Energy Discriminating Photon Counting Detector Research Articles

Multi-material spectral photon-counting micro-CT with minimum residual decomposition and self-supervised deep denoising.

Spectral micro-CT imaging with direct-detection energy discriminating photon counting detectors having small pixel size (< 100×100 µm2) is mainly hampered by: i) the limited energy resolution of the imaging device due to charge sharing effects and ii) the unavoidable noise amplification in the images resulting from basis material decomposition. In this work, we present a cone-beam micro-CT setup that includes a CdTe photon counting detector implementing a charge summing hardware solution to correct for the charge-sharing issue and an innovative image processing pipeline based on accurate modeling of the spectral response of the imaging system, an improved basis material decomposition (BMD) algorithm named minimum-residual BMD (MR-BMD), and self-supervised deep convolutional denoising. Experimental tomographic projections having a pixel size of 45×45 µm2 of a plastinated mouse sample including I, Ba, and Gd small cuvettes were acquired. Results demonstrate the capability of the combined hardware and software tools to sharply discriminate even between materials having their K-Edge separated by a few keV, such as e.g., I and Ba. By evaluating the quality of the reconstructed decomposed images (water, bone, I, Ba, and Gd), the quantitative performances of the spectral system are here assessed and discussed.

Dual energy X-ray beam ptycho-fluorescence imaging.

X-ray ptychography and X-ray fluorescence are complementary nanoscale imaging techniques, providing structural and elemental information, respectively. Both methods acquire data by scanning a localized beam across the sample. X-ray ptychography processes the transmission signal of a coherent illumination interacting with the sample, to produce images with a resolution finer than the illumination spot and step size. By enlarging both the spot and the step size, the technique can cover extended regions efficiently. X-ray fluorescence records the emitted spectra as the sample is scanned through the localized beam and its spatial resolution is limited by the spot and step size. The requisites for fast ptychography and high-resolution fluorescence appear incompatible. Here, a novel scheme that mitigates the difference in requirements is proposed. The method makes use of two probes of different sizes at the sample, generated by using two different energies for the probes and chromatic focusing optics. The different probe sizes allow to reduce the number of acquisition steps for the joint fluorescence-ptychography scan compared with a standard single beam scan, while imaging the same field of view. The new method is demonstrated experimentally using two undulator harmonics, a Fresnel zone plate and an energy discriminating photon counting detector.

An experimental method to correct low-frequency concentric artifacts in photon counting CT

Large-area photon counting detectors (PCDs) are usually built by tiling multiple semiconductor panels that often have slightly different spectral responses to input x-rays. As a result of this spectral inconsistency, experimental PCD-CT images of large, human-sized objects may show high-frequency ring artifacts and low-frequency band artifacts. Due to the much larger width of the bands compared with the rings, the concentric artifact problem in PCD-CT images of human-sized objects cannot be adequately addressed by conventional CT ring correction methods. This work presents an experimental method to correct the concentric artifacts in PCD-CT. The method is applicable to not only energy-discriminating PCDs with multiple bins but also PCDs with only a single threshold controller. Its principle is similar to the two-step beam hardening correction method, except that the proposed method uses pixel-specific polynomial functions to address the spectral inconsistency problem across the detector plane. The pixel-specific polynomial coefficients were experimentally calibrated using 15 acrylic sheets and 6 aluminum sheets of known thicknesses. The pixel-specific polynomial functions were used to convert the measured PCD-CT projection data to acrylic- and aluminum-equivalent thicknesses that are energy-independent. The proposed method was experimentally evaluated using a human cadaver head and multiple physical phantoms: two of them contain iodine and one phantom contains dual K-edge contrast materials (gadolinium and iodine). The results show that the proposed method can effectively remove the low-frequency concentric artifacts in PCD-CT images while reducing beam hardening artifacts. In contrast, the conventional CT ring correction algorithm did not adequately address the low-frequency band artifacts. Compared with the direct material decomposition-based correction method, the proposed method not only relaxes the requirement of multi-energy bins but also generates images with lower noise and fewer concentric artifacts.

