Organ Dose and Attributable Cancer Risk in Lung Cancer Screening with Low-Dose Computed Tomography

Evaluation of the safety of short-term follow-up CT for the management of consolidation in lung cancer screening

OBJECTIVES

Focal consolidation on CT may be inflammatory or malignant, and PET-CT imaging is rarely discriminatory. Furthermore, consolidation may demonstrate spontaneous resolution obviating the need for PET-CT imaging. This retrospective study sought to assess the safety and cost-effectiveness of short-interval 6-week follow-up CT for consolidation in a lung cancer screening programme.

METHODS

Between January 2019 and January 2024, participants in a regional lung cancer screening programme with focal indeterminate consolidation underwent a 6-week repeat CT rather than immediate PET-CT and invasive investigation. The proportion of participants with non-resolving consolidation, the risk of malignancy in consolidation at a 6-week follow-up, and the risk of upstaging over a 6-week delay were determined. Cost savings were estimated from National Health Service reference costs.

RESULTS

In 10,247 CT studies, focal indeterminate consolidation was detected in 113 participants (1.1%) (mean age 68 years, range 55-76, 65 males). Consolidation spontaneously resolved at 6 weeks in 63/110 (57%) who attended follow-up; 14/110 (12.7%) participants had malignancy; no patients upstaged during follow-up. An estimated cost saving of £47,600/10,000 screening CTs performed might be obtained through a conservative approach of short-term interval CT, rather than immediate PET-CT and further investigation.

CONCLUSION

Early repeat CT avoids PET-CT in more than half of patients with consolidation and can be utilised to reduce over-investigation of screen-detected consolidation, which may demonstrate spontaneous resolution.

KEY POINTS

Question Is short-term interval follow-up CT in lung cancer screening a safe and cost-effective approach to managing indeterminate (inflammatory or malignant) consolidation? Findings Short-interval CT imaging demonstrates spontaneous resolution of consolidation in over 50% participants in this study, whilst persistent consolidation has a high likelihood of malignancy. Clinical relevance Short-interval CT did not result in upstaging of malignancy and therefore can be considered a safe strategy to prevent the over-investigation of screen-detected consolidation, supporting recent European and American screening recommendations.

Determining optimal imaging protocols for enhanced chest CT: From phantom to clinical study

OBJECTIVES

To investigate the optimal imaging protocols for enhanced chest CT to achieve good image quality and diagnostic performance with a lower radiation dose.

MATERIALS AND METHODS

This IRB-approved study included both phantoms and patients. Two phantoms were scanned using 4 scanning modes. Images in each group were reconstructed using adaptive statistical iterative reconstruction-V (ASiR-V) at three strength levels of 40%, 60%, and 80%, denoted A1-3-D1-3, respectively with 1-3 representing the three ASiR-V levels. The image quality and radiation dose were evaluated to obtain the best imaging mode. 48 patients underwent contrast-enhanced standard dose CT (SDCT, protocol A) and low-dose CT (LDCT, protocol D) of the chest for follow-up. The image quality, radiation dose, and volume measurements of the left lung, right lung, and trachea using an AI-based software were compared.

RESULTS

In the phantom study, D2 protocol which had the lowest dose, was selected as the optimal imaging protocol for enhanced chest CT. Compared with SDCT, LDCT reduced the radiation dose by 45% compared to SDCT. Images in LDCT with ASiR-V60% had similar noise as the standard SDCT images with the standard ASiR-V40%, but they had higher SNRs and CNRs. In addition, the volumes of the left lung, right lung, and bronchus did not significantly differ between the two groups.

CONCLUSION

The combination of Auto-kV prescription and ODMfull with ASiR-V60% in contrast-enhanced chest CT can achieve individualized low dose scanning with satisfactory image quality and diagnostic accuracy.

