Repeatability of small lung nodule measurement in low-dose lung screening: a phantom study

Assessing the effect of scanning parameter on the size and density of pulmonary nodules: a phantom study

BACKGROUND

Lung cancer remains a leading cause of death among cancer patients. Computed tomography (CT) plays a key role in lung cancer screening. Previous studies have not adequately quantified the effect of scanning protocols on the detected tumor size. The aim of this study was to assess the effect of various CT scanning parameters on tumor size and densitometry based on a phantom study and to investigate the optimal energy and mA image quality for screening assessment.

METHODS

We proposed a new model using the LUNGMAN N1 phantom multipurpose anthropomorphic chest phantom (diameters: 8, 10, and 12 mm; CT values: - 100, - 630, and - 800 HU) to evaluate the influence of changes in tube voltage and tube current on the size and density of pulmonary nodules. In the LUNGMAN N1 model, three types of simulated lung nodules representing solid tumors of different sizes were used. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used to evaluate the image quality of each scanning combination. The consistency between the calculated results based on segmentation from two physicists was evaluated using the interclass correlation coefficient (ICC).

RESULTS

In terms of nodule size, the longest diameters of ground-glass nodules (GGNs) were closest to the ground truth on the images measured at 100 kVp tube voltage, and the longest diameters of solid nodules were closest to the ground truth on the images measured at 80 kVp tube voltage. In respect to density, the CT values of GGNs and solid nodules were closest to the ground truth when measured at 80 kVp and 100 kVp tube voltage, respectively. The overall agreement demonstrates that the measurements were consistent between the two physicists.

CONCLUSIONS

Our proposed model demonstrated that a combination of 80 kVp and 140 mA scans was preferred for measuring the size of the solid nodules, and a combination of 100 kVp and 100 mA scans was preferred for measuring the size of the GGNs when performing lung cancer screening. The CT values at 80 kVp and 100 kVp were preferred for the measurement of GGNs and solid nodules, respectively, which were closest to the true CT values of the nodules. Therefore, the combination of scanning parameters should be selected for different types of nodules to obtain more accurate nodal data.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study

PURPOSE

To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol.

METHODS

A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared.

RESULTS

With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs.

CONCLUSION

Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Application effect of low-dose spiral CT on pulmonary nodules and its diagnostic value for benign and malignant nodules

OBJECTIVE

This study was designed to determine the application effect of low-dose computed tomography (LDCT) on detecting pulmonary nodules (PNs) and its diagnostic value for benign and malignant pulmonary nodules.

METHODS

Data of 432 patients with PNs admitted to Julu County Hospital between March 2018 and June 2021 in were collected and analysed retrospectively. All patients underwent LDCT and conventional-dose spiral computed tomography (CT). The detection rate and image characteristics of the two methods were compared, and the image quality and radiation dose of the two diagnostic methods were also compared.

RESULTS

No significant difference was found between LDCT and conventional-dose spiral CT in the detection rate of lung cancer (P>0.05). The area under the curve of conventional-dose CT was 0.932, with a specificity and sensitivity of 93.87% and 92.45%, and the area under the curve of LDCT was 0.902, with a specificity and sensitivity of 90.80% and 89.62%. The radiation dose consumed during LDCT was greatly less than that consumed by conventional-dose CT (P<0.05). Additionally, the two methods were not different in CT image quality and superior vena cava artifact (P>0.05). No notable difference was found between LDCT and conventional-dose CT in terms of the diagnosis rate of PNs in vascular aggregation sign, pleural indentation sign, lobulation sign and spiculation sign.

CONCLUSION

LDCT can clearly show the typical images of early lung cancer, with less effective radiation dose, and can thus contribute to a high detection rate, so it is worth popularizing.