Published online May 28, 2026. doi: 10.4329/wjr.v18.i5.119048
Revised: February 23, 2026
Accepted: April 15, 2026
Published online: May 28, 2026
Processing time: 129 Days and 2.6 Hours
Although photon-counting computed tomography (PCCT) has demonstrated superior dose efficiency and image quality compared with conventional energy-integrating detector computed tomography (EID-CT) in theory and in some clinical fields, systematic evaluations of its performance in abdominal imaging, particularly comprehensive studies incorporating both radiation dose and image quality, remain very limited.
To compare radiation dose and image quality between PCCT and conventional EID-CT in abdominal imaging, and to evaluate the potential of PCCT for low-dose, high-quality clinical applications.
Eighty participants were included, comprising 40 subjects who underwent pro
Compared with EID-CT, PCCT achieved an approximate 50% reduction in CTDIvol, dose-length product, and ef
PCCT provides substantially improved dose efficiency and image quality compared with conventional EID-CT in abdominal imaging, supporting its potential role in low-dose clinical computed tomography protocols.
Core Tip: Photon-counting computed tomography (PCCT) is an emerging technology that offers superior dose efficiency and image quality compared to conventional energy-integrating detector computed tomography. In this abdominal imaging study, PCCT achieved approximately 50% reduction in radiation dose while significantly improving both objective and subjective image quality. These findings highlight the potential of PCCT to enable low-dose, high-quality clinical protocols, representing a significant advancement in diagnostic imaging.
- Citation: Zhu TT, Zhu XY, Yang M, Liu WM, Yang WJ, Liu X. Photon-counting computed tomography enables low-dose, high-quality abdominal imaging: A comparative study with energy-integrating detector computed tomography. World J Radiol 2026; 18(5): 119048
- URL: https://www.wjgnet.com/1949-8470/full/v18/i5/119048.htm
- DOI: https://dx.doi.org/10.4329/wjr.v18.i5.119048
Computed tomography (CT) is indispensable for abdominal imaging, providing rapid and comprehensive assessment of a wide range of diseases. Nevertheless, radiation exposure remains a major concern, particularly for patients requiring repeated examinations or long-term surveillance[1-3]. Conventional energy-integrating detector CT (EID-CT) is inherently limited in low-dose settings by electronic noise, reduced contrast resolution, and suboptimal visualization of subtle soft-tissue structures[4,5].
Photon-counting CT (PCCT) represents a paradigm shift in CT detector technology by directly counting individual photons and discriminating their energy[6,7]. In contrast to EID-CT, PCCT eliminates electronic noise and enables energy-weighted image reconstruction, theoretically allowing improved contrast resolution, enhanced spatial resolution, and superior dose efficiency[8-10]. Early clinical investigations have reported promising results for PCCT in car
However, systematic evaluations of PCCT performance in abdominal imaging remain limited. In particular, comprehensive comparisons incorporating radiation dose metrics, dose-normalized objective image quality indices, and subjective radiologist assessments under clinically relevant protocols are scarce[15-17]. Addressing these gaps is essential to clarify the clinical value of PCCT for abdominal applications[18].
Therefore, the aim of this study was to systematically compare radiation dose and image quality between PCCT and conventional EID-CT in abdominal imaging. By integrating objective quantitative metrics and subjective evaluations, we sought to assess whether PCCT can achieve low-dose imaging without compromising diagnostic image quality.
This study included 80 subjects who underwent abdominal CT examinations at Ruijin Hospital. The experimental group comprised 40 healthy volunteers prospectively enrolled for abdominal PCCT, whereas the control group consisted of 40 subjects retrospectively identified who had undergone routine non-contrast abdominal EID-CT. We have clarified that frequency matching was used: EID-CT controls were selected from the database based on age, sex, and body mass index (BMI) to match PCCT subjects. When multiple matches existed, the one with the closest BMI and age was selected, ensuring balanced baseline characteristics between groups (Table 1). The study workflow and patient selection process are illustrated in Figure 1.
| Variable | PCCT | EID-CT | P value |
| Gender | 0.8222 | ||
| Male | 17 (42.5) | 19 (47.5) | |
| Female | 23 (57.5) | 21 (52.5) | |
| Age | 42.58 ± 15.49 | 47.68± 12.0 | 0.056 |
| Height (cm) | 165.85 ± 8.66 | 163.15 ± 7.71 | 0.145 |
| Weight (kg) | 61.00 (57.50-72.00) | 65.00 (56.50-73.00) | 0.769 |
| BMI (kg/m2) | 23.49 ± 3.43 | 24.39 ± 4.28 | 0.299 |
The study protocol was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (approval No. 2025-250). Written informed consent was obtained from all prospectively enrolled participants in the PCCT group, while the requirement for informed consent was waived for the retrospective EID-CT group in accordance with institutional regulations.
