Brain PET Partial-Volume Compensation Using Blurred Anatomical Labels

Bataille, Frédéric; Comtat, Claude; Jan, S.; Sureau, F.; Trébossen, R.

doi:10.1109/tns.2007.906165

Cited by 13 publications

(9 citation statements)

References 22 publications

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“…Even if other stopping criteria are considered (e.g., as described by Selivanov et al (7)), it seems that RM should be included in maximum a posteriori algorithms. This approach has already been used and has proved beneficial (31). Early stopping of the reconstruction algorithm implies that the signal has not converged at the voxel level.…”

Section: Discussionmentioning

confidence: 99%

Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tomograph

Sureau¹,

Reader²,

Comtat³

et al. 2008

J Nucl Med

228

196

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Brain PET in small structures is challenged by low resolution inducing bias in the activity measurements. Improved spatial resolution may be obtained by using dedicated tomographs and more comprehensive modeling of the acquisition system during reconstruction. In this study, we assess the impact of resolution modeling (RM) during reconstruction on image quality and on the estimates of biologic parameters in a clinical study performed on a high-resolution research tomograph. Methods: An accelerated list-mode ordinary Poisson ordered-subset expectation maximization (OP-OSEM) algorithm, including sinogram-based corrections and an experimental stationary model of resolution, has been designed. Experimental phantom studies are used to assess contrast and noise characteristics of the reconstructed images. The binding potential of a selective tracer of the dopamine transporter is also assessed in anatomic volumes of interest in a 5-patient study. Results: In the phantom experiment, a slower convergence and a higher contrast recovery are observed for RM-OP-OSEM than for OP-OSEM for the same level of statistical noise. RM-OP-OSEM yields contrast recovery levels that could not be reached without RM as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with RM than without at matched resolution. In a uniform activity region, RM induces higher positive and lower negative correlations with neighboring voxels, leading to lower spatial variance. Clinical images reconstructed with RM demonstrate better delineation of cortical and subcortical structures in both time-averaged and parametric images. The binding potential in the striatum is also increased, a result similar to the one observed in the phantom study. Conclusion: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects.

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Section: Discussionmentioning

confidence: 99%

Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tomograph

Sureau¹,

Reader²,

Comtat³

et al. 2008

J Nucl Med

228

196

View full text Add to dashboard Cite

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“…Comtat et al (2002) presented an algorithm for reconstructing wholebody PET/CT data, in which the anatomical labels were blurred in order to take into account the uncertainty associated with the anatomical information. This method was later also applied to brain studies by Bataille et al (2007). Baete et al (2004) presented a MAP reconstruction algorithm for brain PET studies using a tissue composition model, in which WM and CSF were treated as uniform for the purpose of estimating the GM values.…”

Section: Image Enhancement Techniquesmentioning

confidence: 99%

A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology

et al. 2012

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Accurate quantification in PET and SPECT requires correction for a number of physical factors, such as photon attenuation, Compton scattering and random coincidences (in PET). Another factor affecting quantification is the limited spatial resolution. While considerable effort has gone into development of routine correction techniques for the former factors, less attention has been paid to the latter. Spatial resolution-related effects, referred to as 'partial volume effects' (PVEs), depend not only on the characteristics of the imaging system but also on the object and activity distribution. Spatial and/or temporal variations in PVE can often be confounding factors. Partial volume correction (PVC) could in theory be achieved by some kind of inverse filtering technique, reversing the effect of the system PSF. However, these methods are limited, and usually lead to noise-amplification or image artefacts. Some form of regularization is therefore needed, and this can be achieved using information from co-registered anatomical images, such as CT or MRI. The purpose of this paper is to enhance understanding of PVEs and to review possible approaches for PVC. We also present a review of clinical applications of PVC within the fields of neurology, cardiology and oncology, including specific examples.

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“…This is achieved by convolving the partition (brain mask) with the point spread function (PSF) of the PET imaging system. This approach was extended to two (GM and WM) [20] and three compartments assuming that the CSF activity is not only the result of spillover from contiguous regions [18].…”

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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Brain PET Partial-Volume Compensation Using Blurred Anatomical Labels

Cited by 13 publications

References 22 publications

Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tomograph

Impact of Image-Space Resolution Modeling for Studies with the High-Resolution Research Tomograph

A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology

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

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