Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

Abstract

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (MeanRHD, and STDRHD CVRHD) of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the (CVRHD) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.

3D task‐transfer function representation of the signal transfer properties of low‐contrast lesions in FBP‐ and iterative‐reconstructed CT

“…Results from this study parallel those obtained in a previous study involving high contrast lung lesions (~1000 HU), where TTF was used to model the transfer properties of a CT imaging system . Using a 3D Hausdorff distance metric, Robins et al.…”

“…Using a 3D Hausdorff distance metric, Robins et al. showed that physical and simulated lesion boundaries differed by less than 5%, across multiple doses and reconstruction settings . They also found that the volumetric difference between physical and simulated lesions was <3% on average 15 .…”

Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases

“…Digital lesion insertion was conducted via a previously validated 3D image domain insertion technique described as Technique B in an earlier publication (see Fig. ).…”

“…Also, it can often be challenging to establish ground truth in clinical images. Phantom studies are often used as a substitute, but they bear several general limitations, the foremost being a lack of realistic anatomical complexity . Indeed, even sophisticated anthropomorphic phantoms cannot sufficiently simulate lung and liver parenchyma .…”

“…This approach allows us to surpass the capabilities of clinical studies and model a myriad of realistic disease conditions without sample size limitations or the need for repetitive patient image acquisitions. We have previously demonstrated the use of hybrid datasets in quantitative CT applications, and in reader and technology evaluation tasks . This reference database can allow for investigations of new applications and software tools.…”