1. Optical to Planar X-ray Mouse Image Mapping in Preclinical Nuclear Medicine Using Conditional Adversarial Networks
- Author
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Eleftherios Fysikopoulos, Maritina Rouchota, Vasilis Eleftheriadis, Christina-Anna Gatsiou, Irinaios Pilatis, Sophia Sarpaki, George Loudos, Spiros Kostopoulos, and Dimitrios Glotsos
- Subjects
molecular preclinical imaging ,image-to-image translation ,PET ,SPECT ,deep learning ,pix2pix ,Photography ,TR1-1050 ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
In the current work, a pix2pix conditional generative adversarial network has been evaluated as a potential solution for generating adequately accurate synthesized morphological X-ray images by translating standard photographic images of mice. Such an approach will benefit 2D functional molecular imaging techniques, such as planar radioisotope and/or fluorescence/bioluminescence imaging, by providing high-resolution information for anatomical mapping, but not for diagnosis, using conventional photographic sensors. Planar functional imaging offers an efficient alternative to biodistribution ex vivo studies and/or 3D high-end molecular imaging systems since it can be effectively used to track new tracers and study the accumulation from zero point in time post-injection. The superimposition of functional information with an artificially produced X-ray image may enhance overall image information in such systems without added complexity and cost. The network has been trained in 700 input (photography)/ground truth (X-ray) paired mouse images and evaluated using a test dataset composed of 80 photographic images and 80 ground truth X-ray images. Performance metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and Fréchet inception distance (FID) were used to quantitatively evaluate the proposed approach in the acquired dataset.
- Published
- 2021
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