Inclusion criteria were as follows: (1) Age between 18 and 75 years; (2) Abdominal CT examination performed for evaluation of abdominal organs, including the liver, pancreas, spleen, kidneys, or other abdominal structures; (3) Availability of complete and technically adequate CT images suitable for quantitative image quality analysis; and (4) Availability of complete demographic and radiation dose information.
Exclusion criteria included: (1) Pregnancy or lactation at the time of CT examination; (2) Prior high-dose abdominal radiotherapy or multiple recent CT examinations that could substantially affect radiation dose assessment; (3) Presence of abdominal metallic implants, severe motion artifacts, or other factors significantly compromising image quality; (4) Psychiatric or neurological disorders impairing cooperation with imaging procedures; (5) Incomplete imaging or clinical data precluding reliable analysis; and (6) Any other condition deemed inappropriate for inclusion by the investigators.
Abdominal PCCT examinations were performed using a photon-counting detector CT system (uCT Ultima, United Imaging Healthcare, Shanghai, China). Scanning was conducted in a low-dose helical mode, covering the region from the upper margin of the diaphragm to the lower margin of the pubic symphysis. All scans were acquired at a tube voltage of 120 kVp with automatic tube current modulation. The detector operated in photon-counting mode, enabling high spatial resolution imaging.
Images were reconstructed with slice thicknesses of 0.208 mm and 1 mm at corresponding intervals. A pitch of 0.9844 and a gantry rotation time of 0.5 seconds were applied. Iterative reconstruction combined with a deep learning-based reconstruction algorithm was used to optimize image quality under low-dose conditions. Reconstruction kernels included a standard body kernel (BodyStand) and a deep learning denoising kernel (DL-Denoise3). Image matrices were set to 512 × 512 for 1-mm slices and 1024 × 1024 for 0.208-mm slices. Automatic dose modulation and dose optimization functions were enabled throughout all examinations to ensure diagnostic image quality while minimizing radiation exposure.
Routine abdominal EID-CT examinations were performed using a dual-source CT scanner (SOMATOM Force, Siemens Healthineers, Forchheim, Germany). Scans were acquired in helical mode at a tube voltage of 120 kVp with automatic tube current modulation (CARE Dose 4D), covering the abdomen from the upper margin of the diaphragm to the lower margin of the pubic symphysis.
Images were reconstructed with a slice thickness and interval of 1 mm. A pitch ranging from 0.9 to 1.2 and a gantry rotation time of 0.25 seconds were used according to routine clinical abdominal protocols. Iterative reconstruction was performed using the ADMIRE algorithm (strength level 3-5) with a standard abdominal kernel (Br40). The image matrix was set to 512 × 512. Tube voltage and current were automatically adjusted using CARE kV and CARE Dose 4D systems to balance image quality and radiation dose in accordance with standard clinical practice.
Basic clinical and demographic information was collected for all enrolled subjects. In the prospective PCCT cohort, clinical data were obtained through on-site interviews and standardized data collection forms completed at the time of CT examination. In the retrospective EID-CT cohort, corresponding clinical information was retrieved from the electronic medical record and picture archiving and communication system databases. Collected variables included imaging identification number, gender, age, height, weight, and BMI. BMI was calculated as weight (kg) divided by height squared (m2).
Radiation dose information was automatically recorded by the CT scanner and extracted from the DICOM metadata. Dose parameters included the volume CT dose index (CTDIvol, mGy), dose-length product (DLP, mGy × cm), and effective dose (ED, mSv). The ED was calculated using the following formula: ED = DLP × k, where k represents the abdominal conversion coefficient (0.015 mSv × mGy-1 × cm-1). All dose parameters were obtained using the same scanner and acquisition protocol to ensure measurement consistency.
Image quality assessment was based on mean CT attenuation values of the liver and subcutaneous fat (meanliver and meanfat) and their corresponding SDs (SDliver and SDfat). Region-of-interest (ROI) measurements were independently performed by two radiologists with more than 10 years of experience in CT interpretation under double-blind conditions. Prior to evaluation, all images were fully anonymized by removing all patient information, scanner model, acquisition parameters, and dose information. The images were then randomly ordered to ensure that the radiologists were blinded to whether each image belonged to the PCCT or EID-CT group, as well as to all clinical and technical parameters.
Liver ROIs were placed in the right hepatic lobe (typically segments VII or VIII), avoiding large vessels, bile ducts, and visible artifacts. Three circular ROIs with an area of approximately 80 mm2 were manually placed within the liver parenchyma. The mean of the three measurements was recorded as meanliver, and the corresponding SD was recorded as SDliver, representing liver signal intensity and noise, respectively.
