Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 34882262.
- Also identified by DOI 10.1007/s00259-021-05637-0.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET. One of the approaches uses a CNN to generate μ-maps from the non-attenuation-corrected (NAC) PET images (μ-CNN<sub>NAC</sub>). In the other method, CNN is used to improve the accuracy of μ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (μ-CNN<sub>MLAA</sub>). We investigated the improvement in the CNN performance by combining the two methods (μ-CNN<sub>MLAA+NAC</sub>) and the suitability of μ-CNN<sub>NAC</sub> for providing the scatter distribution required for MLAA reconstruction. Image data from <sup>18</sup>F-FDG (n = 100) or <sup>68</sup> Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. The error of the attenuation correction factors estimated using μ-CT and μ-CNN<sub>NAC</sub> was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from μ-CNN<sub>NAC</sub>. However, CNN<sub>NAC</sub> provided less accurate bone structures in the μ-maps, while the best results in recovering the fine bone structures were obtained by applying CNN<sub>MLAA+NAC</sub>. Additionally, the μ-values in the lungs were overestimated by CNN<sub>NAC</sub>. Activity images (λ) corrected for attenuation using μ-CNN<sub>MLAA</sub> and μ-CNN<sub>MLAA+NAC</sub> were superior to those corrected using μ-CNN<sub>NAC</sub>, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNN<sub>NAC</sub> and CNN<sub>MLAA</sub> approaches was insignificant (percent error for lung cancer lesions, λ-CNN<sub>NAC</sub> = 5.45% ± 7.88%; λ-CNN<sub>MLAA</sub> = 1.21% ± 5.74%; λ-CNN<sub>MLAA+NAC</sub> = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNN<sub>NAC</sub> = 1.37% ± 5.16%; λ-CNN<sub>MLAA</sub> = 0.23% ± 3.81%; λ-CNN<sub>MLAA+NAC</sub> = 0.05% ± 3.49%). The use of CNN<sub>NAC</sub> was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNN<sub>MLAA</sub> outperformed CNN<sub>NAC.</sub>
Medical subject headings
- Deep Learning
- Positron Emission Tomography Computed Tomography