Optimizing Radiation Dose in High-Resolution Chest CT: The Impact of Patient-Specific Factors and Size-Specific Dose Estimates

Background/Objectives: High-resolution chest computed tomography (HRCT) is a critical diagnostic tool, but radiation dose optimization remains a significant concern. Traditional dose metrics such as the volume CT dose index (CTDIvol) and dose-length product (DLP) do not adequately account for patient size variations. This study aimed to assess the radiation dose in HRCT using size-specific dose estimates (SSDEs) and evaluate the influence of patient-specific factors on key dosimetric parameters. Methods: This retrospective cohort study analyzed HRCT scans from 1970 adult patients conducted between September 2022 and February 2024. Radiation dose data, including the CTDIvol, DLP, SSDE, and effective dose, were extracted from the DoseWatch™ software. Patient demographics, scan protocols, and exposure parameters were collected. Descriptive statistics, correlation analyses, and significance testing were conducted using IBM SPSS (Version 26). Results: A significant positive correlation was found between the radiation dose parameters (CTDIvol, DLP, SSDE) and patient body size metrics, particularly BMI (rpb = 0.445, p < 0.01). The SSDE values ranged from 2.7 to 12.4 mGy, providing a more patient-specific dose assessment than traditional indices. Gender differences were observed, with male patients receiving higher radiation doses (p < 0.01). The scanning range exhibited a significant negative correlation with the CTDIvol and SSDE, suggesting dose variations with anatomical coverage. Conclusions: SSDEs provide a more accurate, patient-centered dose assessment in HRCT, allowing for optimized radiation safety strategies. These findings emphasize the need for size-adapted scan protocols to minimize exposure while maintaining diagnostic image quality. The routine integration of SSDE into clinical practice is recommended to enhance individualized dose management in HRCT.

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM)

PURPOSE

Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM).

MATERIAL AND METHODS

In a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired t-test, and Bland-Altman plots.

RESULTS

Patients averaged 49.4 years and 100% were female with mean BMI 26.6 ± 6.1 kg/m2. Cyst score measured by AiCE reconstruction with SilverBeam filter correlated well with that of AIDR3D reconstruction with standard filter, with a 1.5% difference, and allowed for an 85.5% median radiation dosage reduction (0.33 mSv vs. 2.27 mSv, respectively, p < 0.001). Compared to standard filter with AIDR3D, SNR for SilverBeam AiCE images was slightly lower (3.2 vs. 3.1, respectively, p = 0.005).

CONCLUSION

SilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease.

CLINICAL RELEVANCE STATEMENT

Radiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm.

KEY POINTS

• Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction. • SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT. • SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.

Construction of lung cancer serum markers based on ReliefF feature selection

Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.

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.

Evaluation of organ dose using size-specific dose estimation (SSDE) and related cancer risk due to chest CT scan during the COVID-19 pandemic

This study aimed to estimate lung and breast doses for individual patients using the size-specific dose estimate (SSDE) method, as well as calculating effective doses, in patients who underwent chest CT scans during the COVID-19 pandemic. Cancer risk incidence was estimated using excess relative risk (ERR), excess absolute risk (EAR), and lifetime attributable risk (LAR) models from the Biological Effects of Ionizing Radiation Report VII (BEIR-VII). Data from about 570 patients who underwent CT scans for COVID-19 screening were utilized for this study. Using the header of the CT images in a Python script, SSDE and effective dose were calculated for each patient. The SSDE obtained by water equivalent effective diameter (wSSDE) was considered as lung and breast dose, and applied in organ-specific cancer risk estimation. The mean wSSDE value for females (13.3 mGy) was slightly higher than that for males (13.1 mGy), but the difference was not statistically significant (P value = 0.41). No significant differences were observed between males and females in terms of calculated EAR and ERR for lung cancer at 5 and 30 years after exposure (P value = 0.47, 0.46, respectively). Similarly, there was no significant difference in lung cancer LAR values between females and males (P value = 0.48). The results also indicated a decrease in LAR values for both lung and breast cancers with increasing exposure age. In accordance with the ALARA (as low as reasonably achievable) principle, it is important for medical staff and the general public to consider the benefits of CT imaging in detecting such infections. Additionally, imaging medical physicists and CT scan experts should optimize imaging protocols and strike a balance between image quality for detecting abnormalities and radiation dose, all while adhering to the ALARA principle.