For background reference, a circular ROI with an area of approximately 80 mm2 was placed in the subcutaneous fat of the anterior abdominal wall, anterior to the rectus abdominis muscle, avoiding skin, vessels, and artifacts. The mean CT value (meanfat) and SD (SDfat) were recorded.
All ROIs were placed on the same axial slice with identical size and distribution, and measurements were performed on the same post-processing workstation. If the interobserver difference was ≤ 15%, the average value of the two observers was used for analysis. If the interobserver difference exceeded 15%, the case was reviewed by a third senior radiologist, and the final value was determined by consensus among the three radiologists. Interobserver agreement was assessed using the intraclass correlation coefficient based on a two-way random-effects model with absolute agreement.
Objective image quality assessment: Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), dose-corrected SNR [SNR/(CTDIvol)1/2], dose-corrected CNR [CNR/(CTDIvol)1/2], excellent image index (EII), and noise ratio (NR). The definitions were as follows: SNR = meanliver/SDliver; CNR = |meanliver - meanfat|/(SDliver₂ + SDfat₂)1/2; EII = CNR2/ED; NR = SDliver/SDfat.
Subjective image quality assessment: Independently performed by the same two radiologists under double-blind conditions using a five-point Likert scale, with scores defined as follows: 1 = non-diagnostic image quality; 2 = suboptimal image quality with limited diagnostic confidence; 3 = acceptable diagnostic quality; 4 = good image quality with clear diagnosis; 5 = excellent image quality with minimal noise or artifacts.
Interobserver agreement for subjective image quality scores was assessed using the weighted Cohen’s kappa coefficient. According to the agreement criteria, a kappa value > 0.80 was considered to indicate excellent agreement. In cases of disagreement between the two observers, a third senior radiologist participated in a joint review, and the final score was determined by consensus among the three radiologists. The same anonymization and randomization procedures described above were applied to ensure blinding to group allocation and all technical parameters.
Normality was assessed using histograms and the Shapiro-Wilk test, and variance homogeneity was evaluated using Levene’s test. Continuous variables with normal distribution and homogeneous variance were expressed as mean ± SD and compared using the independent-samples t test. Non-normally distributed or heteroscedastic variables were presented as median [interquartile range (IQR)] and compared using the Mann-Whitney U test. Categorical variables were expressed as n (%) and compared using the χ2 test or Fisher’s exact test, as appropriate. Ordinal variables, including five-point Likert image quality scores, were analyzed using the Mann-Whitney U test. All tests were two-sided, with P < 0.05 considered statistically significant. Statistical analyses were performed using Python (version 3.11.5).
A total of 80 subjects were included in the analysis, with 40 in the PCCT group and 40 in the EID-CT group. Baseline demographic and physical characteristics were comparable between the two groups (Table 1). No significant differences were observed in gender distribution (PCCT vs EID-CT: 17/23 vs 19/21, P = 0.822), age (42.58 ± 15.49 years vs 47.68 ± 12.00 years, P = 0.056), height (165.85 ± 8.66 cm vs 163.15 ± 7.71 cm, P = 0.145), weight [61.00 (57.50-72.00) kg vs 65.00 (56.50-73.00) kg, P = 0.769], or BMI (23.49 ± 3.43 kg/m2 vs 24.39 ± 4.28 kg/m2, P = 0.299).
Radiation dose parameters differed significantly between the PCCT and EID-CT groups (Table 2, Figure 2). The PCCT group demonstrated significantly lower CTDIvol, DLP, and ED compared with the EID-CT group (all P < 0.001). Specifically, the median CTDIvol in the PCCT group was 3.78 mGy (IQR: 3.20-4.17), approximately half that observed in the EID-CT group [7.15 mGy (IQR: 5.85-8.57)]. Similarly, the median DLP was 103.15 mGy × cm (IQR: 89.23-122.51) in the PCCT group, compared with 201.60 mGy × cm (IQR: 161.15-227.68) in the EID-CT group. The corresponding ED was also significantly reduced in the PCCT group [1.55 mSv (IQR: 1.34-1.84) vs 3.02 mSv (IQR: 2.42-3.42)]. Overall, PCCT achieved an approximate 50% reduction in radiation dose across all evaluated dose metrics compared with conventional EID-CT.
| Variable | PCCT | EID-CT | P value |
| CTDIvol (mGy) | 3.78 (3.20-4.17) | 7.15 (5.85-8.57) | < 0.001 |
| DLP (mGy × cm) | 103.15 (89.23-122.51) | 201.60 (161.15-227.68) | < 0.001 |
| ED (mSv) | 1.55 (1.34-1.84) | 3.02 (2.42-3.42) | < 0.001 |
Interobserver agreement for quantitative image quality parameters was excellent across all measurements. Intraclass correlation coefficients ranged from 0.892 to 0.916, with all corresponding 95% confidence intervals demonstrating strong reliability and all P < 0.001. Detailed results are presented in Table 3.
