Theoretical comparison of a dual energy system and photon counting silicon detector used for material quantification in spectral CT

Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

Cardiovascular Applications of Photon-Counting CT Technology: A Revolutionary New Diagnostic Step

Photon-counting computed tomography (PCCT) is an emerging technology that can potentially transform clinical CT imaging. After a brief description of the PCCT technology, this review summarizes its main advantages over conventional CT: improved spatial resolution, improved signal and contrast behavior, reduced electronic noise and artifacts, decreased radiation dose, and multi-energy capability with improved material discrimination. Moreover, by providing an overview of the existing literature, this review highlights how the PCCT benefits have been harnessed to enhance and broaden the diagnostic capabilities of CT for cardiovascular applications, including the detection of coronary artery calcifications, evaluation of coronary plaque extent and composition, evaluation of coronary stents, and assessment of myocardial tissue characteristics and perfusion.

Dual-Source Photon-Counting Computed Tomography-Part III: Clinical Overview of Vascular Applications beyond Cardiac and Neuro Imaging

Photon-counting computed tomography (PCCT) is an emerging technology that is expected to radically change clinical CT imaging. PCCT offers several advantages over conventional CT, which can be combined to improve and expand the diagnostic possibilities of CT angiography. After a brief description of the PCCT technology and its main advantages we will discuss the new opportunities brought about by PCCT in the field of vascular imaging, while addressing promising future clinical scenarios.

Photon-Counting Computed Tomography (PCCT): Technical Background and Cardio-Vascular Applications

Photon-counting computed tomography (PCCT) is a new advanced imaging technique that is going to transform the standard clinical use of computed tomography (CT) imaging. Photon-counting detectors resolve the number of photons and the incident X-ray energy spectrum into multiple energy bins. Compared with conventional CT technology, PCCT offers the advantages of improved spatial and contrast resolution, reduction of image noise and artifacts, reduced radiation exposure, and multi-energy/multi-parametric imaging based on the atomic properties of tissues, with the consequent possibility to use different contrast agents and improve quantitative imaging. This narrative review first briefly describes the technical principles and the benefits of photon-counting CT and then provides a synthetic outline of the current literature on its use for vascular imaging.

Photon-counting x-ray detectors for CT

The introduction of photon-counting detectors is expected to be the next major breakthrough in clinical x-ray computed tomography (CT). During the last decade, there has been considerable research activity in the field of photon-counting CT, in terms of both hardware development and theoretical understanding of the factors affecting image quality. In this article, we review the recent progress in this field with the intent of highlighting the relationship between detector design considerations and the resulting image quality. We discuss detector design choices such as converter material, pixel size, and readout electronics design, and then elucidate their impact on detector performance in terms of dose efficiency, spatial resolution, and energy resolution. Furthermore, we give an overview of data processing, reconstruction methods and metrics of imaging performance; outline clinical applications; and discuss potential future developments.

Review of Technical Advancements and Clinical Applications of Photon-counting Computed Tomography in Imaging of the Thorax

Photon-counting computed tomography (CT) is a developing technology that has the potential to address some limitations of CT imaging and bring about improvements and potentially new applications to this field. Photon-counting detectors have a fundamentally different detection mechanism from conventional CT energy-integrating detectors that can improve dose efficiency, spatial resolution, and energy-discrimination capabilities. In the past decade, promising human studies have been reported in the literature that have demonstrated benefits of this relatively new technology for various clinical applications. In this review, we provide a succinct description of the photon-counting detector technology and its detection mechanism in comparison with energy-integrating detectors in a manner understandable for clinicians and radiologists, introduce benefits and some of the existing challenges present in this technology, and provide an overview of the current status and potential clinical applications of this technology in imaging of the thorax by providing example images acquired with an investigational whole-body photon-counting CT scanner.

Next-Generation Hardware Advances in CT: Cardiac Applications

Impending major hardware advances in cardiac CT include three areas: ultra-high-resolution (UHR) CT, photon-counting CT, and phase-contrast CT. Cardiac CT is a particularly demanding CT application that requires a high degree of temporal resolution, spatial resolution, and soft-tissue contrast in a moving structure. In this review, cardiac CT is used to highlight the strengths of these technical advances. UHR CT improves visualization of calcified and stented vessels but may result in increased noise and radiation exposure. Photon-counting CT uses multiple photon energies to reduce artifacts, improve contrast resolution, and perform material decomposition. Finally, phase-contrast CT uses x-ray refraction properties to improve spatial and soft-tissue contrast. This review describes these hardware advances in CT and their relevance to cardiovascular imaging.

