Spatially Variant Resolution Modelling for Iterative List-Mode PET Reconstruction

Bickell, Matthew; Zhou, Lin

doi:10.1109/tmi.2016.2526631

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…We used a spatially invariant kernel for resolution modelling which is optimal at the centre (Angelis et al 2015). A spatially variant kernel (Bickell et al 2016 provide further improvement but we have not investigated this. Regarding noise, the inter-voxel variance of a uniform ROI certainly increases as a function of the duration of the tracking gap or loss of counts, however we have also observed that bias remains <0.5% even when the decrease in counts is >90% (unpublished data).…”

Section: Discussionmentioning

confidence: 99%

Open-field PET: Simultaneous brain functional imaging and behavioural response measurements in freely moving small animals

et al. 2019

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A comprehensive understanding of how the brain responds to a changing environment requires techniques capable of recording functional outputs at the whole-brain level in response to external stimuli. Positron emission tomography (PET) is an exquisitely sensitive technique for imaging brain function but the need for anaesthesia to avoid motion artefacts precludes concurrent behavioural response studies. Here, we report a technique that combines motion-compensated PET with a robotically-controlled animal enclosure to enable simultaneous brain imaging and behavioural recordings in unrestrained small animals. The technique was used to measure in vivo displacement of [ 11 C]raclopride from dopamine D2 receptors (D2R) concurrently with changes in the behaviour of awake, freely moving rats following administration of unlabelled raclopride or amphetamine. The timing and magnitude of [ 11 C]raclopride displacement from D2R were reliably estimated and, in the case of amphetamine, these changes coincided with a marked increase in stereotyped behaviours and hyper-locomotion. The technique, therefore, allows simultaneous measurement of changes in brain function and behavioural responses to external stimuli in conscious unrestrained animals, giving rise to important applications in behavioural neuroscience.

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Section: Discussionmentioning

confidence: 99%

Open-field PET: Simultaneous brain functional imaging and behavioural response measurements in freely moving small animals

et al. 2019

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“…The PSF has to be sampled using the reconstructed image voxel size. To do so, instead of transforming the PSF using (6), as suggested in the derivation of the resolution model in (9), it is more convenient to transform the image space to the scanner space using the inverse transformation of (6). Then, the PSF can be sampled according to the image space sampling size at the corresponding scanner position.…”

Section: ) Calculation Of the Motion Dependent And Spatially Variantmentioning

confidence: 99%

“…More realistic representations consider models which can capture the spatially variant and asymmetric shape of the true PSF [7], [8] usually observed in PET scanners. In addition, the resolution model can be implemented in the projection space [9] or in the image space [3]. However, there is minimal difference in image quality between implementation of both methods [4].…”

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confidence: 99%

Motion Dependent and Spatially Variant Resolution Modeling for PET Rigid Motion Correction

Miranda

Staelens

Stroobants

et al. 2020

IEEE Trans. Med. Imaging

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Recent advances in positron emission tomography (PET) have allowed to perform brain scans of freely moving animals by using rigid motion correction. One of the current challenges in these scans is that, due to the PET scanner spatially variant point spread function (SVPSF), motion corrected images have a motion dependent blurring since animals can move throughout the entire field of view (FOV). We developed a method to calculate the image-based resolution kernels of the motion dependent and spatially variant PSF (MD-SVPSF) to correct the loss of spatial resolution in motion corrected reconstructions. The resolution kernels are calculated for each voxel by sampling and averaging the SVPSF at all positions in the scanner FOV where the moving object was measured. In resolution phantom scans, the use of the MD-SVPSF resolution model improved the spatial resolution in motion corrected reconstructions and corrected the image deformation caused by the parallax effect consistently for all motion patterns, outperforming the use of a motion independent SVPSF or Gaussian kernels. Compared to motion correction in which the SVPSF is applied independently for every pose, our method performed similarly, but with more than two orders of magnitude faster computation time. Importantly, in scans of freely moving mice, brain regional quantification in motion-free and motion corrected images was better correlated when using the MD-SVPSF in comparison with motion independent SVPSF and a Gaussian kernel. The method developed here allows to obtain consistent spatial resolution and quantification in motion corrected images, independently of the motion pattern of the subject.

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“…Since for listmode reconstruction no sinograms are calculated, an alternative to incorporate the resolution model in the projection space consists on repositioning the LORs according to the probable LOR position calculated from the resolution model. However, this approach has been shown to not perform well using ML-EM listmode reconstruction (Bickell et al 2016). On the other hand, resolution modelling in the image space can be easily adapted for ML-EM list-mode reconstruction, showing good image quality (Cloquet et al 2010), and in practice is easier to adapt than the projection space approach.…”

Section: Introductionmentioning

confidence: 99%

Validation of a spatially variant resolution model for small animal brain PET studies

Miranda

Bertoglio

Glorie

et al. 2020

Biomed. Phys. Eng. Express

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In small animal positron emission tomography (PET) studies, given the spatial resolution of preclinical PET scanners, quantification in small regions can be challenging. Moreover, in scans where animals are placed away from the center of the field of view (CFOV), e.g. in simultaneous scans of multiple animals, quantification accuracy can be compromised due to the loss of spatial resolution towards the edge of the FOV. Here, we implemented a spatially variant resolution model to improve quantification in small regions and to allow simultaneous scanning of multiple animals without compromising quantification accuracy. The scanner’s point spread function (PSF) was characterized across the FOV and modelled using a spatially variant and asymmetric Gaussian function. The spatially variant PSF (SVPSF) was then used for resolution modelling in the iterative reconstruction. To assess the image quality, a line source phantom in a cold and warm background, as well as mouse brain [18F]FDG scans, were performed. The SVPSF and the vendor’s maximum a posteriori (MAP3D) reconstructions produced uniform spatial resolution across the scanner FOV, but MAP3D resulted in lower spatial resolution. The line sources recovery coefficient using SVPSF was similar at the CFOV and at the edge of the FOV. In contrast, the other tested reconstructions produced lower recovery coefficient at the edge of the FOV. In mouse brain reconstructions, less spill-over from hot regions to cold regions, as well as more symmetric regional brain uptake was observed using SVPSF. The contrast in brain images was the highest using SVPSF, in mice scanned at the CFOV and off-center. Incorporation of a spatially variant resolution model for small animal brain PET improves quantification accuracy in small regions and produces consistent image spatial resolution across the FOV. Therefore, simultaneous scanning of multiple animals can benefit by using spatially variant resolution modelling.

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Spatially Variant Resolution Modelling for Iterative List-Mode PET Reconstruction

Cited by 10 publications

References 39 publications

Open-field PET: Simultaneous brain functional imaging and behavioural response measurements in freely moving small animals

Open-field PET: Simultaneous brain functional imaging and behavioural response measurements in freely moving small animals

Motion Dependent and Spatially Variant Resolution Modeling for PET Rigid Motion Correction

Validation of a spatially variant resolution model for small animal brain PET studies

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