT-MBIR than in LDCT-1, LDCT-2, and ULDCT-HIR (p < 0.01). The overall quality of ULDCT-DLR was higher than of ULDCT-HIR and ULDCT-MBIR (p < 0.01), and almost comparable with that of LDCT-2 (mean score: 3.4 ± 0.5). DLR yielded the highest NPS peak frequency and TTF50% for high-contrast object. In clinical ULDCT images, the subjective acceptability of DLR was higher than of HIR and MBIR (p < 0.01). DLR optimized for lung CT improves image quality and provides possible greater dose optimization opportunity than HIR and MBIR.

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

BACKGROUND. Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images with standard-dose iterative reconstruction (IR) images or explored application of DLR to low-tube-voltage scanning in children. OBJECTIVE. The purpose of this study was to assess whether DLR can be used to reduce radiation dose while maintaining diagnostic image quality in comparison with hybrid IR (HIR) and model-based IR (MBIR) for low-tube-voltage pediatric CT. METHODS. This retrospective study included children 6 years old or younger who underwent contrast-enhanced 80-kVp CT with a standard-dose or lower-dose protocol. Standard images were reconstructed with HIR, and lower-dose images were reconstructed with HIR, MBIR, and DLR. Size-specific dose estimate (SSDE) was calculated for both protocols. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Two radiologists independently evaluated noise magnitude, noise texture, streak artifact, edge sharpness, and overall quality. Interreader agreement was assessed, and mean values were calculated. To evaluate task-based object detection performance, a phantom was imaged with 80-kVp CT at six doses (SSDE, 0.6-5.3 mGy). Detectability index (d') was calculated from the noise power spectrum and task-based transfer function. Reconstruction methods were compared. RESULTS. Sixty-five children (mean age, 25.0 ± 25.2 months) who underwent CT with standard- (n = 31) or lower-dose (n = 34) protocol were included. SSDE was 54% lower for the lower-dose than for the standard-dose group (1.9 ± 0.4 vs 4.1 ± 0.8 mGy). Lower-dose DLR and MBIR yielded lower image noise and higher SNR and CNR than standard-dose HIR (p < .05). Interobserver agreement on subjective features ranged from a kappa coefficient of 0.68 to 0.78. The readers subjectively scored noise texture, edge sharpness, and overall quality lower for lower-dose MBIR than for standard-dose HIR (p < .001), though higher for lower-dose DLR than for standard-dose HIR (p < .001). In the phantom, DLR provided higher d' than HIR and MBIR at each dose. Object detectability was greater for 2.0-mGy DLR than for 4.0-mGy HIR for low-contrast (3.67 vs 3.57) and high-contrast (1.20 vs 1.04) objects. CONCLUSION. Compared with IR algorithms, DLR results in substantial dose reduction with preserved or even improved image quality for low-tube-voltage pediatric CT. CLINICAL IMPACT. Use of DLR at 80 kVp allows greater dose reduction for pediatric CT than do current IR techniques.

Multiple Buried Target Reconstruction Using a Multiscale Hybrid of Diffraction Tomography and CMA-ES Optimization

In this article, a hierarchical stochastic optimization algorithm for profiling of multiple high-contrast buried objects in large investigation domains (IDs) is presented. As this problem is highly non-linear and ill-posed, a combination of different profiling modalities is required to tackle the challenges. First, an initialization step using qualitative diffraction tomography (DT) method is performed to not only limit the ID to scatterers’ locations but also obtain an approximation of their dielectric permittivity range. Then, an algorithm which combines the iterative multi-scaling approach (IMSA) with the reconstruction capabilities of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented. IMSA is a multistep strategy, which starts with coarse meshes in lower frequencies, then, step by step, tightens the ID to the newly found domain of scatterers and uses finer meshes for partitioning them. This procedure enhances the resolution without increasing the number of unknowns. In each step, the inversion process is executed using the global optimization technique of CMA-ES. The proposed technique uses the full advantages of global optimization technique and at the same time, by executing it on a multi-scaling scheme, and using the initializing step, reduces the number of unknowns, the degree of freedom in the search space, and the required measured data. The Numerical assessments for various scenarios are performed that clearly shows an acceptable dielectric profile retrieval, even for inhomogeneous dielectric distributions or noisy measurements. Moreover, a comparison between CMA-ES and other global optimization algorithms is performed, which reveals the outperformance of CMA-ES in these scenarios.

