Characterization and validation of the thorax phantom Lungman for dose assessment in chest radiography optimization studies

Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging

PURPOSE

To validate the performance of computer-aided detection (CAD) and volumetry software using an anthropomorphic phantom with a ground truth (GT) set of 3D-printed nodules.

METHODS

The Kyoto Kaguku Lungman phantom, containing 3D-printed solid nodules including six diameters (4 to 9 mm) and three morphologies (smooth, lobulated, spiculated), was scanned at varying CTDIvol levels (6.04, 1.54 and 0.20 mGy). Combinations of reconstruction algorithms (iterative and deep learning image reconstruction) and kernels (soft and hard) were applied. Detection, volumetry and density results recorded by a commercially available AI-based algorithm (AVIEW LCS + ) were compared to the absolute GT, which was determined through µCT scanning at 50 µm resolution. The associations between image acquisition parameters or nodule characteristics and accuracy of nodule detection and characterization were analyzed with chi square tests and multiple linear regression.

RESULTS

High levels of detection sensitivity and precision (minimal 83 % and 91 % respectively) were observed across all acquisitions. Neither reconstruction algorithm nor radiation dose showed significant associations with detection. Nodule diameter however showed a highly significant association with detection (p < 0.0001). Volumetric measurements for nodules > 6 mm were accurate within 10 % absolute range from volumeGT, regardless of dose and reconstruction. Nodule diameter and morphology are major determinants of volumetric accuracy (p < 0.001). Density assignment was not significantly influenced by any parameters.

CONCLUSIONS

Our study confirms the software's accurate performance in nodule volumetry, detection and density characterization with robustness for variations in CT imaging protocols. This study suggests the incorporation of similar phantom setups in quality assurance of CAD tools.

Simulation of X-ray projections on GPU: Benchmarking gVirtualXray with clinically realistic phantoms

BACKGROUND AND OBJECTIVES

This study provides a quantitative comparison of images created using gVirtualXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes.

METHODS

Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned.

RESULTS

The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume.

CONCLUSIONS

When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simulations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to minimise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character animation, which can be deployed in virtual reality applications.

Impact of dose reduction and iterative reconstruction algorithm on the detectability of pulmonary nodules by artificial intelligence

PURPOSE

The purpose of this study was to assess whether the performances of an automated software for lung nodule detection with computed tomography (CT) are affected by radiation dose and the use of iterative reconstruction algorithm.

MATERIALS AND METHODS

A chest phantom (Multipurpose Chest Phantom N1; Kyoto Kagaku Co. Ltd, Kyoto, Japan) with 15 pulmonary nodules was scanned with a total of five CT protocol settings with up to 20-fold dose reduction. All CT examinations were reconstructed with iterative reconstruction algorithms ADMIRE 3 and ADMIRE 5 and were then analyzed for the presence of pulmonary nodules with a fully automated computer aided detection software system (InferRead^TM CT Lung, Infervision), which is based on deep neural networks.

RESULTS

The sensitivity of fully automated pulmonary nodule detection for ground-glass nodules at standard dose CT was greater (70.0%; 14/20; 95% CI: 51.6-88.4%) than at 10-fold and 20-fold dose reduction (30.0%; 6/20; 95% CI: 0.0%-62.5%). There were less false positive findings when ADMIRE 5 reconstruction was used (4.0 ± 2.8 [SD]; range: 2-6) instead of ADMIRE 3 reconstruction (25.0 ± 15.6 [SD]; range: 14-36). There was no difference in the sensitivity of detection of solid and subsolid nodules between standard dose (100%; 95% CI: 100-100%) and 10- and 20-fold reduced dose CT (92.5%; 95% CI: 83.8-100.0%). Image noise was significantly greater with ADMIRE 3 (81 ± 2 [SD] [range: 79-84]; 104 ± 3 [SD] [range: 101-107]; 114 ± 5 [SD] [range: 110-119]; 193 ± 10 [SD] [range: 183-203]; 220 ± 16 [SD] [range: 210-238]) compared to ADMIRE 5 (44 ± 2 [SD] [range: 42-46]; 60 ± 2 [SD] [range: 57-61]; 66 ± 1 [SD] [range: 65-67]; 103 ± 4 [SD] [range: 98-106]; 110 ± 1 [SD] [range: 109-111]), respectively in each of the five CT protocols.

CONCLUSION

This phantom study suggests that dose reduction and iterative reconstruction settings have an impact on detectability of pulmonary nodules by artificial intelligence software and we therefore encourage adaption of dose levels and reconstruction methods prior to widespread implementation of fully automatic nodule detection software for lung cancer screening purposes.

Methodology to create 3D models of COVID-19 pathologies for virtual clinical trials

Purpose: We describe the creation of computational models of lung pathologies indicative of COVID-19 disease. The models are intended for use in virtual clinical trials (VCT) for task-specific optimization of chest x-ray (CXR) imaging. Approach: Images of COVID-19 patients confirmed by computed tomography were used to segment areas of increased attenuation in the lungs, all compatible with ground glass opacities and consolidations. Using a modeling methodology, the segmented pathologies were converted to polygonal meshes and adapted to fit the lungs of a previously developed polygonal mesh thorax phantom. The models were then voxelized with a resolution of 0.5 × 0.5 × 0.5    mm 3 and used as input in a simulation framework to generate radiographic images. Primary projections were generated via ray tracing while the Monte Carlo transport code was used for the scattered radiation. Realistic sharpness and noise characteristics were also simulated, followed by clinical image processing. Example images generated at 120 kVp were used for the validation of the models in a reader study. Additionally, images were uploaded to an Artificial Intelligence (AI) software for the detection of COVID-19. Results: Nine models of COVID-19 associated pathologies were created, covering a range of disease severity. The realism of the models was confirmed by experienced radiologists and by dedicated AI software. Conclusions: A methodology has been developed for the rapid generation of realistic 3D models of a large range of COVID-19 pathologies. The modeling framework can be used as the basis for VCTs for testing detection and triaging of COVID-19 suspected cases.

Translation from murine to human lung imaging using x-ray dark field radiography: A simulation study

Recent studies on murine models have demonstrated the potential of dark field (DF) x-ray imaging for lung diseases. The alveolar microstructure causes small angle scattering, which is visualised in DF images. Whether DF imaging works for human lungs is not a priori guaranteed as human alveoli are larger and system settings for murine imaging will probably have to be adapted. This work examines the potential of translating DF imaging to human lungs. The DF contrast due to murine and human lung models was studied using numerical wave propagation simulations, where the lungs were modelled as a volume filled with spheres. Three sphere diameters were used: 39, 60 and 80 μm for the murine model and 200, 300 and 400 μm spheres for the human model. System settings applied for murine lung response modelling were taken from a prototype grating interferometry scanner used in murine lung experiments. The settings simulated for human lung imaging simulations combine the requirements for grating interferometry and conventional chest RX in terms of x-ray energy and pixel size. The DF signal in the simulated murine model was consistent with results from experimental DF data. The simulated linear diffusion coefficient for medium alveoli diameters was found to be (1.31±0.01)⋅10-11 mm-1, 120 times larger than those of human lung tissue ((1.09±0.01)⋅10-13 mm-1). However, as the human thorax is typically a factor 15 times larger than that of murine animals, the overall DF effect in human lungs remains substantial. At the largest lung thickness and for the DF setup simulated, human lungs have an estimated DF response of around 0.31 and murine lungs of 0.23. Dark field imaging can therefore be considered a promising modality for use in human lung imaging.