An evaluation of CT radiation doses within the Yorkshire Lung Screening Trial

OBJECTIVES

To evaluate radiation doses for all low-dose CT scans performed during the first year of a lung screening trial.

METHODS

For all lung screening scans that were performed using a CT protocol that delivered image quality meeting the RSNA QIBA criteria, radiation dose metrics, participant height, weight, gender, and age were recorded. Values of volume CT dose index (CTDIvol) and dose length product (DLP) were evaluated as a function of weight in order to assess the performance of the scan protocol across the participant cohort. Calculated effective doses were used to establish the additional lifetime attributable cancer risks arising from trial scans.

RESULTS

Median values of CTDIvol, DLP, and effective dose (IQR) from the 3521 scans were 1.1 mGy (0.70), 42.4 mGycm (24.9), and 1.15 mSv (0.67), whilst for 60-80kg participants the values were 1.0 mGy (0.30), 35.8 mGycm (11.4), and 0.97 mSv (0.31). A statistically significant correlation between CTDIvol and weight was identified for males (r = 0.9123, P < .001) and females (r = 0.9052, P < .001), however, the effect of gender on CTDIvol was not statistically significant (P = .2328) despite notable differences existing at the extremes of the weight range. The additional lifetime attributable cancer risks from a single scan were in the range 0.001%-0.006%.

CONCLUSIONS

Low radiation doses can be achieved across a typical lung screening cohort using scan protocols that have been shown to deliver high levels of image quality. The observed dose levels may be considered as typical values for lung screening scans on similar types of scanners for an equivalent participant cohort.

ADVANCES IN KNOWLEDGE

Presentation of typical radiation dose levels for CT lung screening examinations in a large UK trial. Effective radiation doses can be of the order of 1 mSv for standard sized participants. Lifetime attributable cancer risks resulting from a single low-dose CT scan did not exceed 0.006%.

A Novel Artificial Intelligence Based Denoising Method for Ultra-Low Dose CT Used for Lung Cancer Screening

RATIONALE AND OBJECTIVES

To assess ultra-low-dose (ULD) computed tomography as well as a novel artificial intelligence-based reconstruction denoising method for ULD (dULD) in screening for lung cancer.

MATERIALS AND METHODS

This prospective study included 123 patients, 84 (70.6%) men, mean age 62.6 ± 5.35 (55-75), who had a low dose and an ULD scan. A fully convolutional-network, trained using a unique perceptual loss was used for denoising. The network used for the extraction of the perceptual features was trained in an unsupervised manner on the data itself by denoising stacked auto-encoders. The perceptual features were a combination of feature maps taken from different layers of the network, instead of using a single layer for training. Two readers independently reviewed all sets of images.

RESULTS

ULD decreased average radiation-dose by 76% (48%-85%). When comparing negative and actionable Lung-RADS categories, there was no difference between dULD and LD (p = 0.22 RE, p > 0.999 RR) nor between ULD and LD scans (p = 0.75 RE, p > 0.999 RR). ULD negative likelihood ratio (LR) for the readers was 0.033-0.097. dULD performed better with a negative LR of 0.021-0.051. Coronary artery calcifications (CAC) were documented on the dULD scan in 88(74%) and 81(68%) patients, and on the ULD in 74(62.2%) and 77(64.7%) patients. The dULD demonstrated high sensitivity, 93.9%-97.6%, with an accuracy of 91.7%. An almost perfect agreement between readers was noted for CAC scores: for LD (ICC = 0.924), dULD (ICC = 0.903), and for ULD (ICC = 0.817) scans.

CONCLUSION

A novel AI-based denoising method allows a substantial decrease in radiation dose, without misinterpretation of actionable pulmonary nodules or life-threatening findings such as aortic aneurysms.

