Can Photon-Counting CT Improve Estimation Accuracy of Morphological Radiomics Features? A Simulation Study for Assessing the Quantitative Benefits from Improved Spatial Resolution in Deep Silicon-Based Photon-Counting CT

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study

OBJECTIVES

To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems.

METHODS

A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD).

RESULTS

The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems.

CONCLUSION

The radiomics features were non-reproducible with significant variability in values among different CT techniques.

CLINICAL RELEVANCE STATEMENT

Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine.

KEY POINTS

CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Assessing the Stability of Photon-Counting CT: Insights from a Two-Year Longitudinal Study

Background

Among the advancements in computed tomography (CT) technology, photon-counting computed tomography (PCCT) stands out as a significant innovation, providing superior spectral imaging capabilities while simultaneously reducing radiation exposure. Its long-term stability is important for clinical care, especially longitudinal studies, but is currently unknown.

Purpose

This study sets out to comprehensively analyze the long-term stability of a first-generation clinical PCCT scanner.

Materials and Methods

Over a two-year period, from November 2021 to November 2023, we conducted weekly identical experiments utilizing the same multi-energy CT protocol. These experiments included various tissue-mimicking inserts to rigorously assess the stability of Hounsfield Units (HU) and image noise in Virtual Monochromatic Images (VMIs) and iodine density maps. Throughout this period, notable software and hardware modifications were meticulously recorded. Each week, VMIs and iodine density maps were reconstructed and analyzed to evaluate quantitative stability over time.

Results

Spectral results consistently demonstrated the quantitative stability of PCCT. VMIs exhibited stable HU values, such as variation in relative error for VMI 70 keV measuring 0.11% and 0.30% for single-source and dual-source modes, respectively. Similarly, noise levels remained stable with slight fluctuations linked to software changes for VMI 40 and 70 keV that corresponded to changes of 8 and 1 HU, respectively. Furthermore, iodine density quantification maintained stability and showed significant improvement with software and hardware changes, especially in dual-source mode with nominal errors decreasing from 1.44 to 0.03 mg/mL.

Conclusion

This study provides the first long-term reproducibility assessment of quantitative PCCT imaging, highlighting its potential for the clinical arena. This study indicates its long-term utility in diagnostic radiology, especially for longitudinal studies.

Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies

OBJECTIVES

To assess perceptual benefits provided by the improved spatial resolution and noise performance of deep silicon photon-counting CT (Si-PCCT) over conventional energy-integrating CT (ECT) using polychromatic images for various clinical tasks and anatomical regions.

MATERIALS AND METHODS

Anthropomorphic, computational models were developed for lungs, liver, inner ear, and head-and-neck (H&N) anatomies. These regions included specific abnormalities such as lesions in the lungs and liver, and calcified plaques in the carotid arteries. The anatomical models were imaged using a scanner-specific CT simulation platform (DukeSim) modeling a Si-PCCT prototype and a conventional ECT system at matched dose levels. The simulated polychromatic projections were reconstructed with matched in-plane resolutions using manufacturer-specific software. The reconstructed pairs of images were scored by radiologists to gauge the task-specific perceptual benefits provided by Si-PCCT compared to ECT based on visualization of anatomical and image quality features. The scores were standardized as z-scores for minimizing inter-observer variability and compared between the systems for evidence of statistically significant improvement (one-sided Wilcoxon rank-sum test with a significance level of 0.05) in perceptual performance for Si-PCCT.

RESULTS

Si-PCCT offered favorable image quality and improved visualization capabilities, leading to mean improvements in task-specific perceptual performance over ECT for most tasks. The improvements for Si-PCCT were statistically significant for the visualization of lung lesion (0.08 ± 0.89 vs. 0.90 ± 0.48), liver lesion (-0.64 ± 0.37 vs. 0.95 ± 0.55), and soft tissue structures (-0.47 ± 0.90 vs. 0.33 ± 1.24) and cochlea (-0.47 ± 0.80 vs. 0.38 ± 0.62) in inner ear.

