Post-Reconstruction Deconvolution of PET Images by Total Generalized Variation Regularization
This work addresses image quality issues in nuclear medicine and radiotherapy, but it is incremental as it builds on existing inverse problem methods with a new regularization approach.
The authors tackled the problem of low resolution and high noise in PET images by proposing a post-reconstruction deconvolution method using total generalized variation regularization, achieving improved image quality as demonstrated on synthetic and real patient data.
Improving the quality of positron emission tomography (PET) images, affected by low resolution and high level of noise, is a challenging task in nuclear medicine and radiotherapy. This work proposes a restoration method, achieved after tomographic reconstruction of the images and targeting clinical situations where raw data are often not accessible. Based on inverse problem methods, our contribution introduces the recently developed total generalized variation (TGV) norm to regularize PET image deconvolution. Moreover, we stabilize this procedure with additional image constraints such as positivity and photometry invariance. A criterion for updating and adjusting automatically the regularization parameter in case of Poisson noise is also presented. Experiments are conducted on both synthetic data and real patient images.