Image quality evaluation of dual-layer spectral CT in comparison to single-layer CT in a reduced-dose setting

Assessment of Spectral Computed Tomography Image Quality and Detection of Lesions in the Liver Based on Image Reconstruction Algorithms and Virtual Tube Voltage

Background: Spectral detector computed tomography (SDCT) has demonstrated superior diagnostic performance and image quality in liver disease assessment compared with traditional CT. Selecting the right reconstruction algorithm and tube voltage is essential to avoid increased noise and diagnostic errors. Objectives: This study evaluated improvements in image quality achieved using various virtual tube voltages and reconstruction algorithms for diagnosing common liver diseases with spectral CT. Methods: This retrospective study involved forty-seven patients who underwent spectral CT scans for liver conditions, including fatty liver, hemangiomas, and metastatic lesions. The assessment utilized signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), with images reconstructed using various algorithms (IMR, iDose) at different levels and virtual tube voltages. Three experienced radiologists analyzed the reconstructed images to identify the best reconstruction methods and tube voltage combinations for diagnosing these liver pathologies. Results: The signal-to-noise ratio (SNR) was highest for spectral CT images using the IMR3 algorithm in metastatic, hemangioma, and fatty liver cases. A strong positive correlation was found between IMR3 at 120 keV and 70 keV (p-value = 0.000). In contrast, iDOSE2 at 120 keV and 70 keV showed a low correlation of 0.291 (p-value = 0.045). Evaluators noted that IMR1 at 70 keV provided the best visibility for liver lesions (mean = 3.58), while IMR3 at 120 keV had the lowest image quality (mean = 2.65). Conclusions: Improvements in image quality were noted with SDCT, especially in SNR values for liver tissues at low radiation doses and a specific IMR level. The IMR1 algorithm reduced noise, enhancing the visibility of liver lesions for better diagnosis.

Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency

BACKGROUND

Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge.

PURPOSE

To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR.

MATERIAL AND METHODS

A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards.

RESULTS

With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards.

CONCLUSION

The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BACKGROUND

This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.

METHODS

Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group.

RESULTS

When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).

CONCLUSION

The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Evaluation of an in-use chest CT protocol in lung cancer screening - A single institutional study

Background

Lung cancer is the most common cause of cancer-related death worldwide and therefore there has been a growing demand for low-dose computed tomography (LDCT) protocols.

Purpose

To investigate and evaluate the dose and image quality of patients undergoing lung cancer screening (LCS) using LDCT in Norway.

Materials and Methods

Retrospective dosimetry data, volumetric CT dose index (CTDIvol) and dose-length product (DLP), from 70 average-size and 70 large-size patients who underwent LDCT scan for LCS were included in the survey. Effective dose and size-specific dose were calculated for each examination and were compared with the American Association of Physicists in Medicine (AAPM) requirement. For a quantitative image quality analysis, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were determined for different regions in the chest with two iterative reconstruction techniques, iDose and Iterative Model Reconstruction. Differences in dose and image quality between average-size and large-size patients were evaluated by Independent sample t test, and Wilcoxon signed rank test within the same patient group.

Results

The independent sample t test revealed significant differences (p < .05) in dose values between average-size and large-size patients. Mean CTDIvol and DLP for average-size patients were 2.8 mGy and 115 mGy.cm, respectively, with appropriate increment for the large-size patients. Image quality (image noise, SNR, and CNR) did not significantly differ between patient groups when images were reconstructed with a model based iterative reconstruction algorithm.

Conclusion

The screening protocol assessed in this study resulted in CTDIvol values that were compliant with AAPM recommendation. No significant differences in objective image quality were found between patient groups.

An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images

OBJECTIVES

Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI.

METHODS

Paired 60 kiloelectron volt (keV) VMI and CPI of 114 consecutive patients suspected of PE, obtained with a detector-based spectral CT scanner, were retrospectively analyzed by an established AI algorithm. The CT pulmonary angiography (CTPA) were classified as positive or negative for PE on a per-patient level. The reference standard was established using a comprehensive method that combined the evaluation of the attending radiologist and three experienced cardiothoracic radiologists aided by two different detection tools. Sensitivity, specificity, positive and negative predictive values and likelihood ratios of the algorithm on VMI and CPI were compared.

RESULTS

The prevalence of PE according to the reference standard was 35.1% (40 patients). None of the diagnostic accuracy measures of the algorithm showed a significant difference between CPI and VMI. Sensitivity was 77.5% (95% confidence interval (CI) 64.6-90.4%) and 85.0% (73.9-96.1%) (p = 0.08) on CPI and VMI respectively and specificity 96.0% (91.4-100.0%) and 94.6% (89.4-99.7%) (p = 0.32).

