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.

The combination of magnetic resonance imaging (MR) and positron emission tomography (PET) scanners can provide a powerful tool for clinical diagnosis and investigation. Among the challenges of developing a combined scanner, obtaining attenuation maps for PET reconstruction is of critical importance. This requires accounting for the presence of MR hardware in the field of view. The attenuation introduced by this hardware cannot be obtained from MR data. We propose the creation of attenuation models of MR hardware, to be registered into the MR-based attenuation map prior to PET reconstruction. Two steps were followed to assess the viability of this method. First, transmission and emission measurements were performed on MR components (RF coils and medical probes). The severity of the artifacts in the reconstructed PET images was evaluated. Secondly, a high-exposure computed tomography (CT) scan was used to obtain a model of a head coil. This model was registered into the attenuation map of PET/CT scans of a uniform phantom fitted with the coil. The resulting PET images were compared to the PET/CT reconstruction in the absence of coils. The artifacts introduced by misregistration of the model were studied. The transmission scans revealed 17% count loss due to the presence of head and neck coils in the field of view. Important sources of attenuation were found in the lock, signal cables and connectors. However, the worst source of attenuation was the casing between both coils. None of the measured medical probes introduced a significant amount of attenuation. Concerning the attenuation model of the head coil, reconstructed PET images with model-based correction were comparable to the reference PET/CT reconstruction. However, inaccuracies greater than 1-2 mm in the axial positioning of the model led to important artifacts. In conclusion, the results show that model-based attenuation correction is possible. Using a high-exposure scan to create an attenuation model of the coils has been proved feasible. However, adequate registration of the model is mandatory.

Metal implants such as hip prostheses and dental fillings produce streak and star artifacts in the reconstructed computed tomography (CT) images. Due to these artifacts, the CT image may not be diagnostically usable. A new reconstruction procedure is proposed that reduces the streak artifacts and that might improve the diagnostic value of the CT images. The procedure starts with a maximum a posteriori (MAP) reconstruction using an iterative reconstruction algorithm and a multimodal prior. This produces an artifact-free constrained image. This constrained image is the basis for an image-based projection completion procedure. The algorithm was validated on simulations, phantom and patient data, and compared with other metal artifact reduction algorithms.

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.

The proposed technique successfully extends the MR FoV in MR-based attenuation correction and shows an improvement of PET quantification in whole-body MR/PET hybrid imaging. In comparison to the PET-based completion of the truncated body contour, the proposed method is also applicable to specialized PET tracers with little uptake in the arms and might reduce the computation time by obviating the need for iterative calculations of the PET emission data beyond those required for reconstructing images.