PET reconstruction with system matrix derived from point source measurements

Panin, Vladimir; Kehren, F.; Rothfuss, H.; Hu, Dongming; Michel, Christian; Casey, Michael

doi:10.1109/tns.2005.862979

Cited by 59 publications

(25 citation statements)

References 9 publications

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“…(Factoring out the projection operation P geom , and using an eight-dimensional SRF S(r, θ, z, φ; r 0 , θ 0 , z 0 , φ 0 ), that maps given projection coordinates (r 0 , θ 0 , z 0 , φ 0 ) to their blurred counterparts (r, θ , z, φ), is actually less general: this is because depth-information (distance of source-voxel to detector) is lost, which may be important in the presence of axial mashing (spanning). 71,72 ) In practice, the use of such functions is not feasible computationally and/or storage-wise, and as a result, reduced dimensionality has been sought by various projection-space approaches in the literature: these methods can be categorized into those based on (a) analytic models, (b) Monte Carlo (MC) simulations, and (c) measured datasets, which we elaborate next.…”

Section: Iiia2 Projection-space Modelingmentioning

confidence: 99%

“…In particular, Panin et al, in the context of 2D PET (Ref. 71) and 3D PET, 72 used a 3D positioning robot to perform very elaborate point source measurements, involving 1599 individual positions, for a whole-body PET/CT scanner. The SRF, as modeled along the radial r and axial z directions, was a function of the radial bin r 0 , axial bin z 0 , and axial angle φ 0 of the incident LOR (and additionally the depth d of the source voxel along the LOR in the case of axial blurring, due to the complicating presence of axial mashing).…”

Section: Iiia2 Projection-space Modelingmentioning

confidence: 99%

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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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Section: Iiia2 Projection-space Modelingmentioning

confidence: 99%

Section: Iiia2 Projection-space Modelingmentioning

confidence: 99%

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

2013

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“…Moreover, s j is corrected to take into account the nonuniform radial sampling due to the gantry's spherical geometry ͑see the Appendix͒. Lastly, the expected element m ij is the shifted 1D PSF value for the ith radial bin, 3. Diagram of the method used to retrieve the projection of a given voxel placed at ͑x j , y j ͒.…”

Section: Iic Osemᠪdr Reconstructionmentioning

confidence: 99%

“…Many researchers ͑such as Qi and Huesman, Panin et al, and Moehrs et al, and among others͒ showed that an accurate system matrix improves image quality in terms of spatial resolution, noise, and the contrast-noise trade-off. [2][3][4] In addition, iterative reconstruction of PET data remains a critical issue since it requires the resolution of an ill-posed problem in which low noise levels in the acquired data may produce high noise levels in the final image. There are various ways of dealing with this latter issue: ͑i͒ Stopping rules, ͑ii͒ true regularization ͑via priors or penalization͒, and ͑iii͒ noise-reduction methods ͑e.g., postfiltering͒.…”

Section: Introductionmentioning

confidence: 99%

A practical way to improve contrast‐to‐noise ratio and quantitation for statistical‐based iterative reconstruction in whole‐body PET imaging