Implementation of Individualized Low-Dose Computed Tomography-Guided Hook Wire Localization of Pulmonary Nodules: Feasibility and Safety in the Clinical Setting

Background: CT-guided hook-wire localization is an essential step in the management of small pulmonary nodules. Few studies, however, have focused on reducing radiation exposure during the procedure. Purpose: This study aims to explore the feasibility of implementing a low-dose computed tomography (CT)-guided hook wire localization using tailored kVp based on patients' body size. Materials and Methods: A total of 151 patients with small pulmonary nodules were prospectively enrolled for CT-guided hook wire localization using individualized low-dose CT (LDCT) vs. standard-dose CT (SDCT) protocols. Radiation dose, image quality, characteristics of target nodules and procedure-related variables were compared. All variables were analyzed using Chi-Square and Student's t-test. Results: The mean CTDIvol was significantly reduced for LDCT (for BMI ≤ 21 kg/m2, 0.56 ± 0.00 mGy and for BMI > 21 kg/m2, 1.48 ± 0.00 mGy) when compared with SDCT (for BMI ≤ 21 kg/m2, 5.24 ± 0.95 mGy and for BMI > 21 kg/m2, 6.69 ± 1.47 mGy). Accordingly, the DLP of LDCT was significantly reduced as compared with that of SDCT (for BMI ≤ 21 kg/m2, 56.86 ± 4.73 vs. 533.58 ± 122.06 mGy.cm, and for BMI > 21 kg/m2, 167.02 ± 38.76 vs. 746.01 ± 230.91 mGy.cm). In comparison with SDCT, the effective dose (ED) of LDCT decreased by an average of 89.42% (for BMI ≤ 21 kg/m2) and 77.68% (for BMI > 21 kg/m2), respectively. Although the images acquired with the LDCT protocol yielded inferior quality to those acquired with the SDCT protocol, they were clinically acceptable for hook wire localization. Conclusions: LDCT-guided localization can provide safety and nodule detection performance comparable to SDCT-guided localization, benefiting radiation dose reduction dramatically, especially for patients with small body mass indexes.

Assessment of the effectiveness of Saba shielding with the composition of Cu-Bi in neck CT imaging: a phantom and patient study

The use of computed tomography (CT) is a very well-established medical diagnostic imaging modality, however, the high radiation dose due to this imaging method is a major concern. Therefore, dose reduction methods are necessary, especially for superficial radiosensitive organs like the thyroid. The aim of this study is to construct and assess a CT shield with composition of 90% Cu and 10% Bi (Saba shield) with regard to dose reduction and image quality. The efficiency of the constructed shields for dose reduction was assessed by measuring entrance skin dose (ESD), using thermoluminescence dosimeters placed on an anthropomorphic phantom. Image quality was assessed quantitatively based on image noise and CT number accuracy by drawing regions of interest on CT images of the anthropomorphic phantom. Image quality was further investigated qualitatively in a patient study. Application of the Saba shield and 100% Bi shield with the thickness of one thickness (1T) reduced ESD by 50.2% and 51.7%, respectively, and using a three-fold thickness reduced ESD by 64.6% and 65.1%, respectively. Saba shield with thickness of 1T had no significant change in image noise in the anterior part, and image noise and mean CT number in the posterior part (P> 0.05). The statistical analysis performed did not find any meaningful difference between the study and control groups in image quality assessment of the patient study (P> 0.05). The 1T Saba shield reduced thyroid dose efficiently during neck CT imaging without causing unwanted effects on image quality.

Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods

PURPOSE

To compare the capability of CTs obtained with a silver or copper x-ray beam spectral modulation filter (Ag filter and Cu filter) and reconstructed with FBP, hybrid-type IR and deep learning reconstruction (DLR) for radiation dose reduction for lung nodule detection using a chest phantom study.

MATERIALS AND METHODS

A chest CT phantom was scanned with a 320-detector row CT with Ag filter at 0.6, 1.6 and 2.5 mGy and Cu filters at 0.6, 1.6, 2.5 and 9.6 mGy, and reconstructed with the aforementioned methods. To compare image quality of all the CT data, SNRs and CNRs for any nodule were calculated for all protocols. To compare nodule detection capability among all protocols, the probability of detection of any nodule was assessed with a 5-point visual scoring system. Then, ROC analyses were performed to compare nodule detection capability of Ag and Cu filters for each radiation dose data with the same method and of the three methods for any radiation dose data and obtained with either filter.

