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Recently developed x-ray transmittance modeling-based three-step algorithm compensates for the spectral distortion in the photon-counting detector (PCD) and estimates the line-integrals of the basis materials. The x-ray transmittance modeling linearizes the nonlinear forward imaging model and derives a computationally efficient three-step algorithm that achieves almost unbiased and minimum variance estimator. In this article, we apply the algorithm to the experimental data from a research whole-body PCD-computed tomography (CT) system. We perform pixel-by-pixel calibration using water-equivalent phantoms to fit the output of the scanner to the forward model and then feed the calibrated data into the three-step algorithm. Experimental data from iodine phantom and swine abdomen scans demonstrate that the proposed algorithm compensates for the spectral distortion effectively and estimates the line-integrals of two basis materials, water and iodine, efficiently. The proposed method substantially reduces the beam hardening and ring artifacts present in the test phantom compared to those of the image-based material decomposition method and the images directly reconstructed from the raw data of the system. The algorithm also exhibits less bias and comparable noise to those of the other methods for various x-ray energies.

We present an upgraded version of the Photon Counting Toolkit (PcTK), a publicly available MATLAB tool for the simulation of semiconductor-based photon counting detectors (PCD), which has been extended and validated to account for gallium arsenide (GaAs)-based PCD(s). The modified PcTK version was validated by performing simulations and acquiring experimental data for three different cases. The LAMBDA 60 K module planar detector (X-Spectrum GmbH, Germany) based on the Medipix3 ASIC technology was used in all cases. This detector has a 500-µm thick GaAs sensor and a 256 × 256-pixel array 55 µm pixel size. The first validation was a comparison between simulated and measured spectra from a 109Cd radionuclide source. In the second validation study, experiments and simulations of mammography spectra were conducted to observe the performance of the GaAs version of the PcTK with polychromatic radiation used in conventional x-ray imaging systems. The third validation study used single event analysis to validate the spatio-energetic model of the extended PcTK version. Overall, the software provided a good agreement between simulated and experimental data, validating the accuracy of the GaAs model. The software could be an attractive tool for accurate simulation of breast imaging modalities relying on photon counting detectors and therefore could assist in their characterization and optimization.

We aim at developing a model-based algorithm that compensates for the effect of both pulse pileup (PP) and charge sharing (CS) and evaluates the performance using computer simulations.The proposed PCP algorithm for PP and CS compensation uses cascaded models for CS and PP we previously developed, maximizes Poisson log-likelihood, and uses an efficient three-step exhaustive search. For comparison, we also developed an LCP algorithm that combines models for a loss of counts (LCs) and CS. Two types of computer simulations, slab- and computed tomography (CT)-based, were performed to assess the performance of both PCP and LCP with 200 and 800 mA, (300 µm)With the slab simulation, the PCP had little or no biases when the expected counts were sufficiently large, even though a probability of count loss (PCL) due to dead-time loss or PP was as high as 0.8. In contrast, the LCP had significant biases (±2 cm of adipose) when the PCL was higher than 0.15. Biases were present with both PCP and LCP when the expected counts were less than 10-120 per datum, which was attributed to the fact that the maximum likelihood did not approach the asymptote. The noise of PCP was within 8% from the Cramér-Rao lower bounds for most cases when no significant bias was present. The two CT studies essentially agreed with the slab simulation study. PCP had little or no biases in the estimated basis line integrals, reconstructed basis density maps, and synthesized monoenergetic CT images. But the LCP had significant biases in basis line integrals when X-ray beams passed through lungs and near the body and neck contours, where the PCLs were above 0.15. As a consequence, basis density maps and monoenergetic CT images obtained by LCP had biases throughout the imaged space.We have developed the PCP algorithm that uses the PP-CS model. When the expected counts are more than 10-120 per datum, the PCP algorithm is statistically efficient and successfully compensates for the effect of the spectral distortion due to both PP and CS providing little or no biases in basis line integrals, basis density maps, and monoenergetic CT images regardless of count-rates. In contrast, the LCP algorithm, which models an LC due to pileup, produces severe biases when incident count-rates are high and the PCL is 0.15 or higher.

