Clinical Advances and Challenges in Photon Counting CT: A State of the Art Review

Ao Liu¹, Yixuan An¹, Zhi Kang Ng¹, Shuang Bai^2*, Ruijun Wang^3*

¹Diagnostic Imaging and Radiotherapy Program, Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
²Department of Dermatology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
³Department of Neurosurgery, The Affiliated Hospital, Inner Mongolia Medical University, Hohhot, China.

DOI: 10.4236/act.2025.141001                

Abstract

Photon-counting computed tomography (PCCT) is a novel imaging technology that uses photon-counting detectors (PCDs) to overcome several limitations of conventional energy-integrating CT systems. By counting individual photons and sorting them by energy, PCCT enables higher spatial resolution, better contrast-to-noise ratio, and multi-energy imaging within a single scan. These advantages allow for improved visualization of fine anatomical structures, enhanced tumor and vascular imaging, and significant radiation dose reduction, particularly valuable in pediatric and chronic disease contexts. Clinically, PCCT has shown promise in cardiovascular assessment, oncology imaging, neurodiagnostics, and interstitial lung disease detection. However, its widespread clinical adoption is limited by high equipment costs, increased computational demands, and the lack of standardized imaging protocols. Current research efforts are focused on advanced contrast agents, AI-based diagnostic tools, and broader deployment strategies. This article reviews the core technical principles, clinical benefits, challenges, and future directions of PCCT as it moves toward routine clinical use.

Keywords

Photon-Counting Computed Tomography, Diagnostic Imaging, CT Imaging, Clinical Application

1. Introduction

Computed tomography (CT) has been used in hospitals since the 1970s. It helps doctors get quick and clear images of the inside of the body. This is useful for many medical problems, like injuries, cancer, or infections [1]. But most CT machines still use a method called energy-integrating detectors (EIDs). These machines collect all the energy from X-rays together, without telling how strong each photon is. Because of this, the images might not be very sharp, and they can have more noise [2]. Also, they can’t tell different materials apart very well [3] [4].

EIDs also have a problem with high-energy photons. Since all the energy is added together, some strong signals can be missed or mixed, making the image less accurate. This is especially true when metal is inside the body, like implants, which can cause streaks in the image [5].

A newer type of CT, called photon-counting CT (PCCT), tries to fix these problems. It uses Photon-Counting Detectors (PCDs), which can see each photon one by one and tell their energy levels. This helps give clearer images, cuts down on noise, and can show different materials more clearly in one scan [6].

PCCT brings a few big improvements. First, it can make sharper images, which helps doctors see small things like tiny blood vessels, lung nodules, or bone details [6]-[8]. Second, it can lower the amount of radiation needed, which is very helpful for pediatric patients or patients who need many scans [7] [9]. Third, it can show energy differences in one scan [10]. That means it can help tell the difference between things like calcium and iodine, or make special images that look like one energy level [11].

This article reviews the workings of PCCT, highlights its advantages over conventional CT and discusses its current clinical applications. It also outlines the challenges that currently exist in the field and considers future developments.

2. Technical Principles of PCCT

2.1. Basic Working Principle

Figure 1. Structural Comparison of EIDs and PCDs in X-ray Detection.

As shown in Figure 1, unlike conventional CT systems that use EIDs, PCCT uses PCDs to detect and process X-ray signals differently. In EIDs, all incoming X-ray photons are converted into light and then into an electric charge. The charges from all photons are summed up, and the total energy is measured [10]. However, this method does not separate photons based on their energy, and it also includes electronic noise [3].

In contrast, PCDs count each photon one by one and measure its energy [6]. These detectors are made of semiconductors like CdTe or CZT, which create a strong electric signal when a photon is absorbed [12]. The signal is then sorted into energy bins. This means PCCT can detect both the number of photons and their energy levels, providing more detailed and useful image data [13].

2.2. Key Advantage Mechanisms

PCCT offers several key improvements due to how PCDs work. First, it reduces electronic noise by setting an energy threshold, so low-energy background signals are ignored. This helps produce cleaner images even at low radiation doses [14]. Second, PCDs have smaller pixel sizes, which increases spatial resolution and helps detect small anatomical features like lung nodules, microcalcifications, or vessel walls [7]. Third, PCDs are more efficient at detecting photons because they avoid signal loss from processes like light conversion and scattering, which happen in EIDs [6].

2.3. Spectral Imaging Capability

One of the most important features of PCCT is its ability to perform spectral (multi-energy) imaging. Because PCDs can sort photons by energy, PCCT can provide virtual monoenergetic images, material decomposition, and even K-edge imaging [15]. This helps distinguish materials like iodine and calcium, reduces beam-hardening artifacts, and improves contrast between soft tissues [16].

2.4. Technical Challenges

Despite its advantages, PCCT still faces several technical limitations. One is charge sharing (Figure 2), where the signal from a single photon spreads into more than one pixel, which can lower the image accuracy [17]. Another issue is pulse pile-up (Figure 3), which happens when multiple photons arrive at the same detector area too quickly, causing missed or incorrect counts [18].

Figure 2. Charge sharing.

Figure 3. Pulse pile-up.

Also, the readout electronics for PCDs need to process very fast and accurate signals, which makes the system complex and expensive [19] [20].

3. Clinical Advances and Applications

3.1. Routine and Enhanced Imaging Performance

One of the main benefits of PCCT is its ability to improve image quality in standard and contrast-enhanced scans. With smaller detector pixels, PCCT offers higher spatial resolution, which helps in visualizing small structures like cranial sutures, tiny lung nodules, and coronary artery calcifications [21]-[24]. This is especially helpful in bone imaging, lung cancer screening, and cardiac CT.