Fin¹,

Bailly²,

Daouk³

et al. 2009

Medical Physics

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In whole-body positron emission tomography (PET) imaging, the detection of small uptake foci (i.e., around two or three times the tomograph's spatial resolution) is a critical issue. Indeed, spatial resolution is altered by postreconstruction smoothing operations used to reduce the noise introduced by (among other things) an inaccurate system matrix. The authors previously proposed a device-dedicated projector, easily applicable on a clinical gantry, based on point-source measurements, which introduces less noise than a geometrical model. In the present study, they took advantage of the lower noise levels by reducing the postfilter and then quantified the approach's impact on image quality. This study was performed on an IEC Body Phantom Set filled with 18F (sphere-to-background activity ratio: 4:1). The same 3 min acquisition was reconstructed with either (i) a clinical system based on a geometrical tomographic operator (OSEM_CL) or (ii) an OSEM algorithm using the suggested system matrix (OSEM_DR). In order to compare the resulting images, they set the 3D Gaussian postfilter (3DGPF) for OSEM_DR so as to obtain similar background signal-to-noise ratio (SNR) to that of OSEM_CL with a Gaussian postfilter full width at half maximum of 5 mm (as recommended for whole-body imaging on a Biograph6). They then assessed the contrast-to-noise ratio (CNR) and quantitation [contrast recovery (CR)] for the phantom's four smallest spheres (with internal diameters of 10, 13, 17, and 22 mm). Evaluation of 3DGPFs ranging from 2.2 to 2.6 mm showed that a value of 2.4 mm in OSEM_DR gave the closest background SNR to that of OSEM_CL with a 3DGPF of 5 mm. For all studied targets, the CNR was higher with OSEM_DR than with OSEM_CL. For the 10 and 13 mm spheres, OSEM_DR increased the size of the CNR peaks by 37% and 20%, relative to OSEM_CL. The OSEM_DR technique yielded higher CR values than OSEM_CL did. For the 10, 13, 17, and 22 mm spheres, the CR values at eight iterations were 0.5, 0.6, 1.1, and 1.0 for OSEM_DR and 0.3, 0.4, 0.9, and 0.8 for OSEM_CL. They evaluated a practical method for determining a device-dedicated system matrix based on point-source acquisitions. This tomographic operator is more realistic than geometrical system matrix and introduces less noise into PET images during statistical reconstruction; it thus reduces the extent of postfiltering operations required. Thus, spatial resolution is better maintained with OSEM_DR than with clinical reconstruction. They showed that this method improves the contrast-to-noise ratio and quantification of uptake foci (especially those that are at the system's limit of detection) and, in a clinical context, could allow better detection and earlier diagnosis.

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“…Many approaches have been proposed to overcome these effects. [8][9][10][11][12][13][14][15][16][17] Some of these approaches relied on improving the intrinsic resolution of PET images by incorporating a model of the scanner point spread function during PET image reconstruction, [8][9][10] while other approaches specifically focused on reducing the effects of the extrinsic factors such as gating the PET data acquisition to minimize motion. [11][12][13][14] Other techniques for improving PET image resolution have also explored the use of continuous bed motion to reduce the aliasing effects in the axial direction ͑Z-axis͒.…”

Section: Introductionmentioning

confidence: 99%

Comparison between two super‐resolution implementations in PET imaging

et al. 2009

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Super-resolution (SR) techniques are used in PET imaging to generate a high-resolution image by combining multiple low-resolution images that have been acquired from different points of view (POV). In this article, the authors propose a novel implementation of the SR technique whereby the required multiple low-resolution images are generated by shifting the reconstruction pixel grid during the image reconstruction process rather than being acquired from different POVs. The objective of this article is to compare the performances of the two SR implementations using theoretical and experimental studies. A mathematical framework is first provided to support the hypothesis that the two SR implementations have similar performance in current PET/CT scanners that use block detectors. Based on this framework, a simulation study, a point source study, and a NEMA/IEC phantom study were conducted to compare the performance of these two SR implementations with respect to contrast, resolution, noise, and SNR. For reference purposes, a comparison with a native reconstruction (NR) image using a high-resolution pixel grid was also performed. The mathematical framework showed that the two SR implementations are expected to achieve similar contrast and resolution but different noise contents. These results were confirmed by the simulation and experimental studies. The simulation study showed that the two SR implementations have an average contrast difference of 2.3%, while the point source study showed that their average differences in contrast and resolution were 0.5% and 1.2%, respectively. Comparisons between the SR and NR images for the point source study showed that the NR image exhibited averages of 30% and 8% lower contrast and resolution, respectively. The NEMA/IEC phantom study showed that the three images (two SR and NR) exhibited different noise structures. The SNR of the new SR implementation was, on average, 21.5% lower than the original implementation largely due to an increase in background noise, while the NR image had averages of 18.5% and 8% lower SNR and contrast, respectively, versus the two SR images. The new SR implementation can potentially replace the original SR approach in current PET scanners that use block detectors while maintaining similar contrast and resolution, but at a relatively lower SNR. A major advantage of the new SR implementation is its shorter overall scan duration which results in an increase in scanner throughput and a reduction in patient motion.

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PET reconstruction with system matrix derived from point source measurements

Cited by 59 publications

References 9 publications

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

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

A practical way to improve contrast‐to‐noise ratio and quantitation for statistical‐based iterative reconstruction in whole‐body PET imaging

Comparison between two super‐resolution implementations in PET imaging

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