| Variable | Intraclass correlation coefficient | 95% confidence interval | P value |
| meanliver | 0.916 | 0.871-0.945 | < 0.001 |
| SDliver | 0.892 | 0.837-0.929 | < 0.001 |
| meanfat | 0.899 | 0.851-0.936 | < 0.001 |
| SDfat | 0.896 | 0.838-0.932 | < 0.001 |
Objective image quality metrics between the PCCT and EID-CT groups are summarized in Table 4 and illustrated in Figures 3 and 4. Overall, except for the mean CT attenuation of subcutaneous fat, all objective image quality parameters were significantly superior in the PCCT group compared with the EID-CT group (all P < 0.001), indicating improved image quality under low-dose conditions.
| Variable | PCCT | EID-CT | P value |
| meanliver | 65.95 (62.60-68.80) | 61.35 (55.77-65.08) | 0.001 |
| SDliver | 11.60 (10.85-12.45) | 18.60 (16.62-20.32) | < 0.001 |
| meanfat | -108.35 (-111.72 to -103.78) | -106.15 (-110.78 to -99.08) | 0.192 |
| SDfat | 11.75 (11.00-12.50) | 14.30 (13.03-16.35) | < 0.001 |
| SNR | 5.57 (5.02-6.32) | 3.26 (2.71-3.76) | < 0.001 |
| CNR | 10.26 (9.76-11.36) | 7.14 (6.21-7.67) | < 0.001 |
| EII | 71.99 (52.96-93.39) | 17.38 (12.71-20.75) | < 0.001 |
| SNR/(CDTI)1/2 | 2.90 (2.46-3.53) | 1.21 (0.97-1.47) | < 0.001 |
| CNR/(CDTI)1/2 | 5.46 (4.77-6.36) | 2.62 (2.36-2.98) | < 0.001 |
| NR | 1.00 (0.94-1.05) | 1.29 (1.10-1.48) | < 0.001 |
| Likert | < 0.001 | ||
| 3 | 1 | 29 | |
| 4 | 8 | 7 | |
| 5 | 31 | 4 |
Regarding signal intensity and image uniformity, the PCCT group demonstrated a significantly higher mean liver attenuation than the EID-CT group [65.95 (62.60-68.80) HU vs 61.35 (55.77-65.08) HU, P = 0.001], accompanied by a markedly lower liver noise level [SDliver: 11.60 (10.85-12.45) vs 18.60 (16.62-20.32), P < 0.001]. The mean CT value of subcutaneous fat did not differ significantly between groups (P = 0.192); however, fat noise was significantly lower in the PCCT group [11.75 (11.00-12.50) vs 14.30 (13.03-16.35), P < 0.001].
As shown in Figure 3A and B, both SNR and CNR were significantly higher in the PCCT group [SNR: 5.57 (5.02-6.32); CNR: 10.26 (9.76-11.36)] compared with the EID-CT group [SNR: 3.26 (2.71-3.76); CNR: 7.14 (6.21-7.67)] (all P < 0.001). After normalization for radiation dose, the PCCT group maintained significantly higher SNR/(CDTI)1/2 and CNR/(CDTI)1/2 values [2.90 (2.46-3.53) and 5.46 (4.77-6.36), respectively] than the EID-CT group [1.21 (0.97-1.47) and 2.62 (2.36-2.98)] (all P < 0.001), as illustrated in Figure 3C and D.
NR was closer to unity in the PCCT group [1.00 (0.94-1.05)] compared with the EID-CT group [1.29 (1.10-1.48), P < 0.001], indicating more uniform noise distribution (Figure 3E). In addition, the EII was markedly higher in the PCCT group [71.99 (52.96-93.39) vs 17.38 (12.71-20.75), P < 0.001], reflecting substantially improved dose efficiency (Figure 3F).
Median-based relative improvement analysis (Figure 4A) demonstrated pronounced performance gains of PCCT over EID-CT, including increases of 71.0% in SNR, 43.5% in CNR, 140.7% in SNR/(CDTI)1/2, 109.0% in CNR/(CDTI)1/2, and 314.2% in EII, alongside a 22.2% reduction in NR. The radar plot (Figure 4B) further illustrates the overall superiority of PCCT across all normalized objective image quality metrics.