Edge-subtraction X-ray ptychographic imaging with pink beam synchrotron radiation and a single photon-counting detector

We present here a new method of performing X-ray edge-subtraction ptychographic imaging by combining multiple harmonics from an undulator synchtrotron source and an energy discriminating photon counting detector. Conventionally, monochromatic far-field X-ray ptychography is used to perform edge subtraction through the use of multiple monochromatic energy scans to obtain spectral information for a variety of applications. Here, we use directly the undulator spectrum from a synchrotron source, selecting two separate harmonics post sample using the Pixirad-1/Pixie-III detector. The result is two monochromatic images, above and below an absorption edge of interest. The proposed method is applied to obtain Au L-edge subtraction imaging of a Au-Ni grid test sample. The Au L-edge subtraction is particularly relevant for the identification of gold nanoparticles for biomedical applications. Switching the energy scan mechanism from a mechanical monochromator to an electronic detector threshold allows for faster spectral data collection with improved stability.

Detective efficiency of photon counting detectors with spectral degradation and crosstalk.

Charge sharing and migration of scattered and fluorescence photons in an energy discriminating photon counting detector (PCD) degrade the detector's energy response and can cause a single incident photon to be registered as multiple events at different energies among neighboring pixels, leading to spatio-energetic correlation. Such a correlation in conventional linear, space-invariant imaging system can be usefully characterized by the frequency dependent detective quantum efficiency DQE(f). Defining and estimating DQE(f) for PCDs in a manner consistent with that of conventional detectors is complicated because the traditional definition of DQE(f) does not address spectral information. We introduce the concept of presampling spectroscopic detective quantum efficiency, DQEs (f), and present an analysis of it for CdTe PCDs using a spatial domain method that starts from a previously described analytic computation of spatio-energetic crosstalk. DQEs (f) is estimated as the squared signal-to-noise ratio of the amplitude of a small-signal sinusoidal modulation of the object (cortical bone) thickness at frequency f estimated using data from the detector under consideration compared that obtained from the photon distribution incident on the detector. DQEs for material decomposition (spectral) and effective monoenergetic imaging tasks for different pixel pitch is studied based on the multipixel Cramér-Rao lower bound (CRLB) that accounts for inter pixel basis material correlation. Effective monoenergetic DQEs is estimated from the CRLB of a linear weighted combination of basis materials, and its energy dependence is also studied. Zero frequency DQEs for the spectral task was ~18%, 25%, and 34% for 250μm, 500μm, and 1mm detector pixels respectively. Inter pixel signal correlation results in positive noise correlation between same basis material estimates of neighboring pixels, resulting in least impact on DQEs at the detector's Nyquist frequency. Effective monoenergetic DQEs (0) at the optimal energy is relatively tolerant of spectral degradation (85-91% depending on pixel size), but is highly dependent on the selected effective energy, with maximum variation (in 250μm pixels) of 17% to 85% for effective energy between 30 to 120keV. Our results show that spatio-energetic correlations degrade DQEs (f) beyond what is lost by poor spectral response in a single detector element. The positive correlation between computed single basis material values in neighboring pixels results in the penalty to DQEs (f) to be the least at the Nyquist frequency of the detector. It is desirable to reduce spectral degradation and crosstalk to minimize the impact on system performance. Larger pixels sizes have better spatio-energetic response due to lower charge sharing and escape of scatter and K-fluorescence photons, and therefore higher DQEs (0). Effective monoenergetic DQEs (0) at the optimal energy is much less affected by spectral degradation and crosstalk compared to DQEs for spectral tasks.

Framework for Photon Counting Quantitative Material Decomposition.

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm3, 100 mg/cm3, 250 mg/cm3, 400 mg/cm3), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT.

Improving pulse detection in multibin photon-counting detectors.