RESULTS

At any of the doses, SNR, CNR and area under the curve for the Ag filter were significantly higher or larger than those for the Cu filter (p < 0.05). Moreover, with DLR, those values were significantly higher or larger than all the others for CTs obtained with any of the radiation doses and either filter (p < 0.05).

CONCLUSION

The Ag filter and DLR can significantly improve image quality and nodule detection capability compared with the Cu filter and other reconstruction methods at each of radiation doses used.

Risk Assessment of Early Lung Cancer with LDCT and Health Examinations

Early detection of lung cancer has a higher likelihood of curative treatment and thus improves survival rate. Low-dose computed tomography (LDCT) screening has been shown to be effective for high-risk individuals in several clinical trials, but has high false positive rates. To evaluate the risk of stage I lung cancer in the general population not limited to smokers, a retrospective study of 133 subjects was conducted in a medical center in Taiwan. Regularized regression was used to build the risk prediction model by using LDCT and health examinations. The proposed model selected seven variables related to nodule morphology, counts and location, and ten variables related to blood tests and medical history, achieving an area under the curve (AUC) value of 0.93. The higher the age, white blood cell count (WBC), blood urea nitrogen (BUN), diabetes, gout, chronic obstructive pulmonary disease (COPD), other cancers, and the presence of spiculation, ground-glass opacity (GGO), and part solid nodules, the higher the risk of lung cancer. Subjects with calcification, solid nodules, nodules in the middle lobes, more nodules, and diseases related to thyroid, liver, and digestive systems were at a lower risk. The selected variables did not indicate causation.

Personalized Chest Computed Tomography: Minimum Diagnostic Radiation Dose Levels for the Detection of Fibrosis, Nodules, and Pneumonia

OBJECTIVES

The purpose of this study was to evaluate the minimum diagnostic radiation dose level for the detection of high-resolution (HR) lung structures, pulmonary nodules (PNs), and infectious diseases (IDs).

MATERIALS AND METHODS

A preclinical chest computed tomography (CT) trial was performed with a human cadaver without known lung disease with incremental radiation dose using tin filter-based spectral shaping protocols. A subset of protocols for full diagnostic evaluation of HR, PN, and ID structures was translated to clinical routine. Also, a minimum diagnostic radiation dose protocol was defined (MIN). These protocols were prospectively applied over 5 months in the clinical routine under consideration of the individual clinical indication. We compared radiation dose parameters, objective and subjective image quality (IQ).

RESULTS

The HR protocol was performed in 38 patients (43%), PN in 21 patients (24%), ID in 20 patients (23%), and MIN in 9 patients (10%). Radiation dose differed significantly among HR, PN, and ID (5.4, 1.2, and 0.6 mGy, respectively; P < 0.001). Differences between ID and MIN (0.2 mGy) were not significant (P = 0.262). Dose-normalized contrast-to-noise ratio was comparable among all groups (P = 0.087). Overall IQ was perfect for the HR protocol (median, 5.0) and decreased for PN (4.5), ID-CT (4.3), and MIN-CT (2.5). The delineation of disease-specific findings was high in all dedicated protocols (HR, 5.0; PN, 5.0; ID, 4.5). The MIN protocol had borderline IQ for PN and ID lesions but was insufficient for HR structures. The dose reductions were 78% (PN), 89% (ID), and 97% (MIN) compared with the HR protocols.

CONCLUSIONS

Personalized chest CT tailored to the clinical indications leads to substantial dose reduction without reducing interpretability. More than 50% of patients can benefit from such individual adaptation in a clinical routine setting. Personalized radiation dose adjustments with validated diagnostic IQ are especially preferable for evaluating ID and PN lesions.

Establishing scanning protocols for a CT lung cancer screening trial in the UK

OBJECTIVES

To develop a CT scanning protocol for lung cancer screening which achieved low radiation dose and a high level of objectively assessed image quality.