CONCLUSIONS

Si-PCCT exhibited mean improvements in task-specific perceptual performance over ECT for most clinical tasks considered in this study, with statistically significant improvement for 6/20 tasks. The perceptual performance of Si-PCCT is expected to improve further with availability of spectral information and reconstruction kernels optimized for high resolution provided by smaller pixel size of Si-PCCT. The outcomes of this study indicate the positive potential of Si-PCCT for benefiting routine clinical practice through improved image quality and visualization capabilities.

Analysis of Epicardial Adipose Tissue Texture in Relation to Coronary Artery Calcification in PCCT: The EAT Signature!

(1) Background: Epicardial adipose tissue influences cardiac biology in physiological and pathological terms. As it is suspected to be linked to coronary artery calcification, identifying improved methods of diagnostics for these patients is important. The use of radiomics and the new Photon-Counting computed tomography (PCCT) may offer a feasible step toward improved diagnostics in these patients. (2) Methods: In this retrospective single-centre study epicardial adipose tissue was segmented manually on axial unenhanced images. Patients were divided into three groups, depending on the severity of coronary artery calcification. Features were extracted using pyradiomics. Mean and standard deviation were calculated with the Pearson correlation coefficient for feature correlation. Random Forest classification was applied for feature selection and ANOVA was performed for group comparison. (3) Results: A total of 53 patients (32 male, 21 female, mean age 57, range from 21 to 80 years) were enrolled in this study and scanned on the novel PCCT. "Original_glrlm_LongRunEmphasis", "original_glrlm_RunVariance", "original_glszm_HighGrayLevelZoneEmphasis", and "original_glszm_SizeZoneNonUniformity" were found to show significant differences between patients with coronary artery calcification (Agatston score 1-99/≥100) and those without. (4) Conclusions: Four texture features of epicardial adipose tissue are associated with coronary artery calcification and may reflect inflammatory reactions of epicardial adipose tissue, offering a potential imaging biomarker for atherosclerosis detection.

Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules

BACKGROUND

We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs).

METHODS

A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC).

RESULTS

Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value.

CONCLUSIONS

The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs.

RELEVANCE STATEMENT

The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work.

KEY POINTS

• We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.

[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.

Photon Counting CT: Technical Principles, Clinical Applications, and Future Prospects

Photon-counting computed tomography (PCCT) is a new technique that utilizes photon-counting detectors to convert individual X-ray photons directly into an electrical signal, which can achieve higher spatial resolution, improved iodine signal, radiation dose reduction, artifact reduction, and multienergy imaging. This review introduces the technical principles of PCCT, and summarizes its first-in-human experience and current applications in clinical settings, and discusses the future prospects of PCCT.

Soft Reconstruction Kernels Improve HCC Imaging on a Photon-Counting Detector CT

RATIONALE AND OBJECTIVES

Hepatocellular carcinoma (HCC) is the only tumor entity that allows non-invasive diagnosis based on imaging without further histological proof. Therefore, excellent image quality is of utmost importance for HCC diagnosis. Novel photon-counting detector (PCD) CT improves image quality via noise reduction and higher spatial resolution, inherently providing spectral information. The aim of this study was to investigate these improvements for HCC imaging with triple-phase liver PCD-CT in a phantom and patient population study focusing on identification of the optimal reconstruction kernel.

MATERIALS AND METHODS

Phantom experiments were performed to analyze objective quality characteristics of the regular body and quantitative reconstruction kernels, each with four sharpness levels (36-40-44-48). For 24 patients with viable HCC lesions on PCD-CT, virtual monoenergetic images at 50 keV were reconstructed using these kernels. Quantitative image analysis included contrast-to-noise ratio (CNR) and edge sharpness. Three raters performed qualitative analyses evaluating noise, contrast, lesion conspicuity, and overall image quality.

RESULTS

In all contrast phases, the CNR was highest using the kernels with a sharpness level of 36 (all p < 0.05), with no significant influence on lesion sharpness. Softer reconstruction kernels were also rated better regarding noise and image quality (all p < 0.05). No significant differences were found in image contrast and lesion conspicuity. Comparing body and quantitative kernels with equal sharpness levels, there was no difference in image quality criteria, neither regarding in vitro nor in vivo analysis.

