Geometrical and Monte Carlo projectors in 3D PET reconstruction

Aguiar, Pablo; Rafecas, M.; Ortuño, Juan E.; Kontaxakis, George; Santos, A.; Pavı́a, Javier; Ros, Domènec

doi:10.1118/1.3501884

Cited by 34 publications

(27 citation statements)

References 39 publications

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“…In practical implementations, it has been observed that the non-negativity constraint used in these algorithms can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background activity (Yang et al 2004). It has also been noted that the spatial resolution measured in air is highly dependent on the pixel size and has limitations when being used to evaluate system resolution (Aguiar et al 2010, Alessio et al 2010). While it has been recommended that a non-zero background be added to the point source data before reconstruction in order to minimize these effects, there has been no detailed study about the effect of the point source contrast level on the measured spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

On the assessment of spatial resolution of PET systems with iterative image reconstruction

Gong

Cherry

2016

Phys. Med. Biol.

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Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.

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Section: Introductionmentioning

confidence: 99%

On the assessment of spatial resolution of PET systems with iterative image reconstruction

Gong

Cherry

2016

Phys. Med. Biol.

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“…Alternatively, the SRM can be calculated using analytical methods. The speed of these methods outperforms others, at the expense of limited precision [7, 8]. Finally, the SRM can be calculated using Monte Carlo methods [9–11].…”

Section: Introductionmentioning

confidence: 99%

High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid

Cabello

Gillam

Rafecas

2012

International Journal of Biomedical Imaging

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Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM) to store, which additionally leads to long reconstruction times. These two problems are partly overcome by exploiting spherical symmetries, although computation time is still slower compared to non-overlapping basis functions. In this work, we have implemented the reconstruction algorithm using Graphical Processing Unit (GPU) technology for speed and a precomputed Monte-Carlo-calculated SRM for accuracy. The reconstruction time achieved using spherical basis functions on a GPU was 4.3 times faster than the Central Processing Unit (CPU) and 2.5 times faster than a CPU-multi-core parallel implementation using eight cores. Overwriting hazards are minimized by combining a random line of response ordering and constrained atomic writing. Small differences in image quality were observed between implementations.

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“…ray-tracing, has efficient implementations such as the Siddon algorithm (Siddon (1985)) that has been applied to PET in Herman & Meyer (1993) and Zhao & Reader (2003) or the orthogonal distance-based ray-tracer of Aguiar et al (2010). The ray-tracing technique allows for on the fly calculation of the matrix elements thus avoiding SM storage problems.…”

Section: System Matrixmentioning

confidence: 99%

Free Software for PET Imaging

Prieta¹

2012

Positron Emission Tomography - Current Clinical and Research Aspects

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Geometrical and Monte Carlo projectors in 3D PET reconstruction

Cited by 34 publications

References 39 publications

On the assessment of spatial resolution of PET systems with iterative image reconstruction

On the assessment of spatial resolution of PET systems with iterative image reconstruction

High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid

Free Software for PET Imaging

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