METHODS

An anthropomorphic chest phantom and a commercially available lung screening image quality phantom were scanned on a series of scan protocols from a previous UK lung screening pilot and on an alternative protocol. The chest phantom scans were used to assess the CT dose metrics on community-based mobile CT scanners and comparisons were made with published recommended doses. Scans of the image quality phantom were objectively assessed against the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) recommendations. Protocol adjustments were made to ensure that the recommended dose and image quality levels were both achieved.

RESULTS

The alternative scan protocol yielded doses up to 72% lower than on the previously used protocols with a CTDIvol of 0.6mGy for the 55 kg equivalent phantom and 1.3mGy with an additional 6 cm of tissue equivalent material in place. Scans on the existing protocols failed on two of the QIBA image quality metrics (edge enhancement and 3D resolution aspect ratio). Following adjustments to the reconstruction parameters of the resulting image quality met all six QIBA recommendations. Radiologist review of phantom images with this scan protocol deemed them suitable for a lung screening trial.

CONCLUSIONS

Scan protocols yielding low radiation doses and high levels of objectively assessed image quality which meet published criteria can be established through the use of specific anthropomorphic and image quality phantoms, and are deliverable in community-based lung cancer screening.

ADVANCES IN KNOWLEDGE

Development of a standard methodology for establishing CT lung screening scanning protocolsUse of QIBA recommendations as objective image quality metricsStandardised lung phantoms are essential tools for setting up lung screening protocols.

Dosimetry and Comparison between Different CT Protocols (Low Dose, Ultralow Dose, and Conventional CT) for Lung Nodules' Detection in a Phantom

Background

The effects of dose reduction in lung nodule detection need better understanding.

Purpose

To compare the detection rate of simulated lung nodules in a chest phantom using different computed tomography protocols, low dose (LD), ultralow dose (ULD), and conventional (CCT), and to quantify their respective amount of radiation.

Materials and Methods

A chest phantom containing 93 simulated lung nodules was scanned using five different protocols: ULD (80 kVp/30 mA), LD A (120 kVp/20 mA), LD B (100 kVp/30 mA), LD C (120 kVp/30 mA), and CCT (120 kVp/automatic mA). Four chest radiologists analyzed a selected image from each protocol and registered in diagrams the nodules they detected. Kruskal-Wallis and McNemar's tests were performed to determine the difference in nodule detection. Equivalent doses were estimated by placing thermoluminescent dosimeters on the surface and inside the phantom.

Results

There was no significant difference in lung nodules' detection when comparing ULD and LD protocols (p=0.208 to p=1.000), but there was a significant difference when comparing each one of those against CCT (p < 0.001). The detection rate of nodules with CT attenuation values lower than -600 HU was also different when comparing all protocols against CCT (p < 0.001 to p=0.007). There was at least moderate agreement between observers in all protocols (κ-value >0.41). Equivalent dose values ranged from 0.5 to 9 mSv.

Conclusion

There is no significant difference in simulated lung nodules' detection when comparing ULD and LD protocols, but both differ from CCT, especially when considering lower-attenuating nodules.

Latest CT technologies in lung cancer screening: protocols and radiation dose reduction

The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in post-processing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols.

CT Image Reconstruction using NLMfuzzyCD Regularization Method

Aims and scope: Computed Tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behaviour) with the help of a limited set of its projections at different angles.

BACKGROUND

To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used.

OBJECTIVE

Although the conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from the optimal smoothing as the number of iterations increase.

METHODS

For solving this problem, in this paper present a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means fuzzy complex diffusion as a regularization term for noise reduction and edge preservation.

RESULTS

The proposed model was evaluated on four test cases phantoms.

CONCLUSION

Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compare with the state-of-the-art techniques.

