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How Real Are Computed Tomography Low Dose Simulations? An Investigational In-Vivo Large Animal Study

Published:January 18, 2023DOI:https://doi.org/10.1016/j.acra.2022.11.008

      Objectives

      CT low-dose simulation methods have gained significant traction in protocol development, as they lack the risk of increased patient exposure. However, in-vivo validations of low-dose simulations are as uncommon as prospective low-dose image acquisition itself. Therefore, we investigated the extent to which simulated low-dose CT datasets resemble their real-dose counterparts.

      Materials and Methods

      Fourteen veterinarian-sedated alive pigs underwent three CT scans on the same third generation dual-source scanner with 2 months between each scan. At each time, three additional scans ensued, with mAs reduced to 50%, 25%, and 10%. All scans were reconstructed using wFBP and ADMIRE levels 1-5. Matching low-dose datasets were generated from the 100% scans using reconstruction-based and DICOM-based simulations. Objective image quality (CT numbers stability, noise, and signal-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1=inferior, 0=equal, 1=superior). The points were averaged for a semiquantitative score, and inter-rater-agreement was measured using Spearman's correlation coefficient. A structural similarity index (SSIM) analyzed the voxel-wise similarity of the volumes. Adequately corrected mixed-effects analysis compared objective and subjective image quality. Multiple linear regression with three-way interactions measured the contribution of dose, reconstruction mode, simulation method, and rater to subjective image quality.

      Results

      There were no significant differences between objective and subjective image quality of reconstruction-based and DICOM-based simulation on all dose levels (p≥0.137). However, both simulation methods produced significantly lower objective image quality than real-dose images below 25% mAs due to noise overestimation (p<0.001; SSIM≤89±3). Overall, inter-rater-agreement was strong (r≥0.68, mean 0.93±0.05, 95% CI 0.92-0.94; each p<0.001). In regression analysis, significant decreases in subjective image quality were observed for lower radiation doses (b ≤ -0.387, 95%CI -0.399 to -0.358; p<0.001) but not for reconstruction modes, simulation methods, raters, or three-way interactions (p≥0.103).

      Conclusion

      Simulated low-dose CT datasets are subjectively and objectively indistinguishable from their real-dose counterparts down to 25% mAs, making them an invaluable tool for efficient low-dose protocol development.

      Abbreviations:

      ADMIRE (Advanced Modeled Iterative Reconstruction), ALARA (As Low As Reasonably Achievable), BMI (Body Mass Index), CTDIvol (Computed Tomography Dose Index), DicomSIM (DICOM-based Low-Dose Simulations), DLP (Dose-length Product), ED (Effective Radiation dose), HU (Hounsfield Units), kV (denoting Tube Voltage), mAs (denoting Tube Current-Exposure Time Product), ReconSIM (Reconstruction-based Low-Dose Simulation), Scan (Reference Real Dose Datasets), SD (Standard Deviation), SSDE (Size-specific Dose Estimate), SSIM (Structural Similarity Index), SNR (Signal-to-Noise Ratio), wFBP (Weighted Filtered Back Projection)
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