Using the bootstrap method to evaluate image noise for investigation of axial collimation in hybrid PET

Groiselle, C.J.; Glick, SJ

doi:10.1109/nssmic.2002.1239439

Cited by 2 publications

(5 citation statements)

References 3 publications

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“…Comparatively, when estimating the variance the bootstrap performs much better achieving good accuracy which has also been reported when estimating noise properties of image reconstruction (Buvat 2002, Dahlbom 2002, Groiselle and Glick 2005, Lartizien et al 2010.…”

Section: Resultssupporting

confidence: 54%

“…Based on this list-mode acquisition, which takes the role of the underlying distribution, multiple reference realisations (corresponding to real world multiple measurements) can be generated by sampling with replacements and with the statistical level varied by changing the size of the sampled realisations. This is similar to bootstrap resampling with the difference that the size of newly generated samples changes according to the desired statistical level as indicated in Haynor and Woods (1989) and Groiselle and Glick (2005).…”

Section: Theory and Methodsmentioning

confidence: 99%

“…Any bootstrap sinogram is then generated by resampling with replacement every sinogram row from the set of corresponding empirical sub-sinogram rows. Examples of the latter resampling of raw and unhistogrammed PET data have also been reported (Haynor and Woods 1989, Dahlbom 2002, Groiselle and Glick 2005, Verhaeghe et al 2010. Such bootstrap resampling was evaluated when applied to estimating PET image noise yielding similar performance to that of independent realisations of phantom scans (Dahlbom 2002) or Monte Carlo simulations (Groiselle and Glick 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Examples of the latter resampling of raw and unhistogrammed PET data have also been reported (Haynor and Woods 1989, Dahlbom 2002, Groiselle and Glick 2005, Verhaeghe et al 2010. Such bootstrap resampling was evaluated when applied to estimating PET image noise yielding similar performance to that of independent realisations of phantom scans (Dahlbom 2002) or Monte Carlo simulations (Groiselle and Glick 2005). Whereas Verhaeghe et al (2010) used the bootstrap resampling of the list mode data in the procedure developed for task-based selection of image reconstruction methodology where the mean square error estimated with the bootstrap was minimised.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of bootstrap resampling performance for PET data

2014

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Bootstrap resampling has been successfully used for estimation of statistical uncertainty of parameters such as tissue metabolism, blood flow or displacement fields for image registration. The performance of bootstrap resampling as applied to PET list-mode data of the human brain and dedicated phantoms is assessed in a novel and systematic way such that: (1) the assessment is carried out in two resampling stages: the 'real world' stage where multiple reference datasets of varying statistical level are generated and the 'bootstrap world' stage where corresponding bootstrap replicates are generated from the reference datasets. (2) All resampled datasets were reconstructed yielding images from which multiple voxel and regions of interest (ROI) values were extracted to form corresponding distributions between the two stages. (3) The difference between the distributions from both stages was quantified using the Jensen-Shannon divergence and the first four moments.It was found that the bootstrap distributions are consistently different to the real world distributions across the statistical levels. The difference was explained by a shift in the mean (up to 33% for voxels and 14% for ROIs) being proportional to the inverse square root of the statistical level (number of counts). Other moments were well replicated by the bootstrap although for very low statistical levels the

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Section: Resultssupporting

confidence: 54%

Section: Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of bootstrap resampling performance for PET data

2014

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show abstract

“…A sub‐sinogram is a sinogram acquired with an entire data acquisition protocol, but with a fraction of the total scanning time (Buvat,2002), and a total‐sinogram is the summation of all of the sub‐sinograms. The concept of sub‐sinograms has already been introduced as part of several bootstrap approaches, but their main interests were focused on estimating the noise properties of the reconstructed images (Buvat and Riddell,2001; Buvat,2002; Dahlbom,2002; Groiselle and Glick,2002). This article shows via our proposed method that the noise can be reduced when the final image is obtained by the summation of reconstructions using sub‐sinograms.…”

Section: Introductionmentioning

confidence: 99%

SPECT reconstruction with sub‐sinogram acquisitions

Hwang

Lee

Zeng

2011

Int J Imaging Syst Tech

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Described herein are the advantages of using sub-sinograms for single photon emission computed tomography image reconstruction. A sub-sinogram is a sinogram acquired with an entire data acquisition protocol, but in a fraction of the total acquisition time. A total-sinogram is the summation of all sub-sinograms. Images can be reconstructed from the total-sinogram or from sub-sinograms and then be summed to produce the final image. For a linear reconstruction method such as the filtered backprojection algorithm, there is no advantage of using sub-sinograms. However, for nonlinear methods such as the maximum likelihood (ML) expectation maximization algorithm, the use of sub-sinograms can produce better results. The ML estimator is a random variable, and one ML reconstruction is one realization of the random variable. The ML solution is better obtained via the mean value of the random variable of the ML estimator. Sub-sinograms can provide many realizations of the ML estimator. We show that the use of sub-sinograms can produce better estimations for the ML solution than can the total-sinogram and can also reduce the statistical noise within iteratively reconstructed images.

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Using the bootstrap method to evaluate image noise for investigation of axial collimation in hybrid PET

Cited by 2 publications

References 3 publications

Assessment of bootstrap resampling performance for PET data

Assessment of bootstrap resampling performance for PET data

SPECT reconstruction with sub‐sinogram acquisitions

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