Performance of sparse-view CT reconstruction with multi-directional gradient operators

To further reduce the noise and artifacts in the reconstructed image of sparse-view CT, we have modified the traditional total variation (TV) methods, which only calculate the gradient variations in x and y directions, and have proposed 8- and 26-directional (the multi-directional) gradient operators for TV calculation to improve the quality of reconstructed images. Different from traditional TV methods, the proposed 8- and 26-directional gradient operators additionally consider the diagonal directions in TV calculation. The proposed method preserves more information from original tomographic data in the step of gradient transform to obtain better reconstruction image qualities. Our algorithms were tested using two-dimensional Shepp–Logan phantom and three-dimensional clinical CT images. Results were evaluated using the root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). All the experiment results show that the sparse-view CT images reconstructed using the proposed 8- and 26-directional gradient operators are superior to those reconstructed by traditional TV methods. Qualitative and quantitative analyses indicate that the more number of directions that the gradient operator has, the better images can be reconstructed. The 8- and 26-directional gradient operators we proposed have better capability to reduce noise and artifacts than traditional TV methods, and they are applicable to be applied to and combined with existing CT reconstruction algorithms derived from CS theory to produce better image quality in sparse-view reconstruction.

OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or ultra-low-dose CT (OPTIMACT)-a randomised controlled trial chest X-ray or ultra-low-dose CT at the ED: design and rationale

BACKGROUND

Chest X-ray has been the standard imaging method for patients suspected of non-traumatic pulmonary disease at the emergency department (ED) for years. Recently, ultra-low-dose chest computed tomography (ULD chest CT) has been introduced, which provides substantially more detailed information on pulmonary conditions that may cause pulmonary disease, with a dose in the order of chest X-ray (0.1 vs. 0.05 mSv). The OPTimal IMAging strategy in patients suspected of non-traumatic pulmonary disease at the emergency department: chest X-ray or CT (OPTIMACT) study is a randomized trial designed to evaluate the effectiveness of replacing chest X-ray for ULD chest CT in the diagnostic work-up of patients suspected of non-traumatic pulmonary disease at the ED.

METHODS

Two thousand four hundred patients presenting at the ED with pulmonary complaints and suspected of non-traumatic pulmonary disease will be enrolled in this multicenter, pragmatic, randomized trial. During randomly assigned periods of one calendar month, either conventional chest X-ray or ULD chest CT scan will be used as the imaging strategy. Randomization will rely on computer-generated blocks of 2 months to control for seasonal effects. Chest X-ray and ULD chest CT will be performed in a standardized way, after obtaining the clinical history and performing physical examination and initial laboratory tests. The primary outcome measure is functional health at 28 days. Secondary outcome measures are mental health, length of hospital stay, mortality within 28 days, quality-adjusted life years (QALYs) during the first 28 days, correct diagnoses at ED discharge as compared to the final post hoc diagnosis, and number of patients in follow-up because of incidental findings on chest X-ray or ULD chest CT. In an economic evaluation, we will estimate total health care costs during the first 28 days.

DISCUSSION

This pragmatic trial will clarify the effects of replacing chest X-ray by ULD chest CT in daily practice, in terms of patient-related health outcomes and costs, in the diagnostic work-up of patients suspected of non-traumatic pulmonary disease at the ED.

TRIAL REGISTRATION

The OPTIMACT trial is registered in the Netherlands National Trial Register under number NTR6163. The date of registration is December 6, 2016.

Single-energy pediatric chest computed tomography with spectral filtration at 100 kVp: effects on radiation parameters and image quality

BACKGROUND

Most of the applied radiation dose at CT is in the lower photon energy range, which is of limited diagnostic importance.

OBJECTIVE

To investigate image quality and effects on radiation parameters of 100-kVp spectral filtration single-energy chest CT using a tin-filter at third-generation dual-source CT in comparison to standard 100-kVp chest CT.

MATERIALS AND METHODS

Thirty-three children referred for a non-contrast chest CT performed on a third-generation dual-source CT scanner were examined at 100 kVp with a dedicated tin filter with a tube current-time product resulting in standard protocol dose. We compared resulting images with images from children examined using standard single-source chest CT at 100 kVp. We assessed objective and subjective image quality and compared radiation dose parameters.

RESULTS

Radiation dose was comparable for children 5 years old and younger, and it was moderately decreased for older children when using spectral filtration (P=0.006). Effective tube current increased significantly (P=0.0001) with spectral filtration, up to a factor of 10. Signal-to-noise ratio and image noise were similar for both examination techniques (P≥0.06). Subjective image quality showed no significant differences (P≥0.2).

