Browsing by Author "Vunckx, K"
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- ItemEvaluation of three MRI-based anatomical priors for quantitative PET brain imaging(Institute of Electrical and Electronics Engineers, 2012-03-01) Vunckx, K; Atre, A; Baete, K; Reilhac, A; Deroose, CM; Van Laere, K; Nuyts, JIn 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. © 2012, IEEE
- ItemFramework for the construction of a Monte Carlo simulated brain PET–MR image database(Elsevier, 2014-01-11) Thomas, BA; Erlandsson, K; Drobnjak, I; Pedemonte, S; Vunckx, K; Bousse, A; Reilhac-Laborde, A; Ourselin, S; Hutton, BFSimultaneous PET–MR acquisition reduces the possibility of registration mismatch between the two modalities. This facilitates the application of techniques, either during reconstruction or post-reconstruction, that aim to improve the PET resolution by utilising structural information provided by MR. However, in order to validate such methods for brain PET–MR studies it is desirable to evaluate the performance using data where the ground truth is known. In this work, we present a framework for the production of datasets where simulations of both the PET and MR, based on real data, are generated such that reconstruction and post-reconstruction approaches can be fairly compared. © 2013 Elsevier B.V.
- ItemWhat approach to brain partial volume correction is best for PET/MRI?(Elsevier B.V., 2013-02-21) Hutton, BF; Thomas, BA; Erlandsson, K; Bousse, A; Reilhac-Laborde, A; Kazantsev, D; Pedemonte, S; Vunckx, K; Arridge, SR; Ourselin, SMany partial volume correction approaches make use of anatomical information, readily available in PET/MRI systems but it is not clear what approach is best. Seven novel approaches to partial volume correction were evaluated, including several post-reconstruction methods and several reconstruction methods that incorporate anatomical information. These were compared with an MRI-independent approach (reblurred van Cittert ) and uncorrected data. Monte Carlo PET data were generated for activity distributions representing both 18F FDG and amyloid tracer uptake. Post-reconstruction methods provided the best recovery with ideal segmentation but were particularly sensitive to mis-registration. Alternative approaches performed better in maintaining lesion contrast (unseen in MRI) with good noise control. These were also relatively insensitive to mis-registration errors. The choice of method will depend on the specific application and reliability of segmentation and registration algorithms. (c) 2012 Elsevier Science B.V.