![]() This tracking information can be used to compensate for subject motion before, after, or during image reconstruction. ![]() To minimize the effect of head motion, other than methods using restraints in an attempt to eliminate movement ( 18), motion correction (MC) can be done through motion tracking with an external system, such as an optical system with cameras or markers ( 19), or with image-based systems. In addition, methods that combine the strengths of both methods have been proposed ( 15– 17) one of these methods has been shown to produce accurate μ-maps from one T1-weighted MR morphologic image alone ( 16). In the former, an atlas that provides the linear attenuation coefficients is warped and coregistered to each new subject ( 10, 11) in the latter, discrete coefficients are assigned to classes of voxels segmented through image processing or machine learning ( 12– 14). There are 2 main approaches for MR-based AC: atlas-based and segmentation-based techniques. Since the transmission or CT-based methods traditionally used to produce attenuation maps (μ-maps) that describe tissue attenuation are not available in integrated PET/MRI scanners, MR-based methods had to be developed for attenuation correction (AC) ( 9). These subject-dependent effects are especially apparent in dementia subjects, who are more prone to motion ( 7) and may have brain atrophy with regional variability ( 8). Third, the limited spatial resolution leads in PET to under- or overestimation in tissue activity concentrations (i.e., partial-volume effects ) that depend on the activity distribution and the size and shape of the structures from which the measurement is being made ( 6). Second, subject motion is difficult to avoid, particularly in patients with disorders of the central nervous system, and leads to degradation of the images when it has large amplitude ( 5). First, the attenuation of the 511-keV annihilation photons needs to be accounted for in both qualitative and quantitative studies. Although PET has the potential to provide highly quantitative measures of the radiotracer concentration over time, its accuracy is confounded by several factors. Importantly, in addition to providing a simultaneous look at these features through the strengths of each modality individually, the combined tool allows us to leverage the MR information to improve the quantification of PET data ( 1). Interest in simultaneous PET and MRI (PET/MRI) has been growing because of its ability to provide complementary, and concurrent, information about morphologic, functional, metabolic, and neurochemical changes in many neurologic disorders ( 1, 2), including dementia ( 1– 4), and whole-body applications ( 3). Conclusion: We have shown that the spatiotemporally correlated data acquired using a single MR sequence can be used for PET attenuation, motion, and partial-volume effects corrections and that the MaPET method may enable more accurate assessment of pathologic changes in dementia and other brain disorders. The optimized images were also shown using the Cohen’s d metric to achieve a greater effect size in distinguishing cortical regions with hypometabolism from regions of preserved metabolism. Results: The optimized PET images reconstructed with MaPET were superior in image quality to images reconstructed using only AC, with high signal-to-noise ratio and low coefficient of variation (5.08 and 0.229 in a composite cortical region compared with 3.12 and 0.570, P < 10 −4 for both comparisons). Region-based analyses were performed to assess the quality of images generated through various stages of PET data optimization. The anatomy provided by the MR volume was incorporated into the PET image reconstruction using a kernel-based method. The embedded navigators were used to derive head motion estimates for event-based PET MC. Methods: For AC, voxelwise linear attenuation coefficient maps were generated using an SPM8-based method on the MR volume. Using the information from one simultaneously acquired morphologic MR sequence with embedded navigators for MR motion correction (MC), we propose an efficient method, MR-assisted PET data optimization (MaPET), for attenuation correction (AC), PET MC, and anatomy-aided reconstruction. ![]() A main advantage of PET is that it provides quantitative measures of the radiotracer concentration, but its accuracy is confounded by factors including attenuation, subject motion, and limited spatial resolution.
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