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A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study

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Abstract

Practical simulations of low-dose CT images have a possibility of being helpful means for optimization of the CT exposure dose. Because current methods reported by several researchers are limited to specific vendor platforms and generally rely on raw sinogram data that are difficult to access, we have developed a new computerized scheme for producing simulated low-dose CT images from real high-dose images without use of raw sinogram data or of a particular phantom. Our computerized scheme for low-dose CT simulation was based on the addition of a simulated noise image to a real high-dose CT image reconstructed by the filtered back-projection algorithm. First, a sinogram was generated from the forward projection of a high-dose CT image. Then, an additional noise sinogram resulting from use of a reduced exposure dose was estimated from a predetermined noise model. Finally, a noise CT image was reconstructed with a predetermined filter and was added to the real high-dose CT image to create a simulated low-dose CT image. The noise power spectrum and modulation transfer function of the simulated low-dose images were very close to those of the real low-dose images. In order to confirm the feasibility of our method, we applied this method to clinical cases which were examined with the high dose initially and then followed with a low-dose CT. In conclusion, our proposed method could simulate the low-dose CT images from their real high-dose images with sufficient accuracy and could be used for determining the optimal dose setting for various clinical CT examinations.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers 22611014 and 15K09898.

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Correspondence to Tomomi Takenaga.

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The authors declare that they have no conflict of interest.

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Takenaga, T., Katsuragawa, S., Goto, M. et al. A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study. Radiol Phys Technol 9, 44–52 (2016). https://doi.org/10.1007/s12194-015-0332-3

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  • DOI: https://doi.org/10.1007/s12194-015-0332-3

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