CONCLUSIONS

Diagnostic performance of the AI algorithm that was trained on CPI did not drop on VMI, which is reassuring for its use in clinical practice.

CLINICAL RELEVANCE STATEMENT

A commercially available AI algorithm, trained on conventional polychromatic CTPA, could be safely used on virtual monochromatic images. This supports the sustainability of AI-aided detection of PE on CT despite ongoing technological advances in medical imaging, although monitoring in daily practice will remain important.

KEY POINTS

• Diagnostic accuracy of an AI algorithm trained on conventional polychromatic images to detect PE did not drop on virtual monochromatic images. • Our results are reassuring as innovations in hardware and reconstruction in CT are continuing, whilst commercial AI algorithms that are trained on older generation data enter healthcare.

A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images

Aims

Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.

Methods and results

Fifty consecutive patients with known left ventricular (LV) dysfunction (LVD) underwent both CE-CMR and (early and late) CE-CCT. According to the CE-CMR patterns, patients were classified as ischemic (n = 15, 30%) or non-ischemic (n = 35, 70%) LVD. Delayed enhancement regions were manually traced on late CE-CCT using CE-CMR as reference. On early CE-CCT images, the myocardial sectors were extracted according to AHA 16-segment model and labeled as with scar or not, based on the late CE-CCT manual tracing. A DL model was developed to classify each segment. A total of 44,187 LV segments were analyzed, resulting in accuracy of 71% and area under the ROC curve of 76% (95% CI: 72%-81%), while, with the bull's eye segmental comparison of CE-CMR and respective early CE-CCT findings, an 89% agreement was achieved.

Conclusions

DL on early CE-CCT acquisition may allow detection of LV sectors affected with myocardial fibrosis, thus without additional contrast-agent administration or radiational dose. Such tool might reduce the user interaction and visual inspection with benefit in both efforts and time.

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

OBJECTIVES

Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP.

MATERIAL AND METHODS

Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram.

RESULTS

This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05).

CONCLUSIONS

Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs' invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.

Computed Tomography Structured Reporting in the Staging of Lymphoma: A Delphi Consensus Proposal

Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports for lymphoma patients during the staging phase to improve communication between radiologists, members of multidisciplinary teams, and patients. A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology (SIRM), was established. A modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. The Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. The final SR version was divided into four sections: (a) Patient Clinical Data, (b) Clinical Evaluation, (c) Imaging Protocol, and (d) Report, including n = 13 items in the "Patient Clinical Data" section, n = 8 items in the "Clinical Evaluation" section, n = 9 items in the "Imaging Protocol" section, and n = 32 items in the "Report" section. Overall, 62 items were included in the final version of the SR. A dedicated section of significant images was added as part of the report. In the first Delphi round, all sections received more than a good rating (≥3). The overall mean score of the experts and the sum of score for structured report were 4.4 (range 1-5) and 1524 (mean value of 101.6 and standard deviation of 11.8). The Cα correlation coefficient was 0.89 in the first round. In the second Delphi round, all sections received more than an excellent rating (≥4). The overall mean score of the experts and the sum of scores for structured report were 4.9 (range 3-5) and 1694 (mean value of 112.9 and standard deviation of 4.0). The Cα correlation coefficient was 0.87 in this round. The highest overall means value, highest sum of scores of the panelists, and smallest standard deviation values of the evaluations in this round reflect the increase of the internal consistency and agreement among experts in the second round compared to first round. The accurate statement of imaging data given to referring physicians is critical for patient care; the information contained affects both the decision-making process and the subsequent treatment. The radiology report is the most important source of clinical imaging information. It conveys critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records this information for future clinical and research use. The present SR was generated based on a multi-round consensus-building Delphi exercise and uses standardized terminology and structures, in order to adhere to diagnostic/therapeutic recommendations and facilitate enrolment in clinical trials, to reduce any ambiguity that may arise from non-conventional language, and to enable better communication between radiologists and clinicians.

Dual-Energy Computed Tomography of the Liver: Uses in Clinical Practices and Applications

Dual-energy computed tomography (DECT) is an imaging technique based on data acquisition at two different energy settings. Recent advances in CT have allowed data acquisitions and simultaneous analyses of X-rays at two energy levels, and have resulted in novel developments in the field of abdominal imaging. The use of low and high X-ray tube voltages in DECT provide fused images that improve the detection of liver tumors owing to the higher contrast-to-noise ratio (CNR) of the tumor compared with the liver. The use of contrast agents in CT scanning improves image quality by enhancing the CNR and signal-to-noise ratio while reducing beam-hardening artifacts. DECT can improve detection and characterization of hepatic abnormalities, including mass lesions. The technique can also be used for the diagnosis of steatosis and iron overload. This article reviews and illustrates the different applications of DECT in liver imaging.