Energy-discriminating, photon-counting (EDPC) detectors are attractive for their potential for improved detective quantum efficiency and for their spectral imaging capabilities. However, at high count rates, counts are lost, the detected spectrum is distorted, and the advantages of EDPC detectors disappear. Existing EDPC detectors identify counts by analyzing the signal with a bank of comparators. We explored alternative methods for pulse detection for multibin EDPC detectors that could improve performance at high count rates. The detector signal was simulated in a Monte Carlo fashion assuming a bipolar shape and analyzed using several methods, including the conventional bank of comparators. For example, one method recorded the peak energy of the pulse along with the width (temporal extent) of the pulse. The Cramer-Rao lower bound of the variance of basis material estimates was numerically found for each method. At high count rates, the variance in water material (bone canceled) measurements could be reduced by as much as an order of magnitude. Improvements in virtual monoenergetic images were modest. We conclude that stochastic noise in spectral imaging tasks could be reduced if alternative methods for pulse detection were utilized.

The impact of the number of projections on image quality in Compton scatter tomography.

The availability of high resolution, energy discriminating photon counting detectors should make it possible to use Compton scattered photons to improve the diagnostic capability of computed tomography (CT). With high, spatial and energy resolution detectors Compton scatter tomography (CST) images of adequate quality can be obtained with a single projection. In practice, the limitations of realistic detectors require multiple projections for good quality images. The relationship between the number of projections used for reconstruction and the reconstructed image quality obtained for conventional CT does not necessarily apply to multi-projection Compton scatter tomography (MPCST). The purpose of this work was to investigate the dependence of the reconstructed image quality on the number of projections for MPCST. Analytical simulations and reconstructions were used to evaluate the contrast and spatial resolution for images reconstructed with one to 720 projections. Contrast-to-noise ratios (CNR) and the modulation transfer functions (MTF) demonstrated that the contrast increases monotonically with the number of projections while spatial resolution was independent of the number of projections. The contrast initially increases rapidly with projection number, becoming more gradual as the number of projections increase, with the rate of change being a function of fluence. The number of projections required to asymptotically approach the maximum contrast decreases as the fluence increases, with no indication of an optimal value for the range of fluences and projections investigated. For the projections considered, an increase in the number of projections increases the CNR even though the number of photons per projection decreases.

Investigation of energy weighting using an energy discriminating photon counting detector for breast CT

Breast CT is an emerging imaging technique that can portray the breast in 3D and improve visualization of important diagnostic features. Early clinical studies have suggested that breast CT has sufficient spatial and contrast resolution for accurate detection of masses and microcalcifications in the breast, reducing structural overlap that is often a limiting factor in reading mammographic images. For a number of reasons, image quality in breast CT may be improved by use of an energy resolving photon counting detector. In this study, the authors investigate the improvements in image quality obtained when using energy weighting with an energy resolving photon counting detector as compared to that with a conventional energy integrating detector. Using computer simulation, realistic CT images of multiple breast phantoms were generated. The simulation modeled a prototype breast CT system using an amorphous silicon (a-Si), CsI based energy integrating detector with different x-ray spectra, and a hypothetical, ideal CZT based photon counting detector with capability of energy discrimination. Three biological signals of interest were modeled as spherical lesions and inserted into breast phantoms; hydroxyapatite (HA) to represent microcalcification, infiltrating ductal carcinoma (IDC), and iodine enhanced infiltrating ductal carcinoma (IIDC). Signal-to-noise ratio (SNR) of these three lesions was measured from the CT reconstructions. In addition, a psychophysical study was conducted to evaluate observer performance in detecting microcalcifications embedded into a realistic anthropomorphic breast phantom. In the energy range tested, improvements in SNR with a photon counting detector using energy weighting was higher (than the energy integrating detector method) by 30%-63% and 4%-34%, for HA and IDC lesions and 12%-30% (with Al filtration) and 32%-38% (with Ce filtration) for the IIDC lesion, respectively. The average area under the receiver operating characteristic curve (AUC) for detection of microcalcifications was higher by greater than 19% (for the different energy weighting methods tested) as compared to the AUC obtained with an energy integrating detector. This study showed that breast CT with a CZT photon counting detector using energy weighting can provide improvements in pixel SNR, and detectability of microcalcifications as compared to that with a conventional energy integrating detector. Since a number of degrading physical factors were not modeled into the photon counting detector, this improvement should be considered as an upper bound on achievable performance.

