Comparison between reconstruction-incorporated super-resolution and super-resolution as a post-processing step for motion correction in PET

Abstract

Super-resolution (SR) aims at generating a high resolution image or image sequence from several slightly different low-resolution images. In previous work, we have showed that a Maximum A Posteriori (MAP) SR algorithm can yield significant image quality enhancement of the individual respiratory gated PET frames. However, in the past it has been also argued that image-based motion correction algorithms yield sub-optimal results, particularly when images are reconstructed using iterative methods. Here, we evaluate the results incorporating MAP SR directly as part of the One-Step-Late (OSL) reconstruction algorithm. The evaluation used GATE simulated data. The IEC phantom with lesions between 10–37mm (contrast 8/1) was used to simulate 200 noise realizations of 6 frames, each shifted by 3mm along the z-axis relative to each other. The algorithm was also tested on four realistic phantoms. Gated respiratory images were reconstructed with the OPL-EM algorithm. Superresolution was performed through a MAP algorithm either as part of the reconstruction process or as a post-processing step applied to the individually reconstructed gated frames. A Huber prior was used as regularization term while the function yielded by the MAP method was optimized through a quasi-Newton algorithm. We compared signal-to-noise ratio (SNR) and contrast, as well as the bias-variance tradeoff for each algorithm. SR incorporated reconstruction led to 9% and 14% higher SNR and contrast respectively compared to the image-based SR. The bias-variance tradeoff showed that reconstruction-incorporated SR has a bias and variance of 39% lower and 60% higher respectively compared to image-based SR.