An SPM8-Based Approach for Attenuation Correction Combining Segmentation and Nonrigid Template Formation: Application to Simultaneous PET/MR Brain Imaging

Abstract

We present an approach for head MR-based attenuation correction (MR-AC) based on the Statistical Parametric Mapping (SPM8) software that combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (µ-maps) from MR data in integrated PET/MR scanners. Methods Coregistered anatomical MR and CT images acquired in 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray and white matter, cerebro-spinal fluid, bone and soft tissue, and air), which were then non-rigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomical MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients (LACs) to be used for AC of PET data. The method was validated on sixteen new subjects with brain tumors (N=12) or mild cognitive impairment (N=4) who underwent CT and PET/MR scans. The µ-maps and corresponding reconstructed PET images were compared to those obtained using the gold standard CT-based approach and the Dixon-based method available on the Siemens Biograph mMR scanner. Relative change (RC) images were generated in each case and voxel- and region of interest (ROI)-based analyses were performed. Results The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain LACs (RC=1.38%±4.52%) compared to the gold standard. Similar results (RC=1.86±4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and ROI-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87±5.0% and 2.74±2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0±10.25% and 9.38±4.97%, respectively). Areas closer to skull showed the largest improvement. Conclusion We have presented an SPM8-based approach for deriving the head µ-map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and only requires the morphological data acquired with a single MR sequence. The method is very accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks.

Quantitative analysis of MRI-guided attenuation correction techniques in time-of-flight brain PET/MRI

“…These results among others reported in the literature (Hofmann et al, 2008, Izquierdo-Garcia et al, 2014, clearly justify the need for novel more accurate AC methods that account for bones. Atlas registration-based methods have therefore been proposed as a viable attenuation correction technique for the prediction of bones from a patient population.…”

“…The authors reported that their method resulted in relative mean errors of b 0.6% for FDG and b 0.1% for Florbetapir, on average, with a standard deviation of 1% for both tracers. This study demonstrated that this multi-atlas registration technique outperforms the single-atlas registration AC method proposed by Izquierdo-Garcia et al (2014).…”

“…The authors recently improved and validated their method using two different PET radiotracers for brain imaging reporting a mean error of b 2%. Recently, Izquierdo-Garcia et al (2014) improved atlas registration through segmentation of the target MR images into 6 distinct regions (using statistical parametric mapping software) followed by its registration to a CT/MRI atlas space. For the atlas dataset, a CT template is calculated by averaging the co-registered CT images.…”

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

“…Finally, PET AC with RESOLUTE benefits from the assignment of continuous bone values. Using a single value for bone (vendorprovided UTE, (Anazodo et al 2014)), or using an incorrect pseudo CT skull value (Burgos et al 2014, Izquierdo-Garcia et al 2014 in the MR-AC map, can in some individuals result in large regional PET errors in the brain close to the skull. This is exemplified in figure 11 where failure to compensate for the high bone density results in errors in the PET image of up to 25% in the cerebellum.…”

“…The atlas-based approaches often involve co-registration of one or more MR images to a database of patients, followed by a back-warp of the CT image corresponding to the best match (Hofmann et al 2008, Burgos et al 2014, Izquierdo-Garcia et al 2014. These types of methods depend on a strong inter-and intra-subject registration, where misregistration due to outlier patients can introduce PET errors.…”