Dual Energy Differential Phase Contrast CT (DE-DPC-CT) Imaging

When more than two elemental materials are present in a given object, material quantification may not be robust and accurate when the routine two-material decomposition scheme in current dual energy CT imaging is employed. In this work, we present an innovative scheme to accomplish accurate three-material decomposition with measurements from a dual energy differential phase contrast CT (DE-DPC-CT) acquisition. A DE-DPC-CT system was constructed using a grating interferometer and a photon counting CT imaging system with two energy bins. The DE-DPC-CT system can simultaneously measure both the imaginary and the real part of the complex refractive index to enable a three-material decomposition. Physical phantom with 21 material inserts were constructed and measured using DE-DPC-CT system. Results demonstrated excellent accuracy in elemental material quantification. For example, relative root-mean-square errors of 4.5% for calcium and 5.2% for iodine were achieved using the proposed three-material decomposition scheme. Biological tissues with iodine inserts were used to demonstrate the potential utility of the proposed spectral CT imaging method. Experimental results showed that the proposed method correctly differentiates the bony structure, iodine, and the soft tissue in the biological specimen samples. A triple spectra CT scan was also performed to benchmark the performance of the DE-DPC-CT scan. Results demonstrated that the material decomposition from the DE-DPC-CT has a much lower quantification noise than that from the triple spectra CT scan.

Detective quantum efficiency of photon-counting CdTe and Si detectors for computed tomography: a simulation study

Purpose: Developing photon-counting CT detectors requires understanding the impact of parameters, such as converter material, thickness, and pixel size. We apply a linear-systems framework, incorporating spatial and energy resolution, to study realistic silicon (Si) and cadmium telluride (CdTe) detectors at a low count rate. Approach: We compared CdTe detector designs with 0.5 × 0.5    mm 2 and 0.225 × 0.225    mm 2 pixels and Si detector designs with 0.5 × 0.5    mm 2 pixels of 30 and 60 mm active thickness, with and without tungsten scatter blockers. Monte-Carlo simulations of photon transport were used together with Gaussian charge sharing models fitted to published data. Results: For detection in a 300-mm-thick object at 120 kVp, the 0.5- and 0.225-mm pixel CdTe systems have 28% to 41% and 5% to 29% higher detective quantum efficiency (DQE), respectively, than the 60-mm Si system with tungsten, whereas the corresponding numbers for two-material decomposition are 2% lower to 11% higher DQE and 31% to 54% lower DQE compared to Si. We also show that combining these detectors with dual-spectrum acquisition is beneficial. Conclusions: In the low-count-rate regime, CdTe detector systems outperform the Si systems for detection tasks, whereas silicon outperforms one or both of the CdTe systems for material decomposition.

Feasibility of unconstrained three-material decomposition: imaging an excised human heart using a prototype silicon photon-counting CT detector

Rationale and objectives

The purpose of this study was to evaluate the feasibility of unconstrained three-material decomposition in a human tissue specimen containing iodinated contrast agent, using an experimental multi-bin photon-counting silicon detector. It was further to evaluate potential added clinical value compared to a 1st-generation state-of-the-art dual-energy computed tomography system.

Materials and methods

A prototype photon-counting silicon detector in a bench-top setup for x-ray tomographic imaging was calibrated using a multi-material calibration phantom. A heart with calcified plaque was obtained from a deceased patient, and the coronary arteries were injected with an iodinated contrast agent mixed with gelatin. The heart was imaged in the experimental setup and on a 1st-generation state-of-the-art dual-energy computed tomography system. Projection-based three-material decomposition without any constraints was performed with the photon-counting detector data, and the resulting images were compared with those obtained from the dual-energy system.

Results

The photon-counting detector images show better separation of iodine and calcium compared to the dual-energy images. Additional experiments confirmed that unbiased estimates of soft tissue, calcium, and iodine could be achieved without any constraints.

Conclusion

The proposed experimental system could provide added clinical value compared to current dual-energy systems for imaging tasks where mix-up of iodine and calcium is an issue, and the anatomy is sufficiently small to allow iodine to be differentiated from calcium. Considering its previously shown count rate capability, these results show promise for future integration of this detector in a clinical CT scanner.

Key Points

• Spectral photon-counting detectors can solve some of the fundamental problems with conventional single-energy CT.

• Dual-energy methods can be used to differentiate iodine and calcium, but to do so must rely on constraints, since solving for three unknowns with only two measurements is not possible. Photon-counting detectors can improve upon these methods by allowing unconstrained three-material decomposition.

