Abstract
With the advent of time-of-flight (TOF) PET scanners, joint maximum-likelihood reconstruction of activity and attenuation (MLAA) maps has recently regained attention for the estimation of PET attenuation maps from emission data. However, the estimated attenuation and activity maps are scaled by unknown scaling factors. We recently demonstrated that in hybrid PET-MR, the scaling issue of this algorithm can be effectively addressed by imposing MR spatial constraints on the estimation of attenuation maps using a penalized MLAA (P-MLAA+) algorithm. With the advent of simultaneous PET-MR systems, MRI-guided PET image reconstruction has also gained attention for improving the quantitative accuracy of PET images, usually degraded by noise and partial volume effects. The aim of this study is therefore to increase the benefits of MRI information for improving the quantitative accuracy of PET images by exploiting MRI-based anatomical penalty functions to guide the reconstruction of both activity and attenuation maps during their joint estimation. We employed an anato-functional joint entropy penalty function for the reconstruction of activity and an anatomical quadratic penalty function for the reconstruction of attenuation. The resulting algorithm was referred to as P-MLAA++ since it exploits both activity and attenuation penalty functions. The performance of the P-MLAA algorithms were compared with MLAA and the widely used activity reconstruction algorithms such as maximum likelihood expectation maximization (MLEM) and penalized MLEM (P-MLEM) both corrected for attenuation using a conventional MRI segmentation-based attenuation correction (MRAC) method. The studied methods were evaluated using simulations and clinical studies taking the PET image reconstructed using reference CT-based attenuation maps as a reference. The simulation results showed that the proposed method can notably improve the visual quality of the PET images by reducing noise while preserving structural boundaries and at the same time improving the quantitative accuracy of the PET images. Our clinical reconstruction results showed that the MLEM-MRAC, P-MLEM-MRAC, MLAA, P-MLAA+ and P-MLAA++ algorithms result in, on average, quantification errors of −13.5 ± 3.1%, −13.4 ± 3.1%, −2.0 ± 6.5%, −3.0 ± 3.5% and −4.2 ± 3.6%, respectively, in different regions of the brain. In conclusion, whilst the P-MLAA+ algorithm showed the best overall quantification performance, the proposed P-MLAA++ algorithm provided simultaneous partial volume and attenuation corrections with only a minor compromise of PET quantification.
Highlights
Following the successful combination of positron emission tomography (PET) and x-ray computed tomography (CT), integrated PET and magnetic resonance imaging (MRI) has recently been introduced in clinical practice to further increase the advantages of multimodality imaging, since MRI provides superior soft tissue contrast and complementary functional information to PET molecular imaging data compared to CT
The performance of the P-maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithms were compared with MLAA and the widely used activity reconstruction algorithms such as maximum likelihood expectation maximization (MLEM) and penalized MLEM (P-MLEM) both corrected for attenuation using a conventional MRI segmentationbased attenuation correction (MRAC) method
The results show that the un-regularized reconstructions at the top of the figure suffer from noise and partial volume effect (PVE) and deviate from the piece-wise smooth true activity image
Summary
Following the successful combination of positron emission tomography (PET) and x-ray computed tomography (CT), integrated PET and magnetic resonance imaging (MRI) has recently been introduced in clinical practice to further increase the advantages of multimodality imaging, since MRI provides superior soft tissue contrast and complementary functional information to PET molecular imaging data compared to CT. The atlas-based methods aim at generating a pseudo CT image by registration of multiple MR-CT pairs (atlas) to the subject's MR image and predict CT Hounsfield units using a variety of methods ranging from a simple averaging of the registered CT images to patch-based Gaussian regression methods (Hofmann et al, 2011) These AC methods have shown promising results in brain PET attenuation correction, since atlas registration is fairly accurate for the brain compared to whole body. Following the rebirth of TOF PET technology, the emission-based AC methods has regained popularity for the estimation of attenuation maps from PET emission data using the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm (Rezaei et al, 2012) These AC methods are especially promising for PET-MR systems where the employed attenuation maps are not patient specific (e.g. in the presence of metallic implants (Mehranian et al, 2016b)). In PET-MR imaging, we previously demonstrated this scaling issue can be effectively addressed using MR-constrained Gaussian penalty functions (Mehranian and Zaidi, 2015b), which can facilitate the application of these new AC methods in clinical practice (Mehranian and Zaidi, 2015a)
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