PURPOSE Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone-beam computed tomography is too slow (it takes 4-10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra-operative four-dimensional soft tissue PErfusion using a standard x-ray system with No gantry rotation. METHODS IPEN uses two input datasets: (a) the contours and locations of three-dimensional regions-of-interest (ROIs) such as arteries and sub-sections of cancerous lesions, and (b) a series of x-ray projection data obtained from an intra-arterial contrast injection to contrast enhancement to wash-out. IPEN then estimates a time-enhancement curve (TEC) for each ROI directly from projections without reconstructing cross-sectional images by maximizing the agreement between synthesized and measured projections with a temporal roughness penalty. When path lengths through ROIs are known for each x-ray beam, the ROI-specific enhancement can be accurately estimated from projections. Computer simulations are performed to assess the performance of the IPEN algorithm. Intra-arterial contrast-enhanced liver scans over 25 s were simulated using XCAT phantom version 2.0 with heterogeneous tissue textures and cancerous lesions. The following four sub-studies were performed: (a) The accuracy of the estimated TECs with overlapped lesions was evaluated at various noise (dose) levels with either homogeneous or heterogeneous lesion enhancement patterns; (b) the accuracy of IPEN with inaccurate ROI contours was assessed; (c) we investigated how overlapping ROIs and noise in projections affected the accuracy of the IPEN algorithm; and (d) the accuracy of the perfusion indices was assessed. RESULTS The TECs estimated by IPEN were sufficiently accurate at a reference dose level with the root-mean-square deviation (RMSD) of 0.0027 ± 0.0001 cm-1 or 13 ± 1 Hounsfield unit (mean ± standard deviation) for the homogeneous lesion enhancement and 0.0032 ± 0.0005 cm-1 for the heterogeneous enhancement (N = 20 each). The accuracy was degraded with decreasing doses: The RMSD with homogeneous enhancement was 0.0220 ± 0.0003 cm-1 for 20% of the reference dose level. Performing 3 × 3 pixel averaging on projection data improved the RMSDs to 0.0051 ± 0.0002 cm-1 for 20% dose. When the ROI contours were inaccurate, smaller ROI contours resulted in positive biases in TECs, whereas larger ROI contours produced negative biases. The bias remained small, within ± 0.0070 cm-1 , when the Sorenson-Dice coefficients (SDCs) were larger than 0.81. The RMSD of the TEC estimation was strongly associated with the condition of the problem, which can be empirically quantified using the condition number of a matrix Az that maps a vector of ROI enhancement values z to projection data and a weighted variance of projection data: a linear correlation coefficient (R) was 0.794 (P

PURPOSE: Spectral distortion due to charge sharing (CS) and pulse pileup (PP) in photon counting detectors (PCDs) degrades the quality of PCD data. We recently proposed multi-energy inter-pixel coincidence counters (MEICC) that provided spectral cross-talk information related to CS. When PP was absent, the normalized Cramér–Rao lower bounds (nCRLBs) of 225-μm pixel PCDs with MEICC was comparable to those of 450-μm pixel PCD without MEICC. The aim of this study was to assess the performance of PCDs with MEICC at the presence of both CS and PP using computer simulations. METHODS: An in-house Monte Carlo program was modified to incorporate the following four temporal elements: (1) A pulse shape with a pulse duration of 20 ns, (2) delays of up to 10 ns in anode arrival times when photons were incident on pixel boundaries, (3) offsets proportional to a vertical separation between the primary and secondary charge clouds at the rate of ±4 ns per ±100 μm, and (4) a stochastic fluctuation of anode arrival times for all of the charge clouds with a standard deviation of 2 ns. We assessed the performance of five PCDs, (a)–(f), for three spectral tasks, (A)–(C): (a) The conventional PCD, (b) a PCD with MEICC, (c) a PCD with one coincidence counter (1CC), (d) a PCD with a 3×3 analog charge summing scheme (ACS), and (e) a PCD with a 3×3 digital count summing scheme (DCS); (A) conventional CT imaging with water (i.e., linear attenuation coefficient maps), (B) water–bone material decomposition, and (C) K-edge imaging with tungsten. The tube current was changed from 1 mA to 1,000 mA and the nCRLB was assessed. RESULTS: The recorded count rate curves were fitted by the non-paralyzable detection model with the effective deadtime parameter. The best fit was achieved by 25.8 ns for the conventional PCD, 18.6 ns for MEICC and 1CC, 140.5 ns for ACS, and 209.0 ns for DCS. The nCRLBs were strongly dependent on count rates. MEICC provided the best nCRLBs for all of the imaging tasks over the count rate range investigated except for a few conditions such as K-edge imaging at 1 mA. PP decreased the merit of MEICC over the conventional PCD in addressing CS. Nonetheless, MEICC consistently provided better nCRLBs than the conventional PCD did. The nCRLBs of MEICC were in the range of 49–58% of those of the conventional PCD for K-edge imaging, 45–76% for water–bone material decomposition, and 81–88% for the conventional CT imaging (i.e., linear attenuation coefficient maps). ACS provided better nCRLBs than the conventional PCD did only when the effect of PP was minor (e.g., when the counting efficiency of the conventional PCD was higher than 0.95 with the tube current of up to 100 mA). CONCLUSION: Besides a few cases, MEICC provides the best nCRLBs for all of the tasks at all of the count rates. ACS and DCS provides better nCRLBs than the conventional PCD does only when count rates are very low.