CONCLUSION

Using 100-kVp spectral filtration chest CT in children by means of a tube-based tin-filter on a third-generation dual-source CT scanner increases effective tube current up to a factor of 10 to provide similar image quality at equivalent dose compared to standard single-source CT without spectral filtration.

Reflections on the Implementation of Low-Dose Computed Tomography Screening in Individuals at High Risk of Lung Cancer in Spain

Lung cancer (LC) is a major public health issue. Despite recent advances in treatment, primary prevention and early diagnosis are key to reducing the incidence and mortality of this disease. A recent clinical trial demonstrated the efficacy of selective screening by low-dose computed tomography (LDCT) in reducing the risk of both lung cancer mortality and all-cause mortality in high-risk individuals. This article contains the reflections of an expert group on the use of LDCT for early diagnosis of LC in high-risk individuals, and how to evaluate its implementation in Spain. The expert group was set up by the Spanish Society of Pulmonology and Thoracic Surgery (SEPAR), the Spanish Society of Thoracic Surgery (SECT), the Spanish Society of Radiology (SERAM) and the Spanish Society of Medical Oncology (SEOM).

Radiation-associated neoplasia: clinical, pathological and genomic correlates

Ionizing radiation is an established risk factor for the development of benign and malignant tumours. The epidemiology of radiation-associated neoplasia has been studied over the decades in diverse populations, including Japanese atomic bomb survivors, exposed communities following the Chernobyl nuclear power plant disaster, and paediatric and adult populations receiving therapeutic irradiation. Radiation has been associated with an increased risk of neoplasia throughout the human body, with some sites showing a markedly increased relative risk of developing tumours (thyroid; soft tissues), depending on patient age and the context of exposure. Although the mechanisms of cellular injury and repair resulting from ionizing radiation are well described, the genomics of radiation-induced tumours are still relatively poorly understood, with some exceptions, such as RET rearrangement in thyroid carcinomas following iodine-131 exposure and MYC amplification in cutaneous angiosarcoma following chest wall irradiation for breast cancer. This review will provide a general overview of the epidemiology, molecular mechanism, pathology and genomics of radiation-associated neoplasia in humans.

Dual Energy CT Pulmonary Angiography with 6g Iodine-A Propensity Score-Matched Study

OBJECTIVE

To evaluate the performance of low contrast media (CM) dose dual-energy computed tomography pulmonary angiography (CTPA) with advanced monoenergetic reconstructions in patients with suspected pulmonary embolism (PE).

MATERIALS AND METHODS

The study had institutional review board approval; all patients gave written informed consent. Forty-one patients (25 men, 16 women, mean age 62.9±14.7 years) undergoing low CM dose (15ml, 6g iodine) dual-energy CTPA with advanced monoenergetic reconstructions were matched via propensity-scoring based on logistic regression analysis with a comparison group of 41 patients (24 men, 17 women, mean age 62.7±13.9 years) undergoing standard CM dose single-energy CTPA (80ml, 24g iodine). Subjective (noise, artifacts) and objective (attenuation, noise, contrast-to-noise ratio (CNR)) image quality was assessed by two blinded, independent readers. All patients underwent clinical follow-up after three months for evaluation of adverse events.

RESULTS

Interrater agreement for subjective image quality in both groups ranged from fair to excellent (ICC: 0.46-0.84); agreement for objective image quality was excellent (ICC: 0.83-0.93). There was no significant difference regarding subjective noise (p = 0.15-0.72) and artifacts (p = 0.16-1) between the low and the standard CM dose group. There was no significant difference regarding CNR between the CM dose groups (p = 0.11-0.87). Seven of the 41 (17%) patients in the low and 5/41 (12%) in the standard CM dose group were diagnosed with PE (p = 0.32). No patient suffered from subsequent PE or PE-associated death during the follow-up period.

CONCLUSION

Dual-energy CTPA with advanced monoenergetic reconstruction is feasible with 6g iodine and allows for the diagnosis and safe exclusion of central, lobar, and segmental PE.