Functional and structural synergy for resolution recovery and partial volume correction in brain PET

Shidahara, Miho; Tsoumpas, Charalampos; Hammers, Alexander; Boussion, Nicolas; Visvikis, Dimitris; Suhara, Tetsuya; Kanno, Iwao; Turkheimer, Federico

doi:10.1016/j.neuroimage.2008.09.012

Cited by 84 publications

(81 citation statements)

References 31 publications

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“…Structuralfunctional synergistic resolution recovery uses a complex wavelet-transform and structural information to regularize the solution and therefore eliminates the confounding effects due to structural variability. 15 We have shown that SFS-RR has less effect on the variability of imaging data than two other image-based PVC algorithms (Lucy-Richardson and Van-Cittert methods). 15,16 We are now seeking to further evaluate the SFS-RR methodology in a clinical context using as a reference the standard PVC methodology used in the field.…”

Section: Introductionmentioning

confidence: 78%

“…The resolution recovery process is performed by the accurate weighting of the functional versus structural information in the wavelet space. 15 Since the finite spatial resolution of PET blurs the distribution of signal in the image, the SFS-RR algorithm replaces the high-resolution component of PET image with those of the structural image, which has superior spatial resolution, to recover the resolution of PET image. The algorithm is based on the multiresolution property of the wavelet transform.…”

Section: Partial Volume Correction Algorithmsmentioning

confidence: 99%

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Partial Volume Correction using Structural–Functional Synergistic Resolution Recovery: Comparison with Geometric Transfer Matrix Method

Kim

Shidahara

Tsoumpas

et al. 2013

J Cereb Blood Flow Metab

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We validated the use of a novel image-based method for partial volume correction (PVC), structural-functional synergistic resolution recovery (SFS-RR) for the accurate quantification of dopamine synthesis capacity measured using [ 18 F]DOPA positron emission tomography. The bias and reliability of SFS-RR were compared with the geometric transfer matrix (GTM) method. Both methodologies were applied to the parametric maps of [ 18 F]DOPA utilization rates (k i cer ). Validation was first performed by measuring repeatability on test-retest scans. The precision of the methodologies instead was quantified using simulated [ 18 F]DOPA images. The sensitivity to the misspecification of the full-width-half-maximum (FWHM) of the scanner point-spread-function on both approaches was also assessed. In the in-vivo data, the k i cer was significantly increased by application of both PVC procedures while the reliability remained high (intraclass correlation coefficients 40.85). The variability was not significantly affected by either PVC approach (o10% variability in both cases). The corrected k i cer was significantly influenced by the FWHM applied in both the acquired and simulated data. This study shows that SFS-RR can effectively correct for partial volume effects to a comparable degree to GTM but with the added advantage that it enables voxelwise analyses, and that the FWHM used can affect the PVC result indicating the importance of accurately calibrating the FWHM used in the recovery model.

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Section: Introductionmentioning

confidence: 78%

Section: Partial Volume Correction Algorithmsmentioning

confidence: 99%

Partial Volume Correction using Structural–Functional Synergistic Resolution Recovery: Comparison with Geometric Transfer Matrix Method

Kim

Shidahara

Tsoumpas

et al. 2013

J Cereb Blood Flow Metab

Self Cite

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“…Similarly, PMOD (PMOD Technologies Ltd., Zurich, Switzerland) -a proprietary application -performs GTM as well as a classical Müller-Gärtner (MG), utilising the Hammers' atlas [13]. Shidahara et al released the Structural-Functional Synergistic Resolution Recovery (SFSRR) method as a MATLAB software tool [14]. Most recently, Chonde et al [15] proposed a MATLAB user interface -Masamune -to a data-processing pipeline which incorporates PVC for dynamic PET analysis, as part of a brain PET-MR workflow.…”

Section: Introductionmentioning

confidence: 99%

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

et al. 2016

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Positron emission tomography (PET) images are degraded by a phenomenon known as the partial volume effect (PVE). Approaches have been developed to reduce PVEs, typically through the utilisation of structural information provided by other imaging modalities such as MRI or CT.These methods, known as partial volume correction (PVC) techniques, reduce PVEs by compensating for the effects of the scanner resolution, thereby improving the quantitative accuracy. The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data.Eight core PVC techniques are available. These core methods can be combined to create a total of twenty-two different PVC techniques. Simulated brain PET data are used to demonstrate the utility of toolbox in idealised conditions, the effects of applying PVC with mismatched pointspread function (PSF) estimates and the potential of novel hybrid PVC methods to improve the quantification of lesions.All anatomy-based PVC techniques achieve complete recovery of the PET signal in cortical grey matter (GM) when performed in idealised conditions. Applying deconvolution-based approaches results in incomplete recovery due to premature termination of the iterative process. PVC techniques are sensitive to PSF mismatch, causing a bias of up to 16.7% in GM recovery when over-estimating the PSF by 3mm. The recovery of both GM and a simulated lesion was improved by combining two PVC techniques together.The PETPVC toolbox has been written in C++, supports Windows, Mac and Linux operating systems, is open-source and publicly available.3

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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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Functional and structural synergy for resolution recovery and partial volume correction in brain PET

Cited by 84 publications

References 31 publications

Partial Volume Correction using Structural–Functional Synergistic Resolution Recovery: Comparison with Geometric Transfer Matrix Method

Partial Volume Correction using Structural–Functional Synergistic Resolution Recovery: Comparison with Geometric Transfer Matrix Method

PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography

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

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