Recently, multienergy interpixel coincidence counter (MEICC) has been proposed for charge sharing (CS) correction and compensation for photon counting detectors (PCDs), which uses energy-dependent coincidence counters to record coincident events between multiple energy windows of a pixel-of-interest and those of neighboring pixels. A Monte Carlo (MC) simulation study was performed to assess the performance of MEICC; however, the performance might have been overestimated in a previous study. The CS increases the number of photons recorded at a PCD pixel at the expense of the spatial resolution, and therefore, when spatially uniform flat-field X-ray signals are used, it gives PCDs with CS more signals than a PCD without CS. In this article, we propose to use spatially contained boxcar signals for evaluating the performances for high spatial frequency tasks because they provide consistent signals regardless of the presence or absence of CS. The flat-field signals must be used for low spatial frequency tasks. We assessed the performances of MEICC and other PCDs with both flat-field signals and boxcar signals, with optimal threshold energies, and with two different pixel sizes. As it is expected, normalized Cramer–Rao lower bounds (nCRLBs) measured with the boxcar signals were worse than those with flat-field signals in general. The nCRLBs of MEICC with 225- $\mu \text{m}$ pixel were close to the current 450- $\mu \text{m}$ PCD. We studied a combination of flat-field signals and $N\times N$ super-pixels, where the output of $N\times N$ pixels were added, using an MC simulation and a simple CS counting model. The study showed that CS had two opposing impacts on the conventional computed tomography imaging—a negative impact with double-counting among $N\times N$ pixels and a positive impact with single-counting spill-in and spill-out across the super-pixel boundary—and the positive impact diminished with increasing $N$ . A use of large $N\times N$ super-pixels such as $N\geq 25$ was suggested to approximate the zero-frequency detection quantum efficiency of PCD with CS.

Purpose: Dual-energy cone-beam CT (DE-CBCT) is an emerging technology with potential application in diagnostic imaging and image-guided interventions. This paper reports DE-CBCT feasibility and investigates decomposition algorithms for maximizing low-dose performance for reconstruction-based DE decomposition. A framework of binary decision theory is used to examine the accuracy of DE decompositions obtained from analytical reconstructions of differentially filtered low-energy (LE) and high-energy (HE) data and from penalized likelihood (PL) reconstructions with differential regularization using quadratic and total variation penalties. Methods: Accurate DE-CBCT decomposition benefits from consideration of all system noise components. Filtered backprojection (FBP) reconstruction-based decomposition was investigated with differential filtering of LE and HE data. Penalized likelihood reconstruction-based decomposition with differential regularization was hypothesized to further improve low-dose performance, especially when coupled with regularization through a total variation edge preserving penalty that encourages piecewise smooth images. Performance of decomposition was assessed in terms of a binary hypothesis framework of sensitivity, specificity, and accuracy. Studies involved experiments on a DE-CBCT testbench, phantoms of variable material type and concentration, and cadavers (knee arthrography). Results: Studies support the overall feasibility of accurate, low-dose DE-CBCT at concentration down to 5 mg/ml (iodine), dose ~3-6 mGy, and accuracy of material classification ~90%. Reconstruction-based decomposition with quadratic PL performed comparably to FBP. PL with a total variation penalty provided edge preservation and piecewise smooth images that aided DE classification and achieved improved performance over FBP and quadratic PL, reaching accuracy of ~0.98 for 2 mg/mL iodine at 3.2 mGy, compared to approx. 0.9 for FBP and quadratic PL. Conclusions: Accurate material decomposition with DE-CBCT is feasible at low dose and benefits from a rigorous assessment of noise mechanisms among various reconstruction-based techniques. The work points to the potential for non-linear iterative reconstruction methods for high-quality decomposition at low material concentration and dose.

The performance of photon counting detectors (PCDs) are limited by the spectral distortion caused by charge sharing and pulse pileup. Recently we have proposed a new detector design called MEICC for multi-energy inter-pixel coincidence counter. MEICC records the counting signals in the same way as the current PCDs, while also recording the energy-dependent cross-talk with neighboring pixels. The previous studies have shown that MEICC decreases the effect of charge sharing significantly. Building on the previous success, the aim of this study was to assess the performance of MEICC when the pulse pileup also causes spectral distortion (in addition to charge sharing). Monte Carlo simulations have been performed to compare the performance of MEICC, the current PCD, and two different versions of real-time charge sharing correction schemes. The study found that MEICC had the best performance when count rates were moderate or high.

Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone-beam computed tomography is too slow (it takes 4–10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra-operative 4-dimensional soft tissue PErfusion using a standard x-ray system with No gantry rotation. In this simulation study, we provide a proof-of-concept that the core ideas work.

One of the challenges in photon counting detector (PCD)-based computed tomography (CT) is the spectral distortion in the PCD caused by physical effects such as charge sharing and K-escape fluorescence. This chapter summarizes for conventional approaches to compensate for the spectral distortion in PCD-CT and provides in great detail for a recently developed x-ray transmittance modeling-based three-step algorithm as a fast alternative to the maximum likelihood-based estimator. We describe both theoretical and practical aspects of the algorithm and also provide an example of two-material decomposition with practical implementations.