PCCT also improves contrast-enhanced imaging. By reducing noise and improving signal separation, it increases the contrast-to-noise ratio (CNR), making it easier to see tumors, blood vessels, and soft tissue borders [13] [16] [23] [25]. This is important in liver and abdominal imaging, as well as in angiography.

Another important strength of PCCT is noise reduction and low-dose imaging. Thanks to the low electronic noise of PCDs and energy thresholds, high-quality images can be obtained with much less radiation [26]. This makes PCCT suitable for patients who need repeated scans, including children and patients with chronic lung disease [27] [28].

3.2. New Diagnostic Capabilities

PCCT enables advanced techniques like material decomposition and virtual monoenergetic imaging (VMI) [13]. These tools help differentiate between iodine, calcium, soft plaques, and hemorrhage. For example, it can separate iodine contrast from calcification in coronary arteries or identify active bleeding more precisely in trauma scans [23] [24].

Technology also helps with artifact reduction. PCDs can reduce beam hardening and metal artifacts due to their spectral capabilities and improved energy discrimination [29]. This is useful for patients with implants, orthopedic hardware, or dental fillings that often cause streak artifacts in conventional CT [30] [31].

3.3. Disease-Specific Applications

In cardiovascular imaging, PCCT offers high spatial resolution and better visualization of small vessels, stents, and atherosclerotic plaques [24] [28]. It can also quantify calcium more accurately and visualize soft plaques in the coronary arteries [8] [23] [32]. Beyond the enhancement of quality, PCCT’s capacity to provide quantifiable measurements, such as precise coronary calcium scoring and iodine concentration mapping, further sets it apart from conventional CT modalities [33] [34].

In oncology, PCCT can assist in tumor detection, staging, and therapy response evaluation [35]. Spectral imaging can help identify iodine uptake differences between tumor types and track treatment-related changes over time [36].

In interstitial lung disease (ILD), PCCT improves visualization of lung parenchyma and early fibrosis [37]. It has shown value in systemic sclerosis and other connective tissue diseases, where subtle ILD patterns need to be detected early [38].

In neuroimaging, PCCT helps distinguish between gray and white matter more clearly, which is important for brain structure analysis [39]. It also improves the detection and differentiation of intracranial hemorrhage and calcifications, which can appear similar in conventional CT [40].

4. Clinical Barriers

Despite its many advantages, PCCT still faces several important barriers that limit its widespread adoption in clinical practice.

4.1. Cost and System Availability

One of the main limitations is the high cost of PCCT systems, both in terms of hardware and ongoing maintenance [41]. The advanced semiconductor detectors (e.g., CdTe or CZT), cooling systems, and high-speed electronics significantly increase the manufacturing cost compared to traditional CT scanners. As a result, only a limited number of hospitals have installed PCCT units, often for research purposes or specialized applications [42]. This restricts equitable access and delays broader clinical validation in general hospital settings.

4.2. Data Volume and Image Reconstruction

Another challenge is the large amount of image data generated by PCCT. Because PCDs detect and store energy-specific photon information, the data sets can be several times larger than those of EID-CT [6] [43]. In particular, the massive data size may place substantial strain on Picture Archiving and Communication Systems (PACS) and hospital data networks that were not originally designed to handle such high-throughput imaging workflows [44]. This increases the need for high-speed data transfer, stronger computational power, and more advanced reconstruction algorithms, especially when spectral decomposition and multi-energy outputs are required [36].

This also brings a burden for image archiving and processing pipelines, which may not yet be optimized in many radiology departments [45].

4.3. Standardization and Protocol Limitations

The lack of standardized clinical protocols is another important obstacle. While many early studies have shown promise in specific applications, PCCT currently lacks broad multi-center validation, and no unified international guidelines exist for its routine diagnostic use [46] [47].

Furthermore, differences in vendor technologies, reconstruction techniques, and energy bin settings create variability across systems, which complicates data interpretation and clinical decision-making [47]. The absence of large-scale prospective trials also makes it difficult to fully define PCCT’s role in existing diagnostic workflows [41].

5. Future Perspectives

One exciting direction in PCCT development is the use of new contrast agents, especially those designed for K-edge imaging [48]. These agents, such as gadolinium- or gold-based compounds, allow specific energy-based detection using PCCT’s spectral capability [49]. This may enable more precise vascular, tumor, or perfusion imaging than current iodine-based contrast agents.

The development of deep learning models is focused on addressing detector-related issues, including pulse pile-up and charge sharing. This aims to enhance photon energy discrimination and spectral fidelity [50].

Another promising area is the integration of artificial intelligence into PCCT workflows. Machine learning models can process multi-parametric spectral data to support lesion classification, risk stratification, and even outcome prediction. Combining PCCT and AI may improve diagnostic accuracy and allow more personalized medical decision-making [45].

Ongoing research and development focus on improving detector materials, such as low-fluorescence semiconductors, as well as applying deep learning techniques to optimize image reconstruction, denoise spectral channels, and assist in data compression [51] [52]. These efforts aim to make PCCT faster, more efficient, and easier to integrate into everyday workflows.

6. Conclusions

PCCT represents a significant advancement in diagnostic imaging. Compared to conventional CT, PCCT provides higher image quality, lower radiation doses and superior spectral capabilities. This makes it suitable for a wide range of clinical applications, including neurology, oncology, and cardiology.

Although challenges remain, including cost, data complexity, and a lack of standardization, early clinical studies have confirmed its transformational potential. As research continues and systems become more accessible, PCCT is expected to play an increasingly important role in precision imaging and personalized care.

Funding

Supported by Projects of Inner Mongolia Medical University (YKD2022ZD002); Inner Mongolia Autonomous Region Health Commission Medical Hygiene Science Project (202201307).

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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