Interobserver agreement for subjective image quality assessment was excellent, with a weighted Cohen’s kappa of 0.928 (95% confidence interval: 0.845-1.000, P < 0.001), indicating a high level of consistency between the two radiologists. As shown in Table 4, subjective image quality scores based on the five-point Likert scale differed significantly between the PCCT and EID-CT groups (P < 0.001). Overall image quality was rated substantially higher in the PCCT group. Specifically, 31 cases (77.5%) were rated as score 5 (excellent image quality), 8 cases (20.0%) as score 4, and only 1 case (2.5%) as score 3. In contrast, in the EID-CT group, only 4 cases (10.0%) received a score of 5, 7 cases (17.5%) a score of 4, and the majority, 29 cases (72.5%), were rated as score 3.
These findings indicate that PCCT provides superior subjective image quality compared with EID-CT in abdominal imaging, as reflected by higher ratings for image clarity, noise suppression, visualization of tissue boundaries, and overall diagnostic impression.
In this study, we systematically compared radiation dose and image quality between PCCT and conventional EID-CT for abdominal imaging under clinically relevant protocols. The principal findings demonstrate that PCCT achieves an approximate 50% reduction in radiation dose while simultaneously providing substantial improvements in both objective and subjective image quality. These results highlight the superior dose efficiency of PCCT and support its potential role in low-dose abdominal CT applications[19,20].
Radiation exposure remains a critical concern in abdominal imaging, particularly for patients requiring repeated examinations or long-term surveillance[1-3]. Our results showed that PCCT significantly reduced CTDIvol, DLP, and ED compared with EID-CT, with an overall dose reduction of approximately 50%. This magnitude of dose savings is clinically meaningful and exceeds that reported in many previous CT dose-optimization studies, underscoring the intrinsic advantage of photon-counting detector technology[5,21]. By directly converting X-ray photons into electrical signals and effectively eliminating electronic noise, PCCT enables more efficient use of low-dose photons, especially in dose-constrained imaging scenarios[22,23].
Beyond dose reduction, PCCT demonstrated markedly superior objective image quality. Both SNR and CNR were significantly higher in the PCCT group despite the lower radiation dose, reflecting improved signal fidelity and contrast resolution[10,24,25]. Importantly, after normalization for radiation dose, dose-corrected metrics [SNR/(CDTI)1/2 and CNR/(CDTI)1/2] remained substantially higher for PCCT, indicating that the observed image quality improvements were not merely a consequence of dose differences but rather reflected fundamentally enhanced dose efficiency[24-26]. Furthermore, the EII, which integrates contrast performance and ED, was more than three times higher in the PCCT group, providing a comprehensive quantitative demonstration of PCCT’s superior imaging efficiency[16,25,27].
Noise characteristics also differed markedly between the two systems. The NR in PCCT images was closer to unity, suggesting more uniform noise distribution between target tissue and background. This finding is consistent with the theoretical advantages of photon-counting detectors, which more closely follow Poisson noise statistics and reduce spatial noise non-uniformity commonly observed in EID-CT, particularly under low-dose conditions[15,16,22,28]. Such improved noise behavior is particularly relevant for abdominal imaging, where subtle soft-tissue contrast differences are critical for lesion detection and characterization[29].
The quantitative improvements observed in PCCT translated into clear advantages in subjective image assessment. A substantially higher proportion of PCCT examinations were rated as excellent by experienced radiologists, whereas most EID-CT images were rated as only acceptable[25-27]. These subjective findings reinforce the clinical relevance of the objective metrics and indicate that the benefits of PCCT are readily perceptible to human observers. Improved visualization of tissue boundaries, reduced noise, and enhanced overall image clarity may facilitate diagnostic confidence in routine abdominal CT examinations[30-32].
The clinical implications of these findings are considerable. Abdominal CT is frequently performed in oncologic follow-up, chronic disease monitoring, and screening settings, where cumulative radiation exposure is a major concern. The ability of PCCT to substantially reduce radiation dose while maintaining or even improving image quality may enable safer long-term imaging strategies, particularly for younger patients and those requiring repeated examinations[33-36]. Moreover, the improved dose efficiency of PCCT provides a technical foundation for further protocol optimization, including ultra-low-dose imaging or contrast dose reduction in future studies.
Several limitations of this study should be acknowledged. First, this was a single-center study with a moderate sample size, which may limit generalizability. Second, the PCCT and EID-CT examinations were performed on different scanner platforms, and although both systems represent state-of-the-art clinical CT technology, hardware- and vendor-specific differences cannot be fully excluded. Third, this study focused on non-contrast abdominal imaging and primarily evaluated image quality metrics rather than diagnostic accuracy for specific diseases. Future multicenter studies with larger cohorts, contrast-enhanced protocols, and task-based diagnostic performance analyses are warranted to further validate the clinical advantages of PCCT.
