Evaluation of Three MRI-Based Anatomical Priors for Quantitative PET Brain Imaging

Abstract

In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems.

The Use of Anatomical Information for Molecular Image Reconstruction Algorithms: Attenuation/Scatter Correction, Motion Compensation, and Noise Reduction

“…21 such as a negative mutual information or negative joint entropy [49,53,110]. A recent study showed that Bowsher prior-based image reconstruction [109] yielded the best image quality among edge information-based method, region-based method, and joint entropy-based method [52]. Iterative image reconstruction requires evaluating the gradient of regularizers at every (sub-)iterations, so computation complexity for using anatomical information-based regularizers is high.…”

“…Ideas of using structural couplings between molecular and anatomical images for reconstruction have been studied a couple of decades ago [41][42][43]. Recently, interesting advances for noise reduction of molecular images using anatomical information have been introduced with state-of-the-art methods for post-reconstruction filtering [44][45][46][47] or regularization in inverse problems [48][49][50][51][52][53].…”

Voxel-based comparison of state-of-the-art reconstruction algorithms for 18F-FDG PET brain imaging using simulated and clinical data

“…Hence, an improvement in one figure of merit does not imply an improvement of all possible figures of merit (8). To suppress noise and Gibbs artifacts, resolution modeling is best combined with the use of anatomical prior information during, e.g., a maximum a posteriori (MAP) image reconstruction (9). In addition, detection accuracy of hypometabolic regions can be significantly improved in this way (10,11).…”

Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors

“…Given this ill-posed problem of PET image reconstruction with low-count projection data, in the maximum likelihood (ML) PET reconstruction framework, penalised likelihood (PL) reconstruction (or equivalently maximum a posteriori, MAP ) has been extensively studied [1][2][3][4][5][6][7]. Such methods involve adding a regularisation (penalty) term to the log likelihood function, and thus the effective forward model complexity can be controlled by changing the weight of this regularisation.…”

“…Among the penalty models, anatomical images acquired by high-resolution magnetic resonance (MR) or X-ray computed tomography (CT) from the same subject are considered useful, as they provide the prior information on the underlying structures. Recent reviews on using anatomical prior information for PET image reconstruction can be found in [5,6,10], and the Bowsher method [4] which encourages PET image smoothness over the neighbour voxels selected from the anatomical image, was found to achieve better performance while being relatively efficient computationally compared to other methods [5]. Apart from the penalised likelihood PET reconstruction frameworks, very recently an alternative perspective of using the image-derived prior was proposed in [11], by incorporating the prior information into the image representation via kernel functions, and the regularisation was applied to the PET forward model.…”