Building a dual-energy CT service line in abdominal radiology

As the access of radiology practices to dual-energy CT (DECT) has increased worldwide, seamless integration into clinical workflows and optimized use of this technology are desirable. In this article, we provide basic concepts of commercially available DECT hardware implementations, discuss financial and logistical aspects, provide tips for protocol building and image routing strategies, and review radiation dose considerations to establish a DECT service line in abdominal imaging. KEY POINTS: • Tube-based and detector-based DECT implementations with varying features and strengths are available on the imaging market. • Thorough assessment of financial and logistical aspects is key to successful implementation of a DECT service line. • Optimized protocol building and image routing strategies are of critical importance for effective use and seamless inception of DECT in routine clinical workflows.

The sub-millisievert era in CTCA: the technical basis of the new radiation dose approach

Computed tomography coronary angiography (CTCA) has become a cornerstone in the diagnostic process of the heart disease. Although the cardiac imaging with interventional procedures is responsible for approximately 40% of the cumulative effective dose in medical imaging, a relevant radiation dose reduction over the last decade was obtained, with the beginning of the sub-mSv era in CTCA. The main technical basis to obtain a radiation dose reduction in CTCA is the use of a low tube voltage, the adoption of a prospective electrocardiogram-triggering spiral protocol and the application of the tube current modulation with the iterative reconstruction technique. Nevertheless, CTCA examinations are characterized by a wide range of radiation doses between different radiology departments. Moreover, the dose exposure in CTCA is extremely important because the benefit-risk calculus in comparison with other modalities also depends on it. Finally, because anatomical evaluation not adequately predicts the hemodynamic relevance of coronary stenosis, a low radiation dose in routine CTCA would allow the greatest use of the myocardial CT perfusion, fractional flow reserve-CT, dual-energy CT and artificial intelligence, to shift focus from morphological assessment to a comprehensive morphological and functional evaluation of the stenosis. Therefore, the aim of this work is to summarize the correct use of the technical basis in order that CTCA becomes an established examination for assessment of the coronary artery disease with low radiation dose.

Virtual monoenergetic reconstructions of dynamic DECT acquisitions for calculation of perfusion maps of blood flow: Quantitative comparison to conventional, dynamic 80 kVp CT perfusion

PURPOSE

Investigation of potential improvements in dynamic CT perfusion measurements by exploitation of improved visualization of contrast agent in virtual monoenergetic reconstructions of images acquired with dual-energy computed tomography (DECT).

METHOD

For 17 patients with pancreatic carcinoma, dynamic dual-source DECT acquisitions were performed at 80kVp/Sn140kVp every 1.5 s over 51 s. Virtual monoenergetic images (VMI) were reconstructed for photon energies between 40 keV and 150 keV (5 keV steps). Using the maximum-slope model, perfusion maps of blood flow were calculated from VMIs and 80kVp images and compared quantitatively with regard to blood flow measured in regions of interest in healthy tissue and carcinoma, standard deviation (SD), and absolute-difference-to-standard-deviation ratio (ADSDR) of measurements.

RESULTS

On average, blood flow calculated from VMIs increased with increasing energy levels from 114.3 ± 37.2 mL/100 mL/min (healthy tissue) and 45.6 ± 25.3 mL/100 mL/min (carcinoma) for 40 keV to 128.6 ± 58.9 mL/100 mL/min (healthy tissue) and 75.5 ± 49.8 mL/100 mL/min (carcinoma) for 150 keV, compared to 114.2 ± 37.4 mL/100 mL/min (healthy tissue) and 46.5 ± 26.6 mL/100 mL/min (carcinoma) for polyenergetic 80kVp. Differences in blood flow between tissue types were significant for all energies. Differences between perfusion maps calculated from VMIs and 80kVp images were not significant below 110 keV. SD and ADSDR were significantly better for perfusion maps calculated from VMIs at energies between 40 keV and 55 keV than for those calculated from 80kVp images. Compared to effective dose of dynamic 80kVp acquisitions (4.6 ± 2.2mSv), dose of dynamic DECT/VMI acquisitions (8.0 ± 3.7mSv) was higher.

CONCLUSIONS

Perfusion maps of blood flow based on low-energy VMIs between 40 keV and 55 keV offer improved robustness and quality of quantitative measurements over those calculated from 80kVp image data (reference standard), albeit at increased patient radiation exposure.