In conclusion, this study demonstrates that PCCT enables substantial radiation dose reduction while delivering superior objective and subjective image quality compared with conventional EID-CT in abdominal imaging. These findings support the clinical potential of PCCT as a next-generation CT technology for low-dose, high-quality abdominal imaging and provide strong evidence for its broader adoption in routine clinical practice.
| 1. | Smith-Bindman R, Chu PW, Azman Firdaus H, Stewart C, Malekhedayat M, Alber S, Bolch WE, Mahendra M, Berrington de González A, Miglioretti DL. Projected Lifetime Cancer Risks From Current Computed Tomography Imaging. JAMA Intern Med. 2025;185:710-719. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 160] [Cited by in RCA: 201] [Article Influence: 201.0] [Reference Citation Analysis (1)] |
| 2. | Smith-Bindman R, Wang Y, Stewart C, Luong J, Chu PW, Kohli M, Westphalen AC, Siegel E, Ray M, Szczykutowicz TP, Bindman AB, Romano PS. Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of Radiation Dose, Image Noise, and Image Quality. Invest Radiol. 2024;59:569-576. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 5] [Cited by in RCA: 4] [Article Influence: 2.0] [Reference Citation Analysis (0)] |
| 3. | Richman IB, Katz MH. Balancing Computed Tomography's Benefits With Radiation Risks. JAMA Intern Med. 2025;185:719. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 3] [Article Influence: 3.0] [Reference Citation Analysis (0)] |
| 4. | McCollough CH, Chen GH, Kalender W, Leng S, Samei E, Taguchi K, Wang G, Yu L, Pettigrew RI. Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. Radiology. 2012;264:567-580. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 223] [Cited by in RCA: 197] [Article Influence: 14.1] [Reference Citation Analysis (0)] |
| 5. | Willemink MJ, Noël PB. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol. 2019;29:2185-2195. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 193] [Cited by in RCA: 357] [Article Influence: 44.6] [Reference Citation Analysis (0)] |
| 6. | Sakai K, Shin D, Singh M, Malik S, Dakroub A, Sami Z, Weber J, Cao JJ, Parikh R, Chen L, Sosa F, Cohen DJ, Moses JW, Shlofmitz RA, Collet C, Shlofmitz E, Jeremias A, Khalique OK, Ali ZA. Diagnostic Performance and Clinical Impact of Photon-Counting Detector Computed Tomography in Coronary Artery Disease. J Am Coll Cardiol. 2025;85:339-348. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 9] [Cited by in RCA: 57] [Article Influence: 57.0] [Reference Citation Analysis (0)] |
| 7. | Symons R, Krauss B, Sahbaee P, Cork TE, Lakshmanan MN, Bluemke DA, Pourmorteza A. Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study. Med Phys. 2017;44:5120-5127. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 98] [Cited by in RCA: 173] [Article Influence: 19.2] [Reference Citation Analysis (0)] |
| 8. | Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: Technical Principles and Clinical Prospects. Radiology. 2018;289:293-312. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1173] [Cited by in RCA: 891] [Article Influence: 111.4] [Reference Citation Analysis (0)] |
| 9. | Shiyovich A, Singh A, Blair CV, Cardoso R, Huck D, Peng G, Shaw LJ, Leipsic JA, Gräni C, Antoniades C, Maurovich-Horvat P, Williamson EE, Cademartiri F, Achenbach S, Blankstein R. Photon-Counting Computed Tomography in Cardiac Imaging. JACC Cardiovasc Imaging. 2026;19:94-117. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 19] [Article Influence: 19.0] [Reference Citation Analysis (0)] |
| 10. | Greffier J, Viry A, Robert A, Khorsi M, Si-Mohamed S. Photon-counting CT systems: A technical review of current clinical possibilities. Diagn Interv Imaging. 2025;106:53-59. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 9] [Cited by in RCA: 78] [Article Influence: 78.0] [Reference Citation Analysis (0)] |
| 11. | Schiebler ML, Jinzaki M, Yanagawa M, Pourmorteza A, Yamada Y, Kato Y, Wada N, Schaefer-Prokop C, Dousset V, Tomiyama N, Prokop M, Lima JA, Hatabu H. Future Applications of Cardiothoracic CT. Radiology. 2025;315:e240085. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 5] [Cited by in RCA: 4] [Article Influence: 4.0] [Reference Citation Analysis (0)] |
| 12. | Marks AE, Marco KPD, Barcena AJR, Bernardino MR, Jacobsen MC, Layman RR, Melancon MP. Photon-Counting Computed Tomography of Degradable Bone Cement Loaded With Gadolinium Nanoparticles. Invest Radiol. 2025. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 1] [Reference Citation Analysis (0)] |
