PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for validation of computed tomography post-processing and inference algorithms
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Abstract
Background
Radiomics 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 Methods
PixelPrint 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, PixelPrint’s reproducibility was evaluated by producing four phantoms from the same clinical images.
Results
Estimated 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. PixelPrint’s 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.
Conclusions
We demonstrated PixelPrint’s 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.
Article activity feed
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SciScore for 10.1101/2022.05.06.22274739: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization Finally, images from both the phantoms and the corresponding patient images were randomized separately each reader evaluation. Blinding Importantly, the participating radiologists were told that they were taking part in a “CT lung image evaluation study” and were completely unaware of the fact that the reviewed datasets included phantom images, which is why this study can be considered a “completely blinded” reader study. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources For each patient, clinical DICOM images reconstructed with a sharp kernel (Table 1) were converted into 3D-printer … SciScore for 10.1101/2022.05.06.22274739: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization Finally, images from both the phantoms and the corresponding patient images were randomized separately each reader evaluation. Blinding Importantly, the participating radiologists were told that they were taking part in a “CT lung image evaluation study” and were completely unaware of the fact that the reviewed datasets included phantom images, which is why this study can be considered a “completely blinded” reader study. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources For each patient, clinical DICOM images reconstructed with a sharp kernel (Table 1) were converted into 3D-printer instructions using PixelPrint software. PixelPrintsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study does have limitations. First, while the reader study included a large sample size of images (210 per reader), these images originated from only three clinical patient scans representing three levels of COVID-19 severity. Second, our study focused on a specific clinical indication, i.e., diagnosis of COVID-19 pneumonia. Further studies are required to validate the adequacy of PixelPrint for other lung imaging indications, e.g., lung nodule detection. Nevertheless, our results provide compelling evidence that PixelPrint can readily serve as an accurate tool for optimization of disease-targeting protocols and for experimental validation of novel inference algorithms, such as radiomics and predictive AI. In conclusion, we have demonstrated PixelPrint’s ability to produce realistic 3D-printed phantoms reliably. As the utilization of these phantoms will grow, they will become more beneficial to the entire community and enable standardization of tests and comparisons of evaluation of advanced medical inference algorithms. For this, we offer copies of the phantoms presented in this study, as well as phantoms based on specific CT images, for the larger medical, academic, and industrial CT community (visit www.pennmedicine.org/CTResearch/PixelPrint).
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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