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

AbstractImprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration. We applied PoR to the [11C]PiB PET data of 92 participants obtained from the Alzheimer’s Disease Neuroimaging Initiative database and compared the registration results, PV-corrected standardized uptake value (SUV) and its ratio to the cerebellum (SUVR), and intra-region coefficient of variation (CoV) between PoR and conventional registration. Significant differences in registration of as much as 2.74 mm and 3.02° were observed between the two methods (effect size < − 0.8 or > 0.8), which resulted in considerable SUVR differences throughout the brain, reaching a maximal difference of 62.3% in the sensory motor cortex. Intra-region CoV was significantly reduced using the PoR throughout the brain. These results suggest that PoR reduces error as a result of imprecise registration in PVC and is a useful method for accurately quantifying the amyloid burden in PET.

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Standardization of dual time point [18F] 2-Deoxy-2-fluoro-D-glucose-positron emission tomography performed with different positron emission tomography scanners using partial volume correction

Research and Reports in Nuclear Medicine ◽

10.2147/rrnm.s73413 ◽

2015 ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Shohei Mikasa ◽

Go Akamatsu ◽

Takafumi Taniguchi ◽

Daisuke Kidera ◽

Ken Kihara ◽

...

Keyword(s):

Positron Emission Tomography ◽

Partial Volume Correction ◽

Emission Tomography ◽

Partial Volume ◽

Volume Correction ◽

Tomography Scanners ◽

Positron Emission ◽

Dual Time Point ◽

Time Point

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Partial volume correction of cardiac mouse positron emission tomography images

10.22215/etd/2012-09506 ◽

2012 ◽

Author(s):

Tyler Dumouchel

Keyword(s):

Positron Emission Tomography ◽

Partial Volume Correction ◽

Emission Tomography ◽

Partial Volume ◽

Volume Correction ◽

Positron Emission

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Region-Based Partial Volume Correction Techniques for PET Imaging: Sinogram Implementation and Robustness

International Journal of Molecular Imaging ◽

10.1155/2013/435959 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Mike Sattarivand ◽

Jennifer Armstrong ◽

Gregory M. Szilagyi ◽

Maggie Kusano ◽

Ian Poon ◽

...

Keyword(s):

Partial Volume Correction ◽

Emission Tomography ◽

Partial Volume ◽

Volume Correction ◽

Noise Characteristics ◽

Point Spread ◽

Positron Emission ◽

Spread Function ◽

3D Pet ◽

Better Than

Background/Purpose. Limited spatial resolution of positron emission tomography (PET) requires partial volume correction (PVC). Region-based PVC methods are based on geometric transfer matrix implemented either in image-space (GTM) or sinogram-space (GTMo), both with similar performance. Although GTMo is slower, it more closely simulates the 3D PET image acquisition, accounts for local variations of point spread function, and can be implemented for iterative reconstructions. A recent image-based symmetric GTM (sGTM) has shown improvement in noise characteristics and robustness to misregistration over GTM. This study implements the sGTM method in sinogram space (sGTMo), validates it, and evaluates its performance. Methods. Two 3D sphere and brain digital phantoms and a physical sphere phantom were used. All four region-based PVC methods (GTM, GTMo, sGTM, and sGTMo) were implemented and their performance was evaluated. Results. All four PVC methods had similar accuracies. Both noise propagation and robustness of the sGTMo method were similar to those of sGTM method while they were better than those of GTMo method especially for smaller objects. Conclusion. The sGTMo was implemented and validated. The performance of the sGTMo in terms of noise characteristics and robustness to misregistration is similar to that of the sGTM method and improved compared to the GTMo method.

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Positron Emission Tomography Partial Volume Correction: Estimation and Algorithms

Journal of Cerebral Blood Flow & Metabolism ◽

10.1097/00004647-200208000-00014 ◽

2002 ◽

Vol 22 (8) ◽

pp. 1019-1034 ◽

Cited By ~ 123

Author(s):

John A. D. Aston ◽

Vincent J. Cunningham ◽

Marie-Claude Asselin ◽

Alexander Hammers ◽

Alan C. Evans ◽

...

Keyword(s):

Positron Emission Tomography ◽

Point Spread Function ◽

Spillover Effects ◽

Partial Volume Correction ◽

Emission Tomography ◽

Partial Volume ◽

Point Spread ◽

Positron Emission ◽

Spread Function ◽

Tissue Fraction

Partial volume effects in positron emission tomography (PET) lead to quantitative under- and over-estimations of the regional concentrations of radioactivity in reconstructed images and corresponding errors in derived functional or parametric images. The limited resolution of PET leads to “tissue-fraction” effects, reflecting underlying tissue heterogeneity, and “spillover” effects between regions. Addressing the former problem in general requires supplementary data, for example, coregistered high-resolution magnetic resonance images, whereas the latter effect can be corrected for with PET data alone if the point-spread function of the tomograph has been characterized. Analysis of otherwise homogeneous region-of-interest data ideally requires a combination of tissue classification and correction for the point-spread function. The formulation of appropriate algorithms for partial volume correction (PVC) is dependent on both the distribution of the signal and the distribution of the underlying noise. A mathematical framework has therefore been developed to accommodate both of these factors and to facilitate the development of new PVC algorithms based on the description of the problem. Several methodologies and algorithms have been proposed and implemented in the literature in order to address these problems. These methods do not, however, explicitly consider the noise model while differing in their underlying assumptions. The general theory for estimation of regional concentrations, associated error estimation, and inhomogeneity tests are presented in a weighted least squares framework. The analysis has been validated using both simulated and real PET data sets. The relations between the current algorithms and those published previously are formulated and compared. The incorporation of tensors into the formulation of the problem has led to the construction of computationally rapid algorithms taking into account both tissue-fraction and spillover effects. The suitability of their application to dynamic and static images is discussed.

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