Purpose Smaller pixel sizes of x-ray photon counting detectors (PCDs) are advantageous for count rate capabilities but disadvantageous for charge sharing. With charge sharing, the energy of an x-ray photon may be split and one photon may produce two or more counts at adjacent pixels, both at lower energies than the incident energy. This "double-counting" increases noise variance and degrades the spectral response. Overall, it has a significantly negative impact on the performance of PCD-based computed tomography (CT). Charge sharing is induced by the detection physics and occurs regardless of count rates; thus, it is impossible to avoid. We propose in this paper a method that has a potential to address both noise and bias added by charge sharing. Methods We propose applying a multi-energy inter-pixel coincidence counter (MEICC) technique, which uses energy-dependent coincidence counters, keeps the book of charge sharing events during data acquisition, and provides the exact number of charge sharing occurrences, which can be used to either correct or compensate for them after the acquisition is completed. MEICC does not interfere with the primary counting process; therefore, PCDs with MEICC will remain as fast as those without MEICC. MEICC can be implemented using current electronics technology because its inter-pixel coincidence counters used to handle digital data are rather simple. We evaluated Cramer-Rao lower bound (CRLB) of PCDs with and without MEICC using a Monte Carlo simulation. Results When the number of energy windows was four or larger and eight neighboring pixels were used, the CRLBs of 225-µm PCD with MEICC normalized by those of the current PCD with the same number of windows were 0.361-0.383 for water density images of two basis functions, which was only 5.7-16.4% worse than those of a PCD without charge sharing (which were at 0.329-0.358). In contrast, the normalized CRLBs of the PCD with one coincidence counter were 0.466-0.499, which were 37.3-45.6% worse than the PCD without charge sharing. The use of eight neighboring pixels provided ~10% better CRLB values than four neighboring pixels for MEICC. With four energy windows, decreasing the number of coincidence counters from 16 to 9 only slightly increased the CRLB from 0.255 to 0.269 (which corresponded to as little as a 5.5% change). The normalized CRLBs of MEICC for K-edge imaging (gold) were 0.295-0.426, while those of the one coincidence counter were 0.926-0.959 and the ideal PCDs were 0.126-0.146. Conclusions The proposed MEICC provides spectral information that can be used to address charge sharing problems in PCDs and is expected to satisfy the requirements for clinical x-ray CT. MEICC is very effective, especially for K-edge imaging, which requires accurate spectral information.

Recently, multi-energy inter-pixel coincidence counter (MEICC) has been proposed for charge sharing correction and compensation for photon counting detectors (PCDs), which uses energy-dependent coincidence counters between a pixel-of-interest (POI) and its neighboring pixels and keeps the book of charge sharing events. A Monte Carlo (MC) simulation study was performed to assess the performance of MEICC; however, the performance might have been overestimated in a previous study. The charge sharing increases the number of photons recorded at a PCD pixel at the expense of the spatial resolution and the effect on the spatial resolution was not taken into account. The aims of this study were, first, to outline a spatially-resolved irradiation scheme for a normalized spatial resolution, and second, to assess the performance of MEICC. The MC program was modified to set the x-ray spectrum and intensity for the POI and those for the neighboring pixels separately. Data were then generated by varying two basis materials, water and bone, by a small amount only for the POI, while those for neighboring pixels remained unchanged. Cramer–Rao lower bound (CRLB) of water and bone estimation was computed from the changes in the mean and covariance matrix estimated. This setting allowed for assessing the CRLB at the same spatial resolution regardless of with or without charge sharing. The CRLBs at the normalized spatial resolution were worse than those at not-normalized spatial resolutions. With eight energy windows, MEICC provided the CRLB comparable to the ideal PCD with no charge sharing. The proposed MEICC has a potential to address charge sharing in PCDs.

Rationale and Objectives To investigate the stabilities of plaque attenuation and coronary lumen for different plaque types, stenotic degrees, lumen densities, and reconstruction methods using coronary vessel phantoms and the visualization of coronary plaques in clinical patients through coronary computed tomography (CT) angiography. Materials and Methods We performed 320-detector volume scanning of vessel tubes with stenosis and a tube without stenosis using three types of plaque CT numbers. The stenotic degrees were 50% and 75%. Images were reconstructed with filtered back projection (FBP) and two types of iterative reconstructions (AIDR3D and FIRST [forward-projected model-based iterative reconstruction solution]), with stenotic CT number of approximately 40, 80, and 150 HU (Hounsfield unit), respectively. In each case, the tubing of the coronary vessel was filled with diluted contrast material and distilled water to reach the target lumen CT numbers of approximately 350 HU and 450 HU, and 0 HU, respectively. Peak lumen and plaque CT numbers were measured to calculate the lumen–plaque contrast. In addition, we retrospectively evaluated the image quality with regard to coronary arterial lumen and the plaque in 10 clinical patients on a 4-point scale. Results At 50% stenosis, the plaque CT number with contrast enhancement increased for FBP and AIDR3D, and the difference in the plaque CT number with and without contrast enhancement was 15–44 HU for FBP and 10–31 HU for AIDR3D. However, the plaque CT number for FIRST had a smaller variation and the difference with and without contrast enhancement was −12 to 8 HU. The visual evaluation score for the vessel lumen was 2.8 ± 0.6, 3.5 ± 0.5, and 3.7 ± 0.5 for FBP, AIDR3D, and FIRST, respectively. Conclusions The FIRST method controls the increase in plaque density and the lumen–plaque contrast. Consequently, it improves the visualization of coronary plaques in coronary CT angiography.

