Anatomically-guided deconvolution of PET using directional total variation regularization

Abstract. Positron emission tomography (PET) provides quantitative functional imaging of biomarkers unavailable in other modalities, however, images are of relatively low resolution compared to modalities such as magnetic resonance imaging (MRI). A typical approach is to reconstruct to a higher resolution and regularize using a structural image, but there are practical limitations to this approach. Alternatively, post-reconstruction approaches involve image-based correction, but typically rely on a segmentation which may be difficult or even ambiguous to find, depending on the anatomical region or deformities. Here, we perform super-resolution by utilising iterative deconvolution, regularized by minimizing shared directional total variation (dTV) with an anatomical MRI image. We present results on synthetic and clinical data. For the former, PET acquisitions were simulated using an analytic PET simulation. The Gaussian blurring model parameters for deconvolution were optimized on a simplistic phantom simulation with a total variation prior. This model was then applied to deconvolve realistic synthetic data using dTV, which was synthesized to include PET-unique lesions. The model was also applied to a single 18F-florbetaben study acquired over 10 minutes. Gray matter-white matter contrast increased using dTV compared with baseline, however, where an accurate segmentation is available, traditional partial volume correction techniques are superior. Hence, dTV-regularised deconvolution can perform PVC and super-resolution in situations where a reliable segmentation cannot be achieved. With appropriate hyper-parameter selection, dTV deconvolution can preserve PET-unique features.