Experimental study of photon-counting CT neural network material decomposition under conditions of pulse pileup

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Improving model-data mismatch for photon-counting detector model using global and local model parameters

BACKGROUND

An energy-discriminating capability of a photon counting detector (PCD) can provide many clinical advantages, but several factors, such as charge sharing (CS) and pulse pileup (PP), degrade the capability by distorting the measured x-ray spectrum. To fully exploit the merits of PCDs, it is important to characterize the output of PCDs. Previously proposed PCD output models showed decent agreement with physical PCDs; however, there were still scopes to be improved: a global model-data mismatch and pixel-to-pixel variations.

PURPOSES

In this study, we improve a PCD model by using count-rate-dependent model parameters to address the issues and evaluate agreement against physical PCDs.

METHODS

The proposed model is based on the cascaded model, and we made model parameters condition-dependent and pixel-specific to deal with the global model-data mismatch and the pixel-to-pixel variation. The parameters are determined by a procedure for model parameter estimation with data acquired from different thicknesses of water or aluminum at different x-ray tube currents. To analyze the effects of having proposed model parameters, we compared three setups of our model: a model with default parameters, a model with global parameters, and a model with global-and-local parameters. For experimental validation, we used CdZnTe-based PCDs, and assessed the performance of the models by calculating the mean absolute percentage errors (MAPEs) between the model outputs and the actual measurements from low count-rates to high count-rates, which have deadtime losses of up to 24%.

RESULTS

The outputs of the proposed model visually matched well with the PCD measurements for all test data. For the test data, the MAPEs averaged over all the bins were 49.2-51.1% for a model with default parameters, 8.0-9.8% for a model with the global parameters, and 1.2-2.7% for a model with the global-and-local parameters.

CONCLUSION

The proposed model can estimate the outputs of physical PCDs with high accuracy from low to high count-rates. We expect that our model will be actively utilized in applications where the pixel-by-pixel accuracy of a PCD model is important.

[A comprehensive review on photon-counting computed tomography: Principles, technical hurdles and analysis of clinical applications]

In recent years, photon-counting computed tomography (PCD-CT) based on photon-counting detectors (PCDs) has become increasingly utilized in clinical practice. Compared with conventional CT, PCD-CT has the potential to achieve micron-level spatial resolution, lower radiation dose, negligible electronic noise, multi-energy imaging, and material identification, etc. This advancement facilitates the promotion of ultra-low dose scans in clinical scenarios, potentially detecting minimal and hidden lesions, thus significantly improving image quality. However, the current state of the art is limited and issues such as charge sharing, pulse pileup, K-escape and count rate drift remain unresolved. These issues could lead to a decrease in image resolution and energy resolution, while an increasing in image noise and ring artifact and so on. This article systematically reviewed the physical principles of PCD-CT, and outlined the structural differences between PCDs and energy integration detectors (EIDs), and the current challenges in the development of PCD-CT. In addition, the advantages and disadvantages of three detector materials were analysed. Then, the clinical benefits of PCD-CT were presented through the clinical application of PCD-CT in the three diseases with the highest mortality rate in China (cardiovascular disease, tumour and respiratory disease). The overall aim of the article is to comprehensively assist medical professionals in understanding the technological innovations and current technical limitations of PCD-CT, while highlighting the urgent problems that PCD-CT needs to address in the coming years.

Spectral Photon-Counting Computed Tomography: A Review on Technical Principles and Clinical Applications

Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.

Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment

Photon counting x-ray detectors (PCDs) with spectral capabilities have the potential to revolutionize computed tomography (CT) for medical imaging. The ideal PCD provides accurate energy information for each incident x-ray, and at high spatial resolution. This information enables material-specific imaging, enhanced radiation dose efficiency, and improved spatial resolution in CT images. In practice, PCDs are affected by non-idealities, including limited energy resolution, pulse pileup, and cross talk due to charge sharing, K-fluorescence, and Compton scattering. In order to maximize their performance, PCDs must be carefully designed to reduce these effects and then later account for them during correction and post-acquisition steps. This review article examines algorithms for using PCDs in spectral CT applications, including how non-idealities impact image quality. Performance assessment metrics that account for spatial resolution and noise such as the detective quantum efficiency (DQE) can be used to compare different PCD designs, as well as compare PCDs with conventional energy integrating detectors (EIDs). These methods play an important role in enhancing spectral CT images and assessing the overall performance of PCDs.