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Effects of MR surface coils on PET quantification.

ABSTRACT: The goal of this work was to investigate the effects of MRI surface coils on attenuation-corrected PET emission data. The authors studied the cases where either an MRI or a CT scan would be used to provide PET attenuation correction (AC). Combined MR/PET scanners that use the MRI for PET AC (MR-AC) face the challenge of absent surface coils in MR images and thus cannot directly account for attenuation in the coils. Combining MR and PET images could be achieved by transporting the subject on a stereotactically registered table between independent MRI and PET scanners. In this case, conventional PET CT-AC methods could be used. A challenge here is that high atomic number materials within MR coils cause artifacts in CT images and CT based AC is typically not validated for coil materials.The authors evaluated PET artifacts when MR coils were absent from AC data (MR-AC), or when coil attenuation was measured by CT scanning (CT-AC). They scanned PET phantoms with MR surface coils on a clinical PET/CT system and used CT-AC to reconstruct PET data. The authors then omitted the coil from the CT-AC image to mimic the MR-AC scenario. Images were acquired using cylinder and anthropomorphic phantoms. They evaluated and compared the following five scenarios: (1) A uniform cylinder phantom and head coil scanned and reconstructed using CT-AC; (2) similar emission data (with head coil present) were reconstructed without the head coil in the AC data; (3) the same cylinder scanned without the head coil present (reference scan); (4) a PET torso phantom with a full MR torso coil present in both PET and CT; (5) only half of the separable torso coil present in the PET/CT acquisition. The authors also performed analytic simulations of the first three scenarios.Streak artifacts were present in CT images containing MR surface coils due to metal components. These artifacts persisted after the CT images were converted for PET AC. The artifacts were significantly reduced when half of the separable coil was removed during the scan. CT scans tended to over-estimate the linear attenuation coefficient (micro) of the metal components when using conventional methods for converting from CT number to micro(511 keV). Artifacts were visible outside the phantom in some of the PET emission images, corresponding to the MRI coil geometry. However, only subtle artifacts were apparent in the emission images inside the phantoms. On the other hand, the PET emission image quantitative accuracy was significantly affected: the activity was underestimated by 19% when AC did not include the head coil, and overestimated by 28% when the CT-AC included the head coil.The presence of MR coils during PET or PET/CT scanning can cause subtle artifacts and potentially important quantification errors. Alternative CT techniques that mitigate artifacts should be used to improve AC accuracy. When possible, removing segments of an MR coil prior to the PET/CT exam is recommended. Further, MR coils could be redesigned to reduce artifacts by rearranging placement of the most attenuating materials.

SUBMITTER: MacDonald LR

PROVIDER: S-EPMC3117895 | biostudies-other | 2011 Jun

REPOSITORIES: biostudies-other

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Project description:BACKGROUND:The quality of carotid wall MRI can benefit substantially from a dedicated RF coil that is tailored towards the human neck geometry and optimized for image signal-to-noise ratio (SNR), parallel imaging performance and RF penetration depth and coverage. In last decades, several of such dedicated carotid coils were introduced. However, a comparison of the more successful designs is still lacking. OBJECTIVE:To perform a head-to-head comparison over four dedicated MR carotid surface coils with 4, 6, 8 and 30 coil elements, respectively. MATERIAL AND METHODS:Ten volunteers were scanned on a 3T scanner. For each subject, multiple black-blood carotid vessel wall images were measured using the four coils with different parallel imaging settings. The performance of the coils was evaluated and compared in terms of image coverage, penetration depth and noise correlations between elements. Vessel wall of a common carotid section was delineated manually. Subsequently, images were assessed based on vessel wall morphology and image quality parameters. The morphological parameters consisted of the vessel wall area, thickness, and normalized wall index (wall area/total vessel area). Image quality parameters consisted of vessel wall SNR, wall-lumen contrast-to-noise ratio (CNR), the vessel g-factor, and CNRindex ((wall-lumen signal) / (wall+lumen signal)). Repeated measures analysis of variance (rmANOVA) was applied for each parameter for the averaged 10 slices for all volunteers to assess effect of coil and SENSE factor. If the rmANOVA was significant, post-hoc comparisons were conducted. RESULTS:No significant coil effect were found for vessel wall morphological parameters. SENSE acceleration affected some morphological parameters for 6- and 8-channel coils, but had no effect on the 30-channel coil. The 30-channel coil achieved high acceleration factors (10x) with significantly lower vessel g-factor values (ps ≤ 0.01), but lower vessel wall SNR and CNR values (ps ≤ 0.01). CONCLUSION:All four coils were capable of high-quality carotid MRI. The 30-channel coil is recommended when rapid image acquisition acceleration is required for 3D measurements, whereas 6- and 8-channel coils demonstrated the highest SNR performance.

Project description:Structural MR images concomitantly acquired with PET images can provide crucial anatomic information for precise quantitative analysis. However, in the clinical setting, not all the subjects have corresponding MR images. Here, we developed a model to generate structural MR images from amyloid PET using deep generative networks. We applied our model to quantification of cortical amyloid load without structural MR. Methods: We used florbetapir PET and structural MR data from the Alzheimer Disease Neuroimaging Initiative database. The generative network was trained to generate realistic structural MR images from florbetapir PET images. After the training, the model was applied to the quantification of cortical amyloid load. PET images were spatially normalized to the template space using the generated MR, and then SUV ratio (SUVR) of the target regions was measured by predefined regions of interest. A real MR-based quantification was used as the gold standard to measure the accuracy of our approach. Other MR-less methods-a normal PET template-based, a multiatlas PET template-based, and a PET segmentation-based normalization/quantification-were also tested. We compared the performance of quantification methods using generated MR with that of MR-based and MR-less quantification methods. Results: Generated MR images from florbetapir PET showed signal patterns that were visually similar to the real MR. The structural similarity index between real and generated MR was 0.91 ± 0.04. The mean absolute error of SUVR of cortical composite regions estimated by the generated MR-based method was 0.04 ± 0.03, which was significantly smaller than other MR-less methods (0.29 ± 0.12 for the normal PET template, 0.12 ± 0.07 for the multiatlas PET template, and 0.08 ± 0.06 for the PET segmentation-based methods). Bland-Altman plots revealed that the generated MR-based SUVR quantification was the closest to the SUVRs estimated by the real MR-based method. Conclusion: Structural MR images were successfully generated from amyloid PET images using deep generative networks. Generated MR images could be used as templates for accurate and precise amyloid quantification. This generative method might be used to generate multimodal images of various organs for further quantitative analyses.

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Effects of MR surface coils on PET quantification.

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