Accurate Iterative Inverse Scattering Methods Based on Finite-Difference Frequency-Domain Inversion

Two accurate finite-difference frequency-domain (FDFD)-based iterative inverse methods, referred to as FDFD-based iterative method (FIM) and distorted FDFD-based iterative method (DFIM), are proposed for electromagnetic inverse scattering. They can be considered as FDFD-based versions of BIM and DBIM and inherit the advantage of the traditional direct FDFD inversion method, i.e., there is no need for Green’s function of complex background, especially for the inhomogeneous medium. The difference is that full-wave consideration is implemented in the proposed methods to build an iterative framework. Then, the scattered field and object parameter profile are updated in each iteration. Using the high-order components in iteration formulation, both the methods are able to reconstruct high-contrast objects and provide accurate reconstruction results. Compared with other full-wave methods based on differential equation, the two methods also have a distinct characteristic that the inversion accuracy is not limited by extra constraints. The difference between the two proposed methods is whether the background coefficient matrix is updated. Generally, DFIM has faster convergence than FIM due to the updated background coefficient matrix in the iterations. To demonstrate the effectiveness of both the methods, several typical 2-D experiments are conducted. The results show that the proposed methods could achieve good accuracy and high imaging quality.

The Effect Of Display Brightness On Visual Function For Young And Old Drivers

Nowadays outdoor advertising displays have become popular. Bright displays near the roads could cause drivers to experience disability or discomfort glare, especially at night. Disability glare increases with age, but discomfort glare thresholds are independent of age. The aim of the study was to assess a luminance level of displays, which causes glare for younger and older subjects. 24 young subjects age of 20 to 24 years and 13 older subjects age of 55 to 69 years participated in the study. The task was by using the method of adjustment to find out the acceptable level of display brightness when the recognition of high (>90%) contrast objects was comfortable. Measurements were done in a photopic and mesopic lighting conditions. Results showed that discomfort glare were larger in mesopic than in photopic lighting conditions (p < 0.001) for both age groups. Preferred display brightness in both lighting conditions did not significantly differ between age groups (p > 0.05). We can conclude that discomfort glare thresholds for displays with textual elements are independent of age.

Anomalous edge response of cadmium telluride-based photon counting detectors jointly caused by high-flux radiation and inter-pixel communication

This work reports an edge enhancing effect experimentally observed in cadmium telluride (CdTe)-based photon counting detector (PCD) systems operated under the charge summing (CS) mode and irradiated by high-flux x-rays. Experimental measurements of the edge spread functions (ESFs) of a PCD system (100 μm pixel size, 88 ns deadtime) were performed at different input flux levels from 4.5 × 105 count per second (cps) mm−2 to 1.5 × 109 cps mm−2 for the single pixel mode (SP) and the CS mode. A theoretical model that incorporates the impacts of inter-pixel communications and the arbitration process involved in the CS mode was developed to help explain the physical origin of the observed edge enhancing effect. Compared with the monotonically increasing ESF of the SP mode, the ESF of the CS mode measured at high-flux levels shows a peak at an intermediate location (50 μm from the edge). The peak became more pronounced with increasing flux levels. The theoretically calculated ESFs agreed well with experimental results with relative errors less than 5% at all flux levels and tested. These results indicate that the anomalous edge enhancing effect is jointly caused by the pileup effect and the CS circuit that introduces negative correlations between adjacent pixels. When the input flux is high enough to deliver photons to multiple adjacent pixels within the same deadtime period, the CS mode may treat the coincident x-rays as shared charges and thus introduce count losses in addition to the well-known pileup count loss. When a high contrast object partially blocks certain pixels from x-rays, the adjacent unblocked pixels have an increased probability of registering counts as a result of the negative correlation. This leads to a peak on the ESF at a pixel-to-edge distance half of the pixel pitch.

Technical Note: A simple method for measuring the slice sensitivity profile of iteratively reconstructed CT images using a non‐slanted edge plane

A method for measuring the slice sensitivity profile (SSP) of computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms was reported by the AAPM Task Group 233 (TG233). In this method, the phantom plane edge is slightly slanted with respect to the scan plane to obtain a composite oversampled edge-spread function (ESF). However, it is expected that a fine-sampled ESF can be obtained directly from images reconstructed with a small slice increment without slanting the edge plane. This study aimed to investigate the validity of using a non-slanted edge plane. In the proposed non-slanted edge method, the phantom was positioned so that the plane edge was perpendicular to the longitudinal z-axis, and images were reconstructed with a 1-mm slice thickness and 0.1-mm increment. The mean CT value was obtained in each slice and plotted as a function of slice position along the z-axis, thereby generating the ESF. The SSP was calculated from the ESF by differentiation. In the TG 233-recommended slanted edge method, the SSP was obtained by following the procedure described in the TG233 report. To validate the methodology, we first used filtered back projection (FBP) images to compare SSPs obtained using the non-slanted edge method, slanted edge method, and a standard method using a high-contrast thin object (coin). Next, for two types of IR algorithms, we compared the SSPs obtained using the non-slanted and slanted edge methods. For the FBP images, the SSP measured using the non-slanted edge method agreed well with SSPs measured using the coin and slanted edge methods. For the IR images, the SSPs measured using the non-slanted and slanted edge methods showed good agreement. The non-slanted edge method was demonstrated to be valid. The simplicity and practicality of the method allows routine and accurate determination of the SSP.