Molecular-targeted therapy was recommended for the systemic therapy of renal cell cancer (RCC) in the RCC guidelines, but these guidelines do not address the order of administration of the multiple presently available agents. There are several aspects that remain unknown regarding the optimal administration order and combination of molecular-targeted drugs. Until the optimal treatment sequence is determined by clinical trials, treatment individualization is required for each patient based on patient and disease characteristics. We herein investigate 12 cases of RCC patients who received axitinib. Axitinib was used as the first-line drug in 4 cases, second-line in 5 cases, third-line in 1 case and as a fourth-line drug in 2 cases. Partial response (PR) was observed in 4 cases (30%) and stable disease in 4 cases (30%) during axitinib treatment, with an overall response rate of 60%. The duration of PR ranged from 6 to 19 months. Based on our cases, axitinib exhibited reasonable therapeutic efficacy as first- as well as second-line treatment. However, more cases are required to draw firm conclusions.

Photon counting detector (PCD)-based computed tomography exploits spectral information from a transmitted x-ray spectrum to estimate basis line-integrals. The recorded spectrum, however, is distorted and deviates from the transmitted spectrum due to spectral response effect (SRE). Therefore, the SRE needs to be compensated for when estimating basis line-integrals. One approach is to incorporate the SRE model with an incident spectrum into the PCD measurement model and the other approach is to perform a calibration process that inherently includes both the SRE and the incident spectrum. A maximum likelihood estimator can be used to the former approach, which guarantees asymptotic optimality; however, a heavy computational burden is a concern. Calibration-based estimators are a form of the latter approach. They can be very efficient; however, a heuristic calibration process needs to be addressed. In this paper, we propose a computationally efficient three-step estimator for the former approach using a low-order polynomial approximation of x-ray transmittance. The low-order polynomial approximation can change the original non-linear estimation method to a two-step linearized approach followed by an iterative bias correction step. We show that the calibration process is required only for the bias correction step and prove that it converges to the unbiased solution under practical assumptions. Extensive simulation studies validate the proposed method and show that the estimation results are comparable to those of the ML estimator while the computational time is reduced substantially.

Purpose: An x-ray photon interacts with photon counting detectors (PCDs) and generates an electron charge cloud or multiple clouds. The clouds (thus, the photon energy) may be split between two adjacent PCD pixels when the interaction occurs near pixel boundaries, producing a count at both of the pixels. This is called double-counting with charge sharing. (A photoelectric effect with K-shell fluorescence x-ray emission would result in double-counting as well). As a result, PCD data are spatially and energetically correlated, although the output of individual PCD pixels is Poisson distributed. Major problems include the lack of a detector noise model for the spatio-energetic cross talk and lack of a computationally efficient simulation tool for generating correlated Poisson data. A Monte Carlo (MC) simulation can accurately simulate these phenomena and produce noisy data; however, it is not computationally efficient. Methods: In this study, the authors developed a new detector model and implemented it in an efficient software simulator that uses a Poisson random number generator to produce correlated noisy integer counts. The detector model takes the following effects into account: (1) detection efficiency; (2) incomplete charge collection and ballistic effect; (3) interaction with PCDs via photoelectric effect (with or without K-shell fluorescence x-ray emission, which may escape from the PCDs or be reabsorbed); and (4) electronic noise. The correlation was modeled by using these two simplifying assumptions: energy conservation and mutual exclusiveness. The mutual exclusiveness is that no more than two pixels measure energy from one photon. The effect of model parameters has been studied and results were compared with MC simulations. The agreement, with respect to the spectrum, was evaluated using the reduced χ2 statistics or a weighted sum of squared errors, χred2(≥1), where χred2=1 indicates a perfect fit. Results: The model produced spectra with flat field irradiation that qualitatively agree with previous studies. The spectra generated with different model and geometry parameters allowed for understanding the effect of the parameters on the spectrum and the correlation of data. The agreement between the model and MC data was very strong. The mean spectra with 90 keV and 140 kVp agreed exceptionally well: χred2 values were 1.049 with 90 keV data and 1.007 with 140 kVp data. The degrees of cross talk (in terms of the relative increase from single pixel irradiation to flat field irradiation) were 22% with 90 keV and 19% with 140 kVp for MC simulations, while they were 21% and 17%, respectively, for the model. The covariance was in strong agreement qualitatively, although it was overestimated. The noisy data generation was very efficient, taking less than a CPU minute as opposed to CPU hours for MC simulators. Conclusions: The authors have developed a novel, computationally efficient PCD model that takes into account double-counting and resulting spatio-energetic correlation between PCD pixels. The MC simulation validated the accuracy.

