Revealing Anatomical Structures in PET to Generate CT for Attenuation Correction

Positron emission tomography (PET) is a molecular imaging technique relying on a step, namely attenuation correction (AC), to correct radionuclide distribution based on pre-determined attenuation coefficients. Conventional AC techniques require additionally-acquired computed tomography (CT) or magnetic resonance (MR) images to calculate attenuation coefficients, which increases imaging expenses, time costs, or radiation hazards to patients, especially for whole-body scanners. In this paper, considering technological advances in acquiring more anatomical information in raw PET images, we propose to conduct attenuation correction to PET by itself. To achieve this, we design a deep learning based framework, namely anatomical skeleton-enhanced generation (ASEG), to generate pseudo CT images from non-attenuation corrected PET images for attenuation correction. Specifically, ASEG contains two sequential modules, i.e., a skeleton prediction module and a tissue rendering module. The former module first delineates anatomical skeleton and the latter module then renders tissue details. Both modules are trained collaboratively with specific anatomical-consistency constraint to guarantee tissue generation fidelity. Experiments on four public PET/CT datasets demonstrate that our ASEG outperforms existing methods by achieving better consistency of anatomical structures in generated CT images, which are further employed to conduct PET attenuation correction with better similarity to real ones. This work verifies the feasibility of generating pseudo CT from raw PET for attenuation correction without acquising additional images. The associated implementation is available at https://github.com/YongshengPan/ASEG-for-PET2CT.