CONCLUSION

Soft reconstruction kernels yield the best overall quality for the evaluation of HCC in PCD-CT. As the image quality of quantitative kernels with potential for spectral post-processing is not restricted compared to regular body kernels, they should be preferred.

A methodology for incorporating a photon-counting CT system into routine clinical use

Photon-counting computed tomography (PCCT) systems are increasingly available in the U.S. following Food and Drug Administration (FDA) approval of the first clinical PCCT system in Fall 2021. Consequently, there will be a need to incorporate PCCTs into existing fleets of traditional CT systems. The commissioning process of a PCCT was devised by evaluating the degree of agreement between the performance of the PCCT and that of established clinical CT systems. A PCCT system (Siemens NAEOTOM Alpha) was evaluated using the American College of Radiology(ACR) CT phantom (Gammex 464). The phantom was scanned on the system and on a 3rd Generation EID CT system (Siemens Force) at three clinical dose levels. Images were reconstructed across the range of available reconstruction kernels and Iterative Reconstruction (IR) strengths. Two image quality metrics-spatial resolution and noise texture-were calculated using AAPM TG233 software (imQuest), as well as a dose metric to achieve target image noise magnitude of 10 HU. For each pair of EID-PCCT kernel/IR strengths, the difference in metrics were calculated, weighted, and multiplied over all metrics to determine the concordance between systems. IR performance was characterized by comparing relative noise texture and reference dose as a function of IR strength for each system. In general, as kernel "sharpness" increased for each system, spatial resolution, noise spatial frequency, and reference dose increased. For a given kernel, EID reconstruction showed higher spatial resolution compared to PCCT in standard resolution mode. PCCT implementation of IR better preserved noise texture across all strengths compared to the EID, demonstrated by respective 20 and 7% shifts in noise texture from IR "Off" to IR "Max." Overall, the closest match for a given EID reconstruction kernel/IR strength was identified as a PCCT kernel with "sharpness" increased by 1 step and IR strength increased by 1-2 steps. Substantial dose reduction potential of up to 70% was found when targeting a constant noise magnitude.

Periaortic adipose radiomics texture features associated with increased coronary calcium score-first results on a photon-counting-CT

BACKGROUND

Cardiovascular diseases remain the world's primary cause of death. The identification and treatment of patients at risk of cardiovascular events thus are as important as ever. Adipose tissue is a classic risk factor for cardiovascular diseases, has been linked to systemic inflammation, and is suspected to contribute to vascular calcification. To further investigate this issue, the use of texture analysis of adipose tissue using radiomics features could prove a feasible option.

METHODS

In this retrospective single-center study, 55 patients (mean age 56, 34 male, 21 female) were scanned on a first-generation photon-counting CT. On axial unenhanced images, periaortic adipose tissue surrounding the thoracic descending aorta was segmented manually. For feature extraction, patients were divided into three groups, depending on coronary artery calcification (Agatston Score 0, Agatston Score 1-99, Agatston Score ≥ 100). 106 features were extracted using pyradiomics. R statistics was used for statistical analysis, calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. Random Forest classification was carried out for feature selection and Boxplots and heatmaps were used for visualization. Additionally, monovariable logistic regression predicting an Agatston Score > 0 was performed, selected features were tested for multicollinearity and a 10-fold cross-validation investigated the stability of the leading feature.

RESULTS

Two higher-order radiomics features, namely "glcm_ClusterProminence" and "glcm_ClusterTendency" were found to differ between patients without coronary artery calcification and those with coronary artery calcification (Agatston Score ≥ 100) through Random Forest classification. As the leading differentiating feature "glcm_ClusterProminence" was identified.

CONCLUSION

Changes in periaortic adipose tissue texture seem to correlate with coronary artery calcium score, supporting a possible influence of inflammatory or fibrotic activity in perivascular adipose tissue. Radiomics features may potentially aid as corresponding biomarkers in the future.