Data In Energy Bins Research Articles

Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors.

Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store. This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors. We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density (0-40g/cm2 water, 0-2.16g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively. The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively. We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

Photon-counting-detector-computed tomography (PCD-CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K-edge escape, and pulse pile-up, which compromise spectral separation of multi-energy data and degrade multi-contrast imaging performance. The purpose of this work was to determine the performance of a dual-source (DS) PCD-CT relative to a single-source (SS) PCD-CT for the separation of simultaneously imaged I,Gd, andBi contrast agents. Phantom experiments were performed using a research whole-body PCD-CT and head/abdomen-sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS-PCD-CT, the phantoms were scanned twice on the SS-PCD-CT using different tube potentials for each scan. A tube potential of 80kV (energy thresholds=25/50keV) was used for low-energy tube, while the high-energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90keV. The same phantoms were scanned also on the SS-PCD-CT using the chess acquisition mode. In chess mode, the 4×4 subpixels within a macro detector pixel are split into two sets based on a chess-board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy-bin data sets, which permits simultaneous multi-contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre-defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS-PCD-CT. A 140kV tube potential and thresholds=25/50/75/90keV were used. These settings were determined based on the K-edges of Gd, andBi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy-bin images obtained from each scan (scan pair) were used to generate I-, Gd-, Bi-specific image via material decomposition. Root-mean-square-error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20-cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi-specific images. Phantom results showed that DS-PCD-CT reduced noise in material-specific images for both head and body phantoms compared to SS-PCD-CT. The noise level of SS-PCD was reduced from 2.55 to 0.90mg/mL (I), 1.97 to 0.78mg/mL (Gd), and 0.85 to 0.74mg/mL (Bi) using DS-PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS-PCD-CT was reduced relative to DS-PCD-CT from 10.52 to 2.76mg/mL (I), 7.90 to 2.01mg/mL (Gd), and 1.91 to 1.16mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS-PCD) to 0.72 (DS-PCD) mg/mL (I), 2.02 to 0.58mg/mL (Gd), and 0.85 to 0.57mg/mL (Bi). We demonstrate the feasibility of performing simultaneous imaging of I,Gd, andBi materials on DS-PCD-CT. Under the condition without cross scattering, DS-PCD reduced the RMSE for quantification of material concentration in relative to a SS-PCD-CT system using chess mode.

Spectral CT Reconstruction with Image Sparsity and Spectral Mean.

Photon-counting detectors can acquire x-ray intensity data in different energy bins. The signal to noise ratio of resultant raw data in each energy bin is generally low due to the narrow bin width and quantum noise. To address this problem, here we propose an image reconstruction approach for spectral CT to simultaneously reconstructs x-ray attenuation coefficients in all the energy bins. Because the measured spectral data are highly correlated among the x-ray energy bins, the intra-image sparsity and inter-image similarity are important prior acknowledge for image reconstruction. Inspired by this observation, the total variation (TV) and spectral mean (SM) measures are combined to improve the quality of reconstructed images. For this purpose, a linear mapping function is used to minimalize image differences between energy bins. The split Bregman technique is applied to perform image reconstruction. Our numerical and experimental results show that the proposed algorithms outperform competing iterative algorithms in this context.

Estimation of signal and noise for a whole-body photon counting research CT system.

Photon-counting CT (PCCT) may yield potential value for many clinical applications due to its relative immunity to electronic noise, increased geometric efficiency relative to current scintillating detectors, and the ability to resolve energy information about the detected photons. However, there are a large number of parameters that require optimization, particularly the energy thresholds configurations. Fast and accurate estimation of signal and noise in PCCT can benefit the optimization of acquisition parameters for specific diagnostic tasks. Based on the acquisition parameters and detector response of our research PCCT system, we derived mathematical models for both signal and noise. The signal model took the tube spectrum, beam filtration, object attenuation, water beam hardening, and detector response into account. The noise model considered the relationship between noise and radiation dose, as well as the propagation of noise as threshold data are subtracted to yield energy bin data. To determine the absolute noise value, a noise look-up table (LUT) was acquired using a limited number of calibration scans. The noise estimation algorithm then used the noise LUT to estimate noise for scans with a variety of combination of energy thresholds, dose levels, and object attenuation. Validation of the estimation algorithms was performed on our whole-body research PCCT system using semi-anthropomorphic water phantoms and solutions of calcium and iodine. The algorithms achieved accurate estimation of signal and noise for a variety of scanning parameter combinations. The proposed method can be used to optimize energy thresholds configuration for many clinical applications of PCCT.

