In routine whole-body PET/MR hybrid imaging, attenuation correction (AC) is usually performed by segmentation methods based on a Dixon MR sequence providing up to 4 different tissue classes. Because of the lack of bone information with the Dixon-based MR sequence, bone is currently considered as soft tissue. Thus, the aim of this study was to evaluate a novel model-based AC method that considers bone in whole-body PET/MR imaging. Methods The new method (“Model”) is based on a regular 4-compartment segmentation from a Dixon sequence (“Dixon”). Bone information is added using a model-based bone segmentation algorithm, which includes a set of prealigned MR image and bone mask pairs for each major body bone individually. Model was quantitatively evaluated on 20 patients who underwent whole-body PET/MR imaging. As a standard of reference, CT-based μ-maps were generated for each patient individually by nonrigid registration to the MR images based on PET/CT data. This step allowed for a quantitative comparison of all μ-maps based on a single PET emission raw dataset of the PET/MR system. Volumes of interest were drawn on normal tissue, soft-tissue lesions, and bone lesions; standardized uptake values were quantitatively compared. Results In soft-tissue regions with background uptake, the average bias of SUVs in background volumes of interest was 2.4% ± 2.5% and 2.7% ± 2.7% for Dixon and Model, respectively, compared with CT-based AC. For bony tissue, the −25.5% ± 7.9% underestimation observed with Dixon was reduced to −4.9% ± 6.7% with Model. In bone lesions, the average underestimation was −7.4% ± 5.3% and −2.9% ± 5.8% for Dixon and Model, respectively. For soft-tissue lesions, the biases were 5.1% ± 5.1% for Dixon and 5.2% ± 5.2% for Model. Conclusion The novel MR-based AC method for whole-body PET/MR imaging, combining Dixon-based soft-tissue segmentation and model-based bone estimation, improves PET quantification in whole-body hybrid PET/MR imaging, especially in bony tissue and nearby soft tissue.

No abstract

Simultaneous PET/MR of the brain is a promising new technology for characterizing patients with suspected cognitive impairment or epilepsy. Unlike CT though, MR signal intensities do not provide a direct correlate to PET photon attenuation correction (AC) and inaccurate radiotracer standard uptake value (SUV) estimation could limit future PET/MR clinical applications. We tested a novel AC method that supplements standard Dixon-based tissue segmentation with a superimposed model-based bone compartment. Methods We directly compared SUV estimation for MR-based AC methods to reference CT AC in 16 patients undergoing same-day, single 18FDG dose PET/CT and PET/MR for suspected neurodegeneration. Three Dixon-based MR AC methods were compared to CT – standard Dixon 4-compartment segmentation alone, Dixon with a superimposed model-based bone compartment, and Dixon with a superimposed bone compartment and linear attenuation correction optimized specifically for brain tissue. The brain was segmented using a 3D T1-weighted volumetric MR sequence and SUV estimations compared to CT AC for whole-image, whole-brain and 91 FreeSurfer-based regions-of-interest. Results Modifying the linear AC value specifically for brain and superimposing a model-based bone compartment reduced whole-brain SUV estimation bias of Dixon-based PET/MR AC by 95% compared to reference CT AC (P < 0.05) – this resulted in a residual −0.3% whole-brain mean SUV bias. Further, brain regional analysis demonstrated only 3 frontal lobe regions with SUV estimation bias of 5% or greater (P < 0.05). These biases appeared to correlate with high individual variability in the frontal bone thickness and pneumatization. Conclusion Bone compartment and linear AC modifications result in a highly accurate MR AC method in subjects with suspected neurodegeneration. This prototype MR AC solution appears equivalent than other recently proposed solutions, and does not require additional MR sequences and scan time. These data also suggest exclusively model-based MR AC approaches may be adversely affected by common individual variations in skull anatomy.

Nous considérons le problème du recalage non-rigide entre images de modalités différentes. Nous proposons un cadre général qui repose sur une formulation variationnelle, que nous appliquons sous la forme de trois algorithmes de recalage multimodal : recalage supervisé par apprentissage de la loi jointe, maximisation de l'information mutuelle, et maximisation du rapport de corrélation. Pour permettre une régularisation de la solution, nous utilisons un opérateur issu de la théorie de l'élasticité. Nous considérons aussi une méthode de régularisation avec préservation des contours. Des résultats expérimentaux préliminaires sur des images synthétiques et des données IRM sont présentés.