Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.

Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensities to PET µ-map values (e.g. modality artifacts, implanted devices, or contrast agents). This work presents a deep learning-based attenuation correction (DL-AC) method to generate attenuation corrected PET (AC PET) from non-attenuation corrected PET (NAC PET) images for whole-body PET imaging, without the use of structural information. 3D patch-based cycle-consistent generative adversarial networks (CycleGAN) is introduced to include NAC-PET-to-AC-PET mapping and inverse mapping from AC PET to NAC PET, which constrains NAC-PET-to-AC-PET mapping to be closer to one-to-one mapping. Since NAC PET images share similar anatomical structures to the AC PET image but lack contrast information, residual blocks, which aim to learn the differences between NAC PET and AC PET, are used to construct generators of CycleGAN. After training, patches from NAC PET images were fed into NAC-PET-to-AC-PET mapping to generate DL-AC PET patches. DL-AC PET image was then reconstructed through patch fusion. We conducted a retrospective study on 55 datasets of whole-body PET/CT scans to evaluate the proposed method. In comparing DL-AC PET with original AC PET, average mean error (ME) and normalized mean square error (NMSE) of the whole-body were 0.62%  ±  1.26% and 0.72%  ±  0.34%. The average intensity changes measured on sequential PET images with AC and DL-AC on both normal tissues and lesions differ less than 3%. There was no significant difference of the intensity changes between AC and DL-AC PET, which demonstrate DL-AC PET images generated by the proposed DL-AC method can reach a same level to that of original AC PET images. The method demonstrates excellent quantification accuracy and reliability and is applicable to PET data collected on a single PET scanner or hybrid platform with computed tomography (PET/CT) or magnetic resonance imaging (PET/MRI).