Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

Pogam, Adrien Le; Hatt, Mathieu; Descourt, Patrice; Boussion, Nicolas; Tsoumpas, Charalampos; Turkheimer, Federico; Prunier-Aesch, Caroline; Baulieu, Jean-Louis; Guilloteau, Denis; Visvikis, Dimitris

doi:10.1118/1.3608907

Cited by 41 publications

(35 citation statements)

References 32 publications

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“…The 'A trous' wavelet transform used in this study (also known as 'stationary wavelet transform') is an algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transform [Sekhar and Giri Prasad, 2011]. This algorithm has been applied in signal denoising and medical imaging in order to reduce the noise and to sharpen the image edges [le Pogam et al, 2011]. To our knowledge, this method had not been used for temporal bone imaging.…”

Section: Discussionmentioning

confidence: 99%

Is Electrode-Modiolus Distance a Prognostic Factor for Hearing Performances after Cochlear Implant Surgery?

et al. 2013

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The aim of this study was to evaluate electrode array position in relation to cochlear anatomy and its influence on hearing performance in cochlear implantees. Twenty-two patients (25 ears) with Med-El cochlear implants were included in this retrospective study. A negative correlation was observed between electrode-modiolus distance (EMD) at the cochlear base and monosyllabic word discrimination 6 months after implantation. We found no correlation between EMD and hearing outcome at 12 months. The insertion depth/cochlear perimeter ratio appeared to negatively influence the EMD at the base. Indeed, deep insertions in small cochleae appeared to yield smaller EMD and better hearing performance. This observation supports the idea of preplanning the surgery by adapting the electrode array to the length of the available scala tympani.

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Section: Discussionmentioning

confidence: 99%

Is Electrode-Modiolus Distance a Prognostic Factor for Hearing Performances after Cochlear Implant Surgery?

et al. 2013

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“…Promising PVC strategies that are capable of compensating for resolution loss in PET images using the information present in the corresponding anatomical images are becoming available. One such promising approach is the mutual multiresolution analysis method using 3D wavelet decomposition of both anatomical and functional images combined with local analysis (using empirical parameters) to adapt the model according to regional information [11]. Another approach uses a Bayesian model that introduces the lacking information in the form of a priori probability of the high frequency activity presence considering as hypothesis the fact that the image formation is a linear combination of the actual activity concentration and a Gaussian noise [12].…”

Section: Discussionmentioning

confidence: 99%

“…More recently, a novel class of PVC algorithms that do not require segmentation of anatomical images was introduced [10]. This includes very promising approaches such as the wavelet decomposition technique [11] and the Bayesian approach [12]. In both cases, the algorithm is able to find the high frequency information lacking in low resolution PET images at a voxel-level without increasing the noise.…”

Section: Introductionmentioning

confidence: 99%

Anatomically guided voxel-based partial volume effect correction in brain PET: Impact of MRI segmentation

Gutiérrez

Montandon

Assal

et al. 2012

Computerized Medical Imaging and Graphics

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a b s t r a c tPartial volume effect is still considered one of the main limitations in brain PET imaging given the limited spatial resolution of current generation PET scanners. The accuracy of anatomically guided partial volume effect correction (PVC) algorithms in brain PET is largely dependent on the performance of MRI segmentation algorithms partitioning the brain into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of four brain MRI segmentation algorithms bundled in the successive releases of Statistical Parametric Mapping (SPM) package (SPM99, SPM2, SPM5, SPM8) using clinical neurological examinations was performed. Subsequently, their impact on PVC in 18 F-FDG brain PET imaging was assessed. The principle of the different variants of the image segmentation algorithm is to spatially normalize the subject's MR images to a corresponding template. PET images were corrected for partial volume effect using GM volume segmented from coregistered MR images. The PVC approach aims to compensate for signal dilution in non-active tissues such as CSF, which becomes an important issue in the case of tissue atrophy to prevent a misinterpretation of decrease of metabolism owing to PVE. The study population consisted of 19 patients suffering from neurodegenerative dementia. Image segmentation performed using SPM5 was used as reference. The comparison showed that previous releases of SPM (SPM99 and SPM2) result in larger gray matter regions (∼20%) and smaller white matter regions (between −17% and −6%), thus introducing non-negligible bias in PVC PET activity estimates (between 30% and 90%). In contrary, the more recent release (SPM8) results in similar results (<1%). It was concluded that the choice of the segmentation algorithm for MRI-guided PVC in PET plays a crucial role for the accurate estimation of PET activity concentration. The segmentation algorithm embedded within the latest release of SPM satisfies the requirement of robust and accurate segmentation for MRI-guided PVC in brain PET imaging.Published by Elsevier Ltd.

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“…Voxel-based methods, by contrast, do produce images. Examples include partitionbased [10][11][12][13] or multiresolution [14][15][16] methods, though these techniques typically include simplifying assumptions. Iterative deconvolution 17 is another possibility, but can lead to enhanced noise levels (though promising enhancements involving regularization 18 or denoising 19 have been noted).…”

Section: Introductionmentioning

confidence: 99%

Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls

2013

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In this paper, the authors review the field of resolution modeling in positron emission tomography (PET) image reconstruction, also referred to as point-spread-function modeling. The review includes theoretical analysis of the resolution modeling framework as well as an overview of various approaches in the literature. It also discusses potential advantages gained via this approach, as discussed with reference to various metrics and tasks, including lesion detection observer studies. Furthermore, attention is paid to issues arising from this approach including the pervasive problem of edge artifacts, as well as explanation and potential remedies for this phenomenon. Furthermore, the authors emphasize limitations encountered in the context of quantitative PET imaging, wherein increased intervoxel correlations due to resolution modeling can lead to significant loss of precision (reproducibility) for small regions of interest, which can be a considerable pitfall depending on the task of interest.

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Evaluation of a 3D local multiresolution algorithm for the correction of partial volume effects in positron emission tomography

Cited by 41 publications

References 32 publications

Is Electrode-Modiolus Distance a Prognostic Factor for Hearing Performances after Cochlear Implant Surgery?

Is Electrode-Modiolus Distance a Prognostic Factor for Hearing Performances after Cochlear Implant Surgery?

Anatomically guided voxel-based partial volume effect correction in brain PET: Impact of MRI segmentation

Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls

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