A novel imaging technique ("X-map") has been developed to identify acute ischemic lesions for stroke patients using non-contrast-enhanced dual-energy computed tomography (NE-DE-CT). Using the 3-material decomposition technique, the original X-map ("X-map 1.0") eliminates fat and bone from the images, suppresses the gray matter (GM)-white matter (WM) tissue contrast, and makes signals of edema induced by severe ischemia easier to detect. The aim of this study was to address the following 2 problems with the X-map 1.0: (1) biases in CT numbers (or artifacts) near the skull of NE-DE-CT images and (2) large intrapatient and interpatient variations in X-map 1.0 values.We improved both an iterative beam-hardening correction (iBHC) method and the X-map algorithm. The new iBHC (iBHC2) modeled x-ray physics more accurately. The new X-map ("X-map 2.0") estimated regional GM values-thus, maximizing the ability to suppress the GM-WM contrast, make edema signals quantitative, and enhance the edema signals that denote an increased water density for each pixel. We performed a retrospective study of 11 patients (3 men, 8 women; mean age, 76.3 years; range, 68-90 years) who presented to the emergency department with symptoms of acute stroke. Images were reconstructed with the old iBHC (iBHC1) and the iBHC2, and biases in CT numbers near the skull were measured. Both X-map 2.0 maps and X-map 1.0 maps were computed from iBHC2 images, both with and without a material decomposition-based edema signal enhancement (ESE) process. X-map values were measured at 5 to 9 locations on GM without infarct per patient; the mean value was calculated for each patient (we call it the patient-mean X-map value) and subtracted from the measured X-map values to generate zero-mean X-map values. The standard deviation of the patient-mean X-map values over multiple patients denotes the interpatient variation; the standard deviation over multiple zero-mean X-map values denotes the intrapatient variation. The Levene F test was performed to assess the difference in the standard deviations with different algorithms. Using 5 patient data who had diffusion weighted imaging (DWI) within 2 hours of NE-DE-CT, mean values at and near ischemic lesions were measured at 7 to 14 locations per patient with X-map images, CT images (low kV and high kV), and DWI images. The Pearson correlation coefficient was calculated between a normalized increase in DWI signals and either X-map or CT.The bias in CT numbers was lower with iBHC2 than with iBHC1 in both high- and low-kV images (2.5 ± 2.0 HU [95% confidence interval (CI), 1.3-3.8 HU] for iBHC2 vs 6.9 ± 2.3 HU [95% CI, 5.4-8.3 HU] for iBHC1 with high-kV images, P0.01; 1.5 ± 3.6 HU [95% CI, -0.8 to 3.7 HU] vs 12.8 ± 3.3 HU [95% CI, 10.7-14.8 HU] with low-kV images, P0.01). The interpatient variation was smaller with X-map 2.0 than with X-map 1.0, both with and without ESE (4.3 [95% CI, 3.0-7.6] for X-map 2.0 vs 19.0 [95% CI, 13.3-22.4] for X-map 1.0, both with ESE, P0.01; 3.0 [95% CI, 2.1-5.3] vs 12.0 [95% CI, 8.4-21.0] without ESE, P0.01). The intrapatient variation was also smaller with X-map 2.0 than with X-map 1.0 (6.2 [95% CI, 5.3-7.3] vs 8.5 [95% CI, 7.3-10.1] with ESE, P = 0.0122; 4.1 [95% CI, 3.6-4.9] vs 6.3 [95% CI, 5.5-7.6] without ESE, P0.01). The best 3 correlation coefficients (R) with DWI signals were -0.733 (95% CI, -0.845 to -0.560, P0.001) for X-map 2.0 with ESE, -0.642 (95% CI, -0.787 to -0.429, P0.001) for high-kV CT, and -0.609 (95% CI, -0.766 to -0.384, P0.001) for X-map 1.0 with ESE.Both of the 2 problems outlined in the objectives have been addressed by improving both iBHC and X-map algorithm. The iBHC2 improved the bias in CT numbers and the visibility of GM-WM contrast throughout the brain space. The combination of iBHC2 and X-map 2.0 with ESE decreased both intrapatient and interpatient variations of edema signals significantly and had a strong correlation with DWI signals in terms of the strength of edema signals.

Smaller pixels sizes of x-ray photon counting detectors (PCDs) have the two conflicting effects as follows. On one hand, smaller pixel sizes improve the ability to handle high count rates of x-rays (i.e., pulse pileups) because incident rates onto each PCD pixel decreases with decreasing the size. On the other hand, smaller pixel sizes increase chances of crosstalk and double-counting (or n-tuple-counting in general) between neighboring pixels, because, while the same size of electron charge cloud generated by a photon is independent of PCD sizes, the charge cloud size relative to the PCD size increases with decreasing the PCD size. In addition, the following two aspects are practical configurations in actual PCD computed tomography systems: N×N-pixel binning and anti-scatter grids. When n-tuple-counting occurs and those data are binned/added during post-acquisition process, the variance of the data will be larger than its mean. The anti-scatter grids may eliminate or decrease the cross-talk and n-tuple-counting by blocking primary x-rays near pixel boundaries or for the width of one pixel entirely. In this study, we studied the effects of PCD pixel sizes, N×N-pixel binning, and pixel masking on soft tissue contrast visibility using newly developed Photon Counting Toolkit (PcTK version 3.2; https://pctk.jhu.edu).

