Robust Energy Calibration Technique for Photon Counting Spectral Detectors

Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT

Purpose

Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence. Even if accurate spectral correction is done, multi-material separation within a volume remains a challenge. Increasing the number of energy bins in material decomposition problems often comes with a significant noise penalty but with minimal decomposition benefits.

Approach

We begin with an empirical spectral correction method executed in the tomographic domain that accounts for distortions in estimated spectral attenuation for each voxel. This is followed by our proposed iterative clustering material decomposition (ICMD) where clustering of voxels is used to reduce the number of basis materials to be resolved for each cluster. Using a larger number of energy bins for the clustering step shows distinct advantages in excellent classification to a larger number of clusters with accurate cluster centers when compared with the National Institute of Standards and Technology attenuation values. The decomposition step is applied to each cluster separately where each cluster has fewer basis materials compared with the entire volume. This is shown to reduce the need for the number of energy bins required in each decomposition step for the clusters. This approach significantly increases the total number of materials that can be decomposed within the volume with high accuracy and with excellent noise properties.

Results

Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a 55 - μ m pitch, we demonstrate the quantitatively accurate decomposition of several materials in a phantom study, where the sample includes mixtures of soft materials such as water and poly-methyl methacrylate along with contrast-enhancing materials. We show improved accuracy and lower noise when all five energy bins were used to yield effective classification of voxels into multiple accurate fundamental clusters which was followed by the decomposition step applied to each cluster using just two energy bins. We also show an example of biological sample imaging and separating three distinct types of tissue in mice: muscle, fat, and bone. Our experimental results show that the combination of effective and practical spectral correction and high-dimensional data clustering enhances decomposition accuracy and reduces noise in micro-CT.

Conclusions

This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.

Contrast and quantum noise in single-exposure dual-energy thoracic imaging with photon-counting x-ray detectors

Objective.Photon-counting x-ray detectors (PCDs) can produce dual-energy (DE) x-ray images of lung cancer in a single x-ray exposure. It is important to understand the factors that affect contrast, noise and the contrast-to-noise ratio (CNR). This study quantifies the dependence of CNR on tube voltage, energy threshold and patient thickness in single exposure, DE, bone-suppressed thoracic imaging with PCDs, and elucidates how the fundamental processes inherent in x-ray detection by PCDs contribute to CNR degradation.Approach.We modeled the DE CNR for five theoretical PCDs, ranging from an ideal PCD that detects every primary photon in the correct energy bin while rejecting all scattered radiation to a non-ideal PCD that suffers from charge-sharing and electronic noise, and detects scatter. CNR was computed as a function of tube voltage and high energy threshold for average and larger-than-average patients. Model predictions were compared with experimental data extracted from images acquired using a cadmium telluride (CdTe) PCD with two energy bins and analog charge summing for charge-sharing suppression. The imaging phantom simulated attenuation, scatter and contrast in lung nodule imaging. We quantified CNR improvements achievable with anti-correlated noise reduction (ACNR) and measured the range of exposure rates over which pulse pile-up is negligible.Main Results.The realistic model predicted overall trends observed in the experimental data. CNR improvements with ACNR were approximately five-fold, and modeled CNR-enhancements were on average within 10% of experiment. CNR increased modestly (i.e.<20%) when increasing the tube voltage from 90 kV to 130 kV. Optimal energy thresholds ranged from 50 keV to 70 keV across all tube voltages and patient thicknesses with and without ACNR. Quantum efficiency, electronic noise, charge sharing and scatter degraded CNR by ~50%. Charge sharing and scatter had the largest effect on CNR, degrading it by ~30% and ~15% respectively. Dead-time losses were less than 5% for patient exposure rates within the range of clinical exposure rates.Significance.In this study, we (1) employed analytical and computational models to assess the impact of different factors on CNR in single-exposure DE imaging with PCDs, (2) evaluated the accuracy of these models in predicting experimental trends, (3) quantified improvements in CNR achievable through ACNR and (4) determined the range of patient exposure rates at which pulse pile-up can be considered negligible. To the best of our knowledge, this study represents the first systematic investigation of single-exposure DE imaging of lung nodules with PCDs.

Transport-of-intensity model for single-mask x-ray differential phase contrast imaging

X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. The single-mask differential phase contrast imaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a model for a single-mask phase imaging system based on the transport-of-intensity equation. Our model provides an accessible understanding of signal and contrast formation in single-mask x-ray phase imaging, offering a clear perspective on the image formation process, for example, the origin of alternate bright and dark fringes in phase contrast intensity images. Aided by our model, we present an efficient retrieval method that yields differential phase contrast imagery in a single acquisition step. Our model gives insight into the contrast generation and its dependence on the system geometry and imaging parameters in both the initial intensity image as well as retrieved images. The model validity as well as the proposed retrieval method are demonstrated via both experimental results on a system developed in house as well as Monte Carlo simulations. In conclusion, our work not only provides a model for an intuitive visualization of image formation but also offers a method to optimize differential phase imaging setups, holding tremendous promise for advancing medical diagnostics and other applications.

The markerless lung target tracking AAPM Grand Challenge (MATCH) results

PURPOSE

Lung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study.

METHODS

MATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient-measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra-treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient-measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth.

RESULTS

A total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C-RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%.

CONCLUSIONS

A common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub-millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.

Quantitative phase retrieval with low photon counts using an energy resolving quantum detector

X-ray phase contrast imaging (PCI) combined with phase retrieval has the potential to improve soft-material visibility and discrimination. This work examined the accuracy, image quality gains, and robustness of a spectral phase retrieval method proposed by our group. Spectroscopic PCI measurements of a physical phantom were obtained using state-of-the-art photon-counting detectors in combination with a polychromatic x-ray source. The phantom consisted of four poorly attenuating materials. Excellent accuracy was demonstrated in simultaneously retrieving the complete refractive properties (photoelectric absorption, attenuation, and phase) of these materials. Approximately 10 times higher SNR was achieved in retrieved images compared to the original PCI intensity image. These gains are also shown to be robust against increasing quantum noise, even for acquisition times as low as 1 s with a low-flux microfocus x-ray tube (average counts of 250 photons/pixels). We expect that this spectral phase retrieval method, adaptable to several PCI geometries, will allow significant dose reduction and improved material discrimination in clinical and industrial x-ray imaging applications.

Spectral Signatures of X-ray Scatter Using Energy-Resolving Photon-Counting Detectors

Energy-resolving photon-counting detectors (PCDs) separate photons from a polychromatic X-ray source into a number of separate energy bins. This spectral information from PCDs would allow advancements in X-ray imaging, such as improving image contrast, quantitative imaging, and material identification and characterization. However, aspects like detector spectral distortions and scattered photons from the object can impede these advantages if left unaccounted for. Scattered X-ray photons act as noise in an image and reduce image contrast, thereby significantly hindering PCD utility. In this paper, we explore and outline several important characteristics of spectral X-ray scatter with examples of soft-material imaging (such as cancer imaging in mammography or explosives detection in airport security). Our results showed critical spectral signatures of scattered photons that depend on a few adjustable experimental factors. Additionally, energy bins over a large portion of the spectrum exhibit lower scatter-to-primary ratio in comparison to what would be expected when using a conventional energy-integrating detector. These important findings allow flexible choice of scatter-correction methods and energy-bin utilization when using PCDs. Our findings also propel the development of efficient spectral X-ray scatter correction methods for a wide range of PCD-based applications.