Improved PET quantification and harmonization by adaptive denosing

Abstract

Quantification in positron emission tomography (PET) is subject to bias due to physical and technical limitations. The goal ofquantitative harmonization is to achieve comparable measurements between different scanners, thus enabling multicenter clinical trials. Clinical guidelines, such as those from the European Association ofNuclear Medicine (EANM), recommend harmonizing PET reconstructions to bring contrast recovery coefficients (CRCs) within specifications. However, these
harmonized reconstructions can show quantitative biases. In this work we improve harmonization by using a novel adaptive filtering scheme. Our goal was to obtain low quantification bias and high peak signal to noise ratio (PSNR) values at the same time.
For this purpose, a novel three-stage adaptive denoising filter was implemented. Filter parameters were optimized to achieve both high PSNRin a digital brain phantom and low quantitative bias ofmaximumCRC values (CRCmax) obtained from a National Electrical Manufacturers Association (NEMA) PET image qualityphantom. TheNEMAphantom was scanned on several PET/CT scanners and reconstructed without postfilters. The optimal filter settings found for a training dataset were then applied to testing reconstructions from other scanners. Harmonization limits were defined using the 95%confidence intervals across reconstructions.
Average CRCmax values close to unity (±5%) were achieved for spheres with diameter equal or greater than 13mmfor the training dataset. PSNR values were comparable to other state-of-the-art filter results. Using the same optimal filter settings for the testing datasets, similar quantitative results were found. Lesion conspicuitywas improved on clinical scans when compared with EANM reconstructions, with no visible artifacts. In this way, our three-stage adaptive filter achieved state-of-the-art quantitative performance for PET imaging. Harmonization tolerances with lower bias and variance tan EANM guidelines were achieved for a variety ofscanner models. CRCmax values were close to unityand the quantification variability was reduced when compared with standard reconstructions.