| 13. | Grunz JP, Huflage H. Photon-Counting Computed Tomography: Experience in Musculoskeletal Imaging. Korean J Radiol. 2024;25:662-672. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 3] [Cited by in RCA: 11] [Article Influence: 5.5] [Reference Citation Analysis (0)] |
| 14. | Douek PC, Boccalini S, Oei EHG, Cormode DP, Pourmorteza A, Boussel L, Si-Mohamed SA, Budde RPJ. Clinical Applications of Photon-counting CT: A Review of Pioneer Studies and a Glimpse into the Future. Radiology. 2023;309:e222432. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 22] [Cited by in RCA: 126] [Article Influence: 42.0] [Reference Citation Analysis (0)] |
| 15. | Becker BV, Kaatsch HL, Nestler K, Overhoff D, Schneider J, Dillinger D, Piechotka J, Brockmann MA, Ullmann R, Port M, Scherthan H, Waldeck S. Initial experience on abdominal photon-counting computed tomography in clinical routine: general image quality and dose exposure. Eur Radiol. 2023;33:2461-2468. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 18] [Reference Citation Analysis (0)] |
| 16. | Greffier J, Dabli D, Faby S, Pastor M, Croisille C, de Oliveira F, Erath J, Beregi JP. Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study. Diagn Interv Imaging. 2024;105:379-385. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 13] [Reference Citation Analysis (0)] |
| 17. | Sawall S, Klein L, Wehrse E, Rotkopf LT, Amato C, Maier J, Schlemmer HP, Ziener CH, Heinze S, Kachelrieß M. Threshold-dependent iodine imaging and spectral separation in a whole-body photon-counting CT system. Eur Radiol. 2021;31:6631-6639. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 13] [Article Influence: 2.6] [Reference Citation Analysis (0)] |
| 18. | Schwartz FR, Samei E, Marin D. Exploiting the Potential of Photon-Counting CT in Abdominal Imaging. Invest Radiol. 2023;58:488-498. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 2] [Cited by in RCA: 24] [Article Influence: 8.0] [Reference Citation Analysis (0)] |
| 19. | Dane B, Ananthakrishnan L, Marin D, Toia GV, Borhani AA, Schwartz FR, Chung R, Leng S, Fletcher JG, Morgan DE. Adult Abdominal Photon-Counting CT Protocols: A Multiinstitutional Consensus of the Society of Abdominal Radiology Photon-Counting Detector CT Emerging Technology Commission. AJR Am J Roentgenol. 2025;225:e2533625. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 2] [Cited by in RCA: 2] [Article Influence: 2.0] [Reference Citation Analysis (0)] |
| 20. | Surov A, Diallo-Danebrock R, Radi A, Kröger JR, Niehoff JH, Michael AE, Gerdes B, Elhabash S, Wienke A, Borggrefe J. Photon Counting Computed Tomography in Rectal Cancer: Associations Between Iodine Concentration, Histopathology and Treatment Response: A Pilot Study. Acad Radiol. 2024;31:3620-3626. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 15] [Reference Citation Analysis (0)] |
| 21. | McCollough CH. Automated data mining of exposure information for dose management and patient safety initiatives in medical imaging. Radiology. 2012;264:322-324. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 10] [Cited by in RCA: 9] [Article Influence: 0.6] [Reference Citation Analysis (0)] |
| 22. | Greffier J, Van Ngoc Ty C, Sammoud S, Croisille C, Beregi JP, Dabli D, Fitton I. Image quality and dose reduction with photon counting detector CT: Comparison between ultra-high resolution mode and standard mode using a phantom study. Diagn Interv Imaging. 2025;106:320-326. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 6] [Reference Citation Analysis (0)] |
| 23. | Tao S, Rajendran K, McCollough CH, Leng S. Feasibility of multi-contrast imaging on dual-source photon counting detector (PCD) CT: An initial phantom study. Med Phys. 2019;46:4105-4115. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 29] [Cited by in RCA: 46] [Article Influence: 6.6] [Reference Citation Analysis (0)] |
| 24. | De Santis D, Del Gaudio A, Santangeli C, Fanelli F, Pacelli F, Capece Minutolo Del Sasso L, Zerunian M, Polici M, Polidori T, Pucciarelli F, Marin D, Laghi A, Caruso D. Every Drop (Photon) Counts: Current Applications and Future Challenges of Photon-Counting Detector CT in Abdominal Imaging. Invest Radiol. 2025;60:647-657. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 4] [Reference Citation Analysis (0)] |