Aims The aim of this article is to determine the association between left atrial appendage (LAA) regional dysfunction using image-based motion-estimation computed tomography (CT) (iME) and a prior history of stroke or transient ischaemic attack (TIA) in patients with atrial fibrillation (AF). Methods and results In this single-centre retrospective case–control study, among patients referred for AF ablation who underwent pre-ablation cardiac CT with retrospective ECG gating, we identified 18 patients with a prior history of stroke or TIA at the time of CT scan and 18 age- and gender-matched controls. All the patients were in sinus rhythm at the time of CT scan. Four-dimensional motion vector field was estimated from the CT images using iME. To assess myocardial deformation, area change ratio ( A ) and area change rate (AR) were calculated over the endocardial surface of the LAA. There was no significant difference in the baseline patient characteristics between the stroke/TIA group and the control group (67.6 ± 8.1 years old, 66.7% male, 16.7% persistent AF). LAA maximum ( A max; 23.8 ± 33.0 vs. 52.9 ± 41.2%, P = 0.02) and pre-atrial contraction area change ratio ( A preA; 13.7 ± 17.7 vs. 30.9 ± 29.2%, P = 0.04) were significantly lower in the stroke/TIA group than in the control group, respectively. The difference in LAA A max and A preA remained significant in multivariate analysis ( P = 0.03 and P = 0.04, respectively). Conclusion LAA regional dysfunction is associated with stroke/TIA in AF patients. Our results offer a basis for a prospective study to determine the role of LAA regional dysfunction by iME in predicting cerebrovascular events such as stroke or TIA.

Purpose: Newly developed spectral computed tomography (CT) such as photon counting detector CT enables more accurate tissue-type identification through material decomposition technique. Many iterative reconstruction methods, including those developed for spectral CT, however, employ a regularization term whose penalty transition is designed using pixel value of CT image itself. Similarly, the tissue-type identification methods are then applied after reconstruction; thus, it is impossible to take into account probability distribution obtained from projection likelihood. The purpose of this work is to develop comprehensive image reconstruction and tissue-type identification algorithm which improves quality of both reconstructed image and tissue-type map. Methods: The authors propose a new framework to jointly perform image reconstruction, material decomposition, and tissue-type identification for photon counting detector CT by applying maximum a posteriori estimation with voxel-based latent variables for the tissue types. The latent variables are treated using a voxel-based coupled Markov random field to describe the continuity and discontinuity of human organs and a set of Gaussian distributions to incorporate the statistical relation between the tissue types and their attenuation characteristics. The performance of the proposed method is quantitatively compared to that of filtered backprojection and a quadratic penalized likelihood method by 100 noise realization. Results: Results showed a superior trade-off between image noise and resolution to current reconstruction methods. The standard deviation (SD) and bias of reconstructed image were improved from quadratic penalized likelihood method: bias, −0.9 vs −0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of tissue-type identification was also improved from quadratic penalized likelihood method: 80.1% vs 86.9%. Conclusions: The proposed method makes it possible not only to identify tissue types more accurately but also to reconstruct CT images with decreased noise and enhanced sharpness owing to the information about the tissue types.

Purpose: The same projection data (or line integrals) are often measured multiple times, e.g., twice from opposite directions during one gantry rotation. The redundant data must be normalized by applying redundancy weighting such as the halfscan algorithm, which assumes that the noise of the data is uniform. This assumption, however, is not correct when a tube current modulation technique is employed. The variance of line integrals, which is inversely related to the tube current, could vary significantly. The purpose of this work is to improve how the projection data are used during analytical reconstruction when the tube current is modulated during the scan. Methods: The authors developed a new redundancy weighting scheme. It not only takes into account the data statistics but also can control how much to weigh the statistics from 100% (αs = 1.0) to 0% (αs = 0.0) by a parameter αs . The proposed weighting scheme reduces to the conventional redundancy weighting scheme when αs = 0.0. The authors evaluated the performance of the proposed scheme using computer simulations targeting at myocardial perfusion CT imaging. The image quality was evaluated in terms of the image noise and halfscan artifacts, and perfusion defect detection performance was evaluated by the positive predictive value (PPV) and the area-under-the-receiver operating characteristic-curve (AUC) value. Results: Results showed a tradeoff between the image noise and halfscan artifacts. The normalized noise standard deviation was 1.00 with halfscan, 0.89 with αs = 1.0, 0.97 with αs = 0.5, and 1.20 with αs = 0.0 when projections over one rotation (75% of projections are acquired with full dose, 25% with 1/10 of the full dose) are used. The halfscan artifacts were 13.4 Hounsfield unit (HU) with halfscan, 8.2 HU with αs = 1.0, 4.5 HU with αs = 0.5, and 3.1 HU with αs = 0.0. Both the PPVs and AUCs were improved from the halfscan method: PPV, 69.0%–70.6% vs 58.0%, P < 0.003; AUC, 0.935–0.938 vs 0.908, P < 0.003. Conclusions: The new redundancy weight allows for decreasing the image noise and controlling the tradeoff between the image noise and artifacts.