Pulse pileup statistics for energy discriminating photon counting x‐ray detectors

Energy discriminating photon counting x-ray detectors can be subject to a wide range of flux rates if applied in clinical settings. Even when the incident rate is a small fraction of the detector's maximum periodic rate No, pulse pileup leads to count rate losses and spectral distortion. Although the deterministic effects can be corrected, the detrimental effect of pileup on image noise is not well understood and may limit the performance of photon counting systems. Therefore, the authors devise a method to determine the detector count statistics and imaging performance. The detector count statistics are derived analytically for an idealized pileup model with delta pulses of a nonparalyzable detector. These statistics are then used to compute the performance (e.g., contrast-to-noise ratio) for both single material and material decomposition contrast detection tasks via the Cramdr-Rao lower bound (CRLB) as a function of the detector input count rate. With more realistic unipolar and bipolar pulse pileup models of a nonparalyzable detector, the imaging task performance is determined by Monte Carlo simulations and also approximated by a multinomial method based solely on the mean detected output spectrum. Photon counting performance at different count rates is compared with ideal energy integration, which is unaffected by count rate. The authors found that an ideal photon counting detector with perfect energy resolution outperforms energy integration for our contrast detection tasks, but when the input count rate exceeds 20% N0, many of these benefits disappear. The benefit with iodine contrast falls rapidly with increased count rate while water contrast is not as sensitive to count rates. The performance with a delta pulse model is overoptimistic when compared to the more realistic bipolar pulse model. The multinomial approximation predicts imaging performance very close to the prediction from Monte Carlo simulations. The monoenergetic image with maximum contrast-to-noise ratio from dual energy imaging with ideal photon counting is only slightly better than with dual kVp energy integration, and with a bipolar pulse model, energy integration outperforms photon counting for this particular metric because of the count rate losses. However, the material resolving capability of photon counting can be superior to energy integration with dual kVp even in the presence of pileup because of the energy information available to photon counting. A computationally efficient multinomial approximation of the count statistics that is based on the mean output spectrum can accurately predict imaging performance. This enables photon counting system designers to directly relate the effect of pileup to its impact on imaging statistics and how to best take advantage of the benefits of energy discriminating photon counting detectors, such as material separation with spectral imaging.

Sci-Fri AM: Imaging - 05: Principal Component X-Ray Simulation Analysis of Breast Biopsy Phantoms

Multivariate principal component analysis (PCA) was performed on x‐ray simulations of breast biopsy phantoms. Semianalytic models were used to generate scatter (Ns) and photon transmission (N) signals from mixtures of fibrous (i), cancerous (j), and adipose tissue (k). A 5mm diameter 30 kV Mo beam is incident on a 5 mm thick biopsy. The incident exposure is 5.08 × 10−4 C/kg. An energy discriminating photon counting detector is assumed (e.g. CZT). For simulation of scattermeasurements, the detector is placed at a scatter angle θ=10°, whereas for transmission θ=0°. The distance from the biopsy center to a 5 mm diameter aperture above the detector is 15 cm. The variables used in the PCA are Ns/N0 (16 keV), Ns/N0 (25 keV) and N/N0 (8 keV) where N0 is the incident spectrum. These variables are calculated 10 times. Each trial consists of biopsy shuffling of tissue blocks for a chosen fractional composition (i,j,k). A statistical component was incorporated in No. As a preliminary test to the classification model, a total of 66 different biopsies have been simulated and are used as unknowns. The PCA classes generated are based on a biopsy composed of 5 layers and 5 radii. The 66 unknown compositions were tested against the PCA classes to determine if the biopsy tissue composition could be identified. Of the 66 biopsies, 46 were correctly identified. An example of a misclassified unknown was for the composition (0,0.2,0.8) which was classified as (0.3,0,0.7). Further work is required to increase accuracy of identification.