RESUMO
BackgroundRadiomics and other modern clinical decision-support algorithms are emerging as the next frontier for diagnostic and prognostic medical imaging. However, heterogeneities in image characteristics due to variations in imaging systems and protocols hamper the advancement of reproducible feature extraction pipelines. There is a growing need for realistic patient-based phantoms that accurately mimic human anatomy and disease manifestations to provide consistent ground-truth targets when comparing different feature extraction or image cohort normalization techniques. Materials and MethodsPixelPrint was developed for 3D-printing lifelike lung phantoms for computed tomography (CT) by directly translating clinical images into printer instructions that control the density on a voxel-by-voxel basis. CT datasets of three COVID-19 pneumonia patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Linear mixed models were utilized to evaluate effect sizes of evaluating phantom as opposed to patient images. Finally, PixelPrints reproducibility was evaluated by producing four phantoms from the same clinical images. ResultsEstimated mean differences between patient and phantom images were small (0.03-0.29, using a 1-5 scale). Effect size assessment with respect to rating variabilities revealed that the effect of having a phantom in the image is within one-third of the inter- and intra-reader variabilities. PixelPrints production reproducibility tests showed high correspondence among four phantoms produced using the same patient images, with higher similarity scores between high-dose scans of the different phantoms than those measured between clinical-dose scans of a single phantom. ConclusionsWe demonstrated PixelPrints ability to produce lifelike 3D-printed CT lung phantoms reliably. These can provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols, as well as for optimizing scan protocols with realistic patient-based phantoms.
