[Generation of the Pseudo CT Image Based on the Deep Learning Technique Aimed for the Attenuation Correction of the PET Image].

Computed tomography (CT) is used for the attenuation correction (AC) of [F-18] fluoro-deoxy-glucose positron emission tomography (PET) image. However, acquisition of a CT image for this purpose requires increasing the radiation dose of the patient. To generate a pseudo-image, a generative adversarial network (GAN) based on deep learning is adopted. The purpose of this study was to generate a pseudo-CT image, using a GAN, for the AC of the PET image, with the aim of reducing the dose of the patient. A set of approximately 15,000 no-AC PET and CT images was used as the training sample, and the CycleGAN was employed as the image generation model. The training samples were inputted in the CycleGAN, and the hyperparameters, i.e., the learning rate, batch size, and number of epochs were set to 0.0001, 1, and 300, respectively. A pseudo-PET image was obtained using a pseudo-CT image, which was used for the AC of the no-AC PET image. The coefficient of similarity between the real and generated pseudo-images was estimated using the peak signal-to-noise ratio (PSNR) , the structural similarity (SSIM), and the dice similarity coefficient (DSC). The average values of PSNR, SSIM, and DSC of the pseudo-CT were 31.0 dB, 0.87, and 0.89, and those of the pseudo-PET were 35.9 dB, 0.90, and 0.95, respectively. The AC for the whole-body PET image could be accomplished using the pseudo-CT image generated via the GAN. The proposed method would be established as the CT-less PET/CT examination.

Influence of the Pixel Sizes of Reference Computed Tomography on Single-photon Emission Computed Tomography Image Reconstruction Using Conjugate-gradient Algorithm.

PURPOSE: The frame-of-reference using computed-tomography (CT) coordinate system on single-photon emission computed tomography (SPECT) reconstruction is one of the advanced characteristics of the xSPECT reconstruction system. The aim of this study was to reveal the influence of the high-resolution frame-of-reference on the xSPECT reconstruction. METHODS: 99mTc line-source phantom and National Electrical Manufacturers Association (NEMA) image quality phantom were scanned using the SPECT/CT system. xSPECT reconstructions were performed with the reference CT images in different sizes of the display field-of-view (DFOV) and pixel. RESULTS: The pixel sizes of the reconstructed xSPECT images were close to 2.4 mm, which is acquired as originally projection data, even if the reference CT resolution was varied. The full width at half maximum (FWHM) of the line-source, absolute recovery coefficient, and background variability of image quality phantom were independent on the sizes of DFOV in the reference CT images. CONCLUSION: The results of this study revealed that the image quality of the reconstructed xSPECT images is not influenced by the resolution of frame-of-reference on SPECT reconstruction.