Purpose To investigate whether a newly-developed single-energy metal artifact reduction (SEMAR) algorithm applied to images acquired on a 320-MDCT volume scanner reduces image artifacts from dental metal. Methods We inserted the lower right teeth covered with a dental metal alloy and crown in a skull phantom and performed single-volume scanning on a second-generation 320-MDCT scanner. A 12-mm diameter spherical lesion was placed either close to or far from the dental metal. The tube voltage and current were 120 kVp and 80 or 155 mA, respectively. Images were reconstructed with filtered back projection (FBP) or iterative reconstruction (IR), with or without SEMAR. We calculated the signal-to-artifact ratios (SAR) to quantify the visibility of the lesion. Two radiologists inspected 96 images (48 with lesion and 48 without) for the presence or absence of the lesion using a 5-point ordinal scale (1 = definitely absent to 5 = definitely present). Results On images reconstructed with FPB and IR with SEMAR, streak artifacts from the dental metal were reduced substantially compared to images without SEMAR. At 155 mA with the lesion near the dental metal, the SARs were better on FBP and IR images (FBP: 1.7 and 0.5 with and without SEMAR, respectively; IR: 1.6 and 0.9 with and without SEMAR, respectively). The observer visual scores improved with SEMAR (FBP: 4.2 and 3.2 with and without SEMAR, respectively; IR: 4.2 and 3.0). Conclusion The SEMAR algorithm reduces dental metal artifacts and improves lesion detectability and image quality in patients with oral cavity lesions.

We report a histologically pure stage 1 seminoma with an elevated human chorionic gonadotropin (hCG). A 38 year-old man was referred for the evaluation of the left testicular swelling. He showed an elevated serum hCG level of 25,265 mIU/ml with normal a fetoprotein and lactate dehydrogenase. Imaging showed heterogeneous tumor without any metastatic lesions. We conducted 4 courses of chemotherapy before detecting hCG nadir. The final pathological report showed pure seminoma with syncytiotrophoblastic cells but no choriocarcinoma components. The patient remains disease free until present time. The case raised several questions regarding diagnosis and treatment strategy for bulky testicular seminoma.

Purpose To investigate the image quality characteristics for virtual monoenergetic images compared with conventional tube-voltage image with dual-layer spectral CT (DLCT). Methods Helical scans were performed using a first-generation DLCT scanner, two different sizes of acrylic cylindrical phantoms, and a Catphan phantom. Three different iodine concentrations were inserted into the phantom center. The single-tube voltage for obtaining virtual monoenergetic images was set to 120 or 140 kVp. Conventional 120- and 140-kVp images and virtual monoenergetic images (40–200-keV images) were reconstructed from slice thicknesses of 1.0 mm. The CT number and image noise were measured for each iodine concentration and water on the 120-kVp images and virtual monoenergetic images. The noise power spectrum (NPS) was also calculated. Results The iodine CT numbers for the iodinated enhancing materials were similar regardless of phantom size and acquisition method. Compared with the iodine CT numbers of the conventional 120-kVp images, those for the monoenergetic 40-, 50-, and 60-keV images increased by approximately 3.0-, 1.9-, and 1.3-fold, respectively. The image noise values for each virtual monoenergetic image were similar (for example, 24.6 HU at 40 keV and 23.3 HU at 200 keV obtained at 120 kVp and 30-cm phantom size). The NPS curves of the 70-keV and 120-kVp images for a 1.0-mm slice thickness over the entire frequency range were similar. Conclusion Virtual monoenergetic images represent stable image noise over the entire energy spectrum and improved the contrast-to-noise ratio than conventional tube voltage using the dual-layer spectral detector CT.

Photon counting detectors (PCDs) provide multiple energy-dependent measurements for estimating basis line-integrals. However, the measured spectrum is distorted from the spectral response effect (SRE) via charge sharing, K-fluorescence emission, and so on. Thus, in order to avoid bias and artifacts in images, the SRE needs to be compensated. For this purpose, we recently developed a computationally efficient three-step algorithm for PCD-CT without contrast agents by approximating smooth X-ray transmittance using low-order polynomial bases. It compensated the SRE by incorporating the SRE model in a linearized estimation process and achieved nearly the minimum variance and unbiased (MVU) estimator. In this paper, we extend the three-step algorithm to K-edge imaging applications by designing optimal bases using a low-rank approximation to model X-ray transmittances with arbitrary shapes (i.e., smooth without the K-edge or discontinuous with the K-edge). The bases can be used to approximate the X-ray transmittance and to linearize the PCD measurement modeling and then the three-step estimator can be derived as in the previous approach: estimating the x-ray transmittance in the first step, estimating basis line-integrals including that of the contrast agent in the second step, and correcting for a bias in the third step. We demonstrate that the proposed method is more accurate and stable than the low-order polynomial-based approaches with extensive simulation studies using gadolinium for the K-edge imaging application. We also demonstrate that the proposed method achieves nearly MVU estimator, and is more stable than the conventional maximum likelihood estimator in high attenuation cases with fewer photon counts.