Cross-validation stopping rule for ML-EM reconstruction of dynamic PET series: effect on image quality and quantitative accuracy

Selivanov, V.; Lapointe, D.; Bentourkia, M’hamed; Lecomte, Roger

doi:10.1109/23.940180

Cited by 43 publications

(3 citation statements)

References 15 publications

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“…We construct the 3025 × 3025 weight matrix by computing, for each mesh point source, the PSF at the different scattering angles for each site on the detector. The reconstruction is carried out by inverting this weight matrix using the Singular Value Decomposition (SVD) method, which is less time consuming, compared to other reconstruction methods [18]. Figure 10 and Figure 11 show the reconstruction results ob-tained using both collimators.…”

Section: Numerical Reconstruction Resultsmentioning

confidence: 99%

Towards a new concept for high sensitivity Compton scatter emission imaging

Nguyen¹,

Driol²,

Truong³

et al. 2008

J. Eur. Opt. Soc.-Rapid Publ.

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International audienceA new efficient scheme for imaging gamma-emitting objects is advocated in this work. It is elaborated on the recent idea of collecting data, using a detector equipped with a parallel-hole collimator, from Compton scattered photons to reconstruct an object in three-dimensions. This paper examines a working mode without collimation, which should increase its sensitivity and field of view. To simplify the otherwise complex mathematical formulation, we choose to discuss the image formation process in two-dimensions, which can be implemented with a slit collimator. Comparison with the standard collimated case, via the analysis of the shapes of the respective point spread functions (PSF), shows marked improvements and numerical simulation results, obtained using a brain phantom, support the viability and attractiveness of this new imaging modality

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Section: Numerical Reconstruction Resultsmentioning

confidence: 99%

Towards a new concept for high sensitivity Compton scatter emission imaging

Nguyen¹,

Driol²,

Truong³

et al. 2008

J. Eur. Opt. Soc.-Rapid Publ.

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“…The well studied ML‐EM algorithms have been found to produce good results in reconstruction from noisy data as they are based on Poisson statistics (Shepp and Vardi,1982; Unser and Eden,1988; Liow and Strother,1991; Barrett et al,1994; Qi,2003; Hwang and Zeng,2006a). Nevertheless, the ML‐EM algorithm still suffers from noise, and many attempts have been made to further reduce this statistical noise (Hebert and Leahy,1989; Green,1990; Liow and Strother,1991; Johnson,1994; Panin et al,1999; Selivanov et al,2001; Nuyts,2002; Nuyts and Fessler,2003; Hwang and Zeng,2005). This article shows that the use of sub‐sinograms can contribute to the reduction of noise within a reconstructed image.…”

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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“…Σμ ηνζηήνζμ π 2 /J πανμοζζάγεζ μιμίςξ βκδζίςξ θείκμοζα ζοιπενζθμνά ηαεχξ αολάκεηαζ μ ανζειυξ ηςκ επακαθήρεςκ ηαζ θαιαάκεζ ηδκ εθάπζζηδ ηζιή ημο ζημ ζδιείμ ιεβζζημπμίδζδξ ηδξ l(x). Έκα άθθμ ηνζηήνζμ ηενιαηζζιμφ είκαζ δ δζαδζηαζία cross-validation [79]. Ζ ζοβηεηνζιέκδ ηεπκζηή πνδζζιμπμζεί δομ ζεη δεδμιέκςκ, ηα μπμία δδιζμονβήεδηακ ιε θέπηοκζδ (thinning) απυ ημ ζεη ηςκ ιεηνμφιεκςκ δεδμιέκςκ.…”

Section: μέζνδνη ηεξκαηηζκνύ ηνπ Em-ml πξηλ ηελ ζύγθιηζεunclassified

Ανάπτυξη τεχνικών ανακατασκευής ιατρικών δεδομένων βασισμένη σε ένα σύστημα small - animal PET μέσω βελτιστοποίησης και σύγκρισης μεθόδων επεξεργασίας και ανάλυσης ιατρικής πληροφορίας

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Cross-validation stopping rule for ML-EM reconstruction of dynamic PET series: effect on image quality and quantitative accuracy

Cited by 43 publications

References 15 publications

Towards a new concept for high sensitivity Compton scatter emission imaging

Towards a new concept for high sensitivity Compton scatter emission imaging

SPECT reconstruction with sub‐sinogram acquisitions

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