| 25. | Brandt EGS, Müller CF, Thomsen H, Rodell AB, Ibragimov B, Andersen MB. Imaging the pancreas with photon-counting CT - A review of normal pancreatic anatomy. Eur J Radiol. 2024;181:111736. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 10] [Reference Citation Analysis (1)] |
| 26. | Frings M, Welsner M, Mousa C, Zensen S, Salhöfer L, Meetschen M, Beck N, Bos D, Westhölter D, Wienker J, Taube C, Umutlu L, Schaarschmidt BM, Forsting M, Haubold J, Sutharsan S, Opitz M. Low-dose high-resolution chest CT in adults with cystic fibrosis: intraindividual comparison between photon-counting and energy-integrating detector CT. Eur Radiol Exp. 2024;8:105. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 17] [Reference Citation Analysis (0)] |
| 27. | Racine D, Mergen V, Viry A, Eberhard M, Becce F, Rotzinger DC, Alkadhi H, Euler A. Photon-Counting Detector CT With Quantum Iterative Reconstruction: Impact on Liver Lesion Detection and Radiation Dose Reduction. Invest Radiol. 2023;58:245-252. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 7] [Cited by in RCA: 40] [Article Influence: 13.3] [Reference Citation Analysis (0)] |
| 28. | Krueger MB, Werncke T, Eicke M, Schwerk N, Eckstein J, Huisinga C, Panknin C, Shin HO, Silchmüller FJ, Schultze-Florey RE, Hansen G, Wacker F, Hellms S, Renz DM. Photon-counting Detector CT Enables Pediatric Low-dose Chest Imaging With Further Reduction of Radiation Exposure. Invest Radiol. 2026;61:318-325. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 4] [Cited by in RCA: 4] [Article Influence: 4.0] [Reference Citation Analysis (0)] |
| 29. | Wildman-Tobriner B, Felice N, Kalisz KR, Allen BC, Thomas SP, Kruse DE, Segars WP, Harrawood B, Bashir MR, Marin D, Morrison S, Erkanli A, Samei E, Abadi E. Photon-Counting CT Effects on Sensitivity for Liver Lesion Detection: A Reader Study Using Virtual Imaging. Radiology. 2025;314:e241568. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 10] [Cited by in RCA: 12] [Article Influence: 12.0] [Reference Citation Analysis (0)] |
| 30. | Zhou W, Huo D, Browne LP, Zhou X, Weinman J. Universal 120-kV Dual-Source Ultra-High Pitch Protocol on the Photon-Counting CT System for Pediatric Abdomen of All Sizes: A Phantom Investigation Comparing With Energy-Integrating CT. Invest Radiol. 2024;59:719-726. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 12] [Reference Citation Analysis (0)] |
| 31. | Dabli D, Pastor M, Faby S, Erath J, Croisille C, Pereira F, Beregi JP, Greffier J. Photon-counting versus energy-integrating CT of abdomen-pelvis: a phantom study on the potential for reducing iodine contrast media. Eur Radiol Exp. 2025;9:36. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 5] [Reference Citation Analysis (0)] |
| 32. | Rajagopal JR, Farhadi F, Solomon J, Sahbaee P, Saboury B, Pritchard WF, Jones EC, Samei E. Comparison of Low Dose Performance of Photon-Counting and Energy Integrating CT. Acad Radiol. 2021;28:1754-1760. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 37] [Cited by in RCA: 69] [Article Influence: 13.8] [Reference Citation Analysis (0)] |
| 33. | Homayounieh F, Gopal N, Firouzabadi FD, Sahbaee P, Yazdian P, Nikpanah M, Do M, Wang M, Gautam R, Ball MW, Pritchard WF, Jones EC, Wen H, Linehan WM, Turkbey EB, Malayeri AA. A Prospective Study of the Diagnostic Performance of Photon-Counting CT Compared With MRI in the Characterization of Renal Masses. Invest Radiol. 2024;59:774-781. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 6] [Cited by in RCA: 7] [Article Influence: 3.5] [Reference Citation Analysis (0)] |
| 34. | Schreck J, Laukamp KR, Niehoff JH, Michael AE, Boriesosdick J, Wöltjen MM, Kröger JR, Reimer RP, Grunz JP, Borggrefe J, Lennartz S. Metal artifact reduction in patients with total hip replacements: evaluation of clinical photon counting CT using virtual monoenergetic images. Eur Radiol. 2023;33:9286-9295. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 26] [Article Influence: 8.7] [Reference Citation Analysis (0)] |
| 35. | Koukoutegos K, 's Heeren R, De Wever L, De Keyzer F, Maes F, Bosmans H. Segmentation-based quantitative measurements in renal CT imaging using deep learning. Eur Radiol Exp. 2024;8:110. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 3] [Reference Citation Analysis (0)] |
| 36. | Khanungwanitkul K, Sliwicka O, Schwartz FR. Abdominal applications of photon-counting CT. Br J Radiol. 2025;98:1854-1858. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 2] [Cited by in RCA: 6] [Article Influence: 6.0] [Reference Citation Analysis (0)] |