• A prototype photon-counting silicon detector with high count rate capability allows performing unconstrained three-material decomposition and qualitatively shows better differentiation of iodine and calcium than dual-energy CT.

Impact of prior information on material decomposition in dual- and multienergy computed tomography

Prior information is often included in the basis material decomposition to solve the quantification problem of three-material mixtures in dual-energy computed tomography (DECT). Multienergy computed tomography (MECT) with more than two energy bins can provide a sufficient solution to this problem without invoking additional prior information. However, a question remains as to whether the prior information should still be included in the material decomposition process using MECT to improve the quantification accuracy and control noise amplification. This study aims to evaluate the impact of the prior information on noise and quantification bias in both DECT and MECT. The material decomposition tasks we used in this study are to quantify water/iodine, water/iodine/gadolinium, and water/ iodine/calcium in two- and three-material decompositions, under the assumption that the object to be decomposed consists of the basis materials and their mixtures. We performed phantom simulation and experimental studies using a clinical DECT system and a research photon-counting-detector-based MECT system. Results in the current phantom studies show that the prior information can still improve the noise performance without substantially affecting the basis material quantitative accuracy during the material decomposition process, even when the number of x-ray energy beams/bins is equal or greater than the number of basis materials.

A framework for performance characterization of energy-resolving photon-counting detectors

PURPOSE

Photon-counting, energy-resolving detectors are subject to intense research interest, and there is a need for a general framework for performance assessment of these detectors. The commonly used linear-systems theory framework, which measures detector performance in terms of noise-equivalent quanta (NEQ) and detective quantum efficiency (DQE) is widely used for characterizing conventional x-ray detectors but does not take energy-resolving capabilities into account. The purpose of this work is to extend this framework to encompass energy-resolving photon-counting detectors and elucidate how the imperfect energy response and other imperfections in real-world detectors affect imaging performance, both for feature detection and for material quantification tasks.

METHOD

We generalize NEQ and DQE to matrix-valued quantities as functions of spatial frequency, and show how these matrices can be calculated from simple Monte Carlo simulations. To demonstrate how the new metrics can be interpreted, we compute them for simplified models of fluorescence and Compton scatter in a photon-counting detector and for a Monte Carlo model of a CdTe detector with 0.5 × 0.5 mm 2 pixels.

RESULTS

Our results show that the ideal-linear-observer performance for any detection or material quantification task can be calculated from the proposed generalized NEQ and DQE metrics. We also demonstrate that the proposed NEQ metric is closely related to a generalized version of the Cramér-Rao lower bound commonly used for assessing material quantification performance. Off-diagonal elements in the NEQ and DQE matrices are shown to be related to loss of energy information due to imperfect energy resolution. The Monte Carlo model of the CdTe detector predicts a zero-frequency dose efficiency relative to an ideal detector of 0.86 and 0.65 for detecting water and bone, respectively. When the task instead is to quantify these materials, the corresponding values are 0.34 for water and 0.26 for bone.

CONCLUSIONS

We have developed a framework for assessing the performance of photon-counting energy-resolving detectors and shown that the matrix-valued NEQ and DQE metrics contain sufficient information for calculating the dose efficiency for both detection and quantification tasks, the task having any spatial and energy dependence. This framework will be beneficial for the development and optimization of photon-counting x-ray detectors.

Photon-counting CT: Technical Principles and Clinical Prospects

Photon-counting CT is an emerging technology with the potential to dramatically change clinical CT. Photon-counting CT uses new energy-resolving x-ray detectors, with mechanisms that differ substantially from those of conventional energy-integrating detectors. Photon-counting CT detectors count the number of incoming photons and measure photon energy. This technique results in higher contrast-to-noise ratio, improved spatial resolution, and optimized spectral imaging. Photon-counting CT can reduce radiation exposure, reconstruct images at a higher resolution, correct beam-hardening artifacts, optimize the use of contrast agents, and create opportunities for quantitative imaging relative to current CT technology. In this review, the authors will explain the technical principles of photon-counting CT in nonmathematical terms for radiologists and clinicians. Following a general overview of the current status of photon-counting CT, they will explain potential clinical applications of this technology.

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dual-energy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all [Formula: see text]). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all [Formula: see text]) for FBP-Ramp images and 53.2% to 67.7% (all [Formula: see text]) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energy-dependent attenuation information of SCT. These methods can be further extended to other clinical tasks such as kidney or urinary stone identification applications.