Enhancement of Partial Volume Correction in MR-Guided PET Image Reconstruction by Using MRI Voxel Sizes.

Martin A Belzunce,Andrew J Reader,Abolfazl Mehranian

doi:10.1109/trpms.2018.2881248

Abstract

Positron emission tomography (PET) suffers from poor spatial resolution which results in quantitative bias when evaluating the radiotracer uptake in small anatomical regions, such as the striatum in the brain which is of importance in this paper of neurodegenerative diseases. These partial volume effects need to be compensated for by employing partial volume correction (PVC) methods in order to achieve quantitatively accurate images. Two important PVC methods applied during the reconstruction are resolution modeling, which suffers from Gibbs artifacts, and penalized likelihood using anatomical priors. The introduction of clinical simultaneous PET-MR scanners has attracted new attention for the latter methods and brought new opportunities to use MRI information to assist PET image reconstruction in order to improve image quality. In this context, MR images are usually down-sampled to the PET resolution before being used in MR-guided PET reconstruction. However, the reconstruction of PET images using the MRI voxel size could achieve a better utilization of the high resolution anatomical information and improve the PVC obtained with these methods. In this paper, we evaluate the importance of the use of MRI voxel sizes when reconstructing PET images with MR-guided maximum a posteriori (MAP) methods, specifically the modified Bowsher method. We also propose a method to avoid the artifacts that arise when PET reconstructions are performed in a higher resolution matrix than the standard for a given scanner. The MR-guided MAP reconstructions were implemented with a modified Lange prior that included anatomical information with the Bowsher method. The methods were evaluated with and without resolution modeling for simulated and real brain data. We show that the use of the MRI voxel sizes when reconstructing PET images with MR-guided MAP enhances PVC by improving the contrast and reducing the bias in six different regions of the brain using regional metrics for a single simulated data set and ensemble metrics for ten noise realizations. Similar results were obtained for real data, where a good enhancement of the contrast was achieved. The combination of MR-guided MAP reconstruction with point-spread function modeling and MRI voxel sizes proved to be an attractive method to achieve considerable enhancement of PVC, while reducing and controlling the noise level and Gibbs artifacts.

Highlights

P OSITRON emission tomography (PET) provides quantitative functional images
An exploration of the regularization hyperparameters for the MR-guided maximum a posteriori (MAP) reconstruction with resolution modeling and standard voxel sizes is shown in Fig. 7, where it can be seen that for MRguided reconstructions the δ value chosen did not have any notable impact on the performance since it was possible to find a β value for each of the δ evaluated (δ = 0.03 for a pseudo TV prior and δ = 3 for a pseudo quadratic prior) so as to obtain matched performance
This is a consequence of the low resolution of the PET images and it is exacerbated by the smoothing applied in the regularization, even when anatomical boundaries are used as prior information, and for that reason an important loss of contrast is observed in those cases where resolution modeling is not implemented

Summary

Introduction

It is well known that PET suffers from poor spatial resolution, around 4 mm in clinical scanners, which results in quantitative bias when evaluating the radiotracer uptake in small anatomical regions. In brain imaging the uptake in cortical gray matter is of interest and it has only a few mm width (from 1 to 4.5 mm) and, as a result, the quantification on this region is greatly affected by PVE [3], [4] This effect is important in other smaller regions of the brain, such as the striatum, which is of importance in the assessment of a number of neurological diseases, such as Parkinson’s and Alzheimer’s disease (AD) [5]–[8]. It is important to correct for this effect with partial volume correction (PVC) methods

Results

Discussion

Conclusion