TH-AB-204-07: Low Dose, Single-Step, X-Ray Phase Contrast Imaging with a Spectral Detection Scheme

Purpose: Widely popular x-ray interferometric x-ray phase contrast methods are known to introduce high radiation dose and require multiple measurement steps per projection to extract absorption and phase signatures from intensity measurements. A newer method like coded-aperture technique mitigates many drawbacks of an interferometric method but still requires multiple measures involving movement of the aperture masks to obtain separate absorption and phase information. This abstract explores a novel method to enable a single-step x-ray phase contrast imaging using spectral detection scheme in a coded-aperture setting. Methods: The proposed setup consists of a coded-aperture phase contrast imaging setup with a spectral detector. Spectral detection scheme allows detection of multiple energy-bin data simultaneously. We derived a new transport of intensity equation (TIE) that describes x-ray propagation and detection in such a setup. Using this new TIE equation in conjunction with a new retrieval algorithm we obtain a solution method for quantitatively accurate estimation of absorption, phase and differential phase contrast. A rigorous forward simulation was used to examine the retrieval accuracy. The phantom consisted of 5cm thick (50–50 density) breast phantom with thin tubes of PMMA, calcification spheres and cancerous tissue. An imaging dose of 1.5 mGy was used in the simulation. Results: Our results show excellent quantitative agreement for retrieved results in absorption, phase and differential phase contrast in comparison to the ground truth. Our method is the first single-step technique to achieve a low dose retrieval of multiple tissue properties. Conclusion: Our proposed method based on a non-interferometric method for spectral solutions to absorption, phase and DP has potential to mitigate existing hurdles in phase contrast imaging. Further investigations are underway in terms of bench top system development and understanding optimal choice of energy windows, detector pixel size and sensor thickness for the described method.

Optimal “image‐based” weighting for energy‐resolved CT

This paper investigates a method of reconstructing images from energy-resolved CT data with negligible beam-hardening artifacts and improved contrast-to-nosie ratio (CNR) compared to conventional energy-weighting methods. Conceptually, the investigated method first reconstructs separate images from each energy bin. The final image is a linear combination of the energy-bin images, with the weights chosen to maximize the CNR in the final image. The optimal weight of a particular energy-bin image is derived to be proportional to the contrast-to-noise-variance ratio in that image. The investigated weighting method is referred to as "image-based" weighting, although, as will be described, the weights can be calculated and the energy-bin data combined prior to reconstruction. The performance of optimal image-based energy weighting with respect to CNR and beam-hardening artifacts was investigated through simulations and compared to that of energy integrating, photon counting, and previously studied optimal "projection-based" energy weighting. Two acquisitions were simulated: dedicated breast CT and a conventional thorax scan. The energy-resolving detector was simulated with five energy bins. Four methods of estimating the optimal weights were investigated, including task-specific and task-independent methods and methods that require a single reconstruction versus multiple reconstructions. Results demonstrated that optimal image-based weighting improved the CNR compared to energy-integrating weighting by factors of 1.15-1.6 depending on the task. Compared to photon-counting weighting, the CNR improvement ranged from 1.0 to 1.3. The CNR improvement factors were comparable to those of projection-based optimal energy weighting. The beam-hardening cupping artifact increased from 5.2% for energy-integrating weighting to 12.8% for optimal projection-based weighting, while optimal image-based weighting reduced the cupping to 0.6%. Overall, optimal image-based energy weighting provides images with negligible beam-hardening artifacts and improved CNR compared to energy-integrating and photon-counting methods.

Acquisition of the effective lateral energy fluence distribution for photon beam dose calculations by convolution models

A method is described for acquiring the lateral energy fluence distribution of therapeutic photon beams. The method is designed for characterization of clinical accelerators for the convolution/superposition methods of photon dose calculations. Radial dose profiles, measured in a water phantom at a reference depth for the largest field size of the machine, are used to construct a discrete Cartesian dose matrix. A corresponding pencil beam energy deposition kernel is calculated from a database of Monte Carlo calculated kernels for monoenergetic photons by using the energy bin data of an effective spectrum as weights. The lateral kernel distribution at the depth of measurement is filtered and resampled into a discrete matrix, similar to that of the measured dose distribution. The dose matrix is then deconvolved by the kernel matrix, using fast Fourier transforms and Wiener filtering, to yield the effective energy fluence distribution. The method is tested by using the acquired energy fluence for convolution dose calculations and comparing the result with measured dose distributions.