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] .

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] .

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] .

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] .

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

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] .

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] .

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] .

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.

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] .

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] .