2000
DOI: 10.1109/23.856555
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Implementation of ML based positioning algorithms for scintillation cameras

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Cited by 35 publications
(7 citation statements)
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“…The spatial resolution using simple COG was about 4.7 mm above the center of PMT. Since, the maximum likelihood (ML) based methods [20][21][22] and also some iterative methods which have been discussed in the [23,24], require more calibration and implementation effort, we used digital CSE method which sums PMT signals in row/column arrangement, then applies a threshold and a correction factor on the row/column signals before COG. In figure 2 (right), the line spacing is uniform regarding the PMT position, indicating image linearity along X and Y directions although, there is still shrinkage in the edges of the crystal.…”
Section: Discussionmentioning
confidence: 99%
“…The spatial resolution using simple COG was about 4.7 mm above the center of PMT. Since, the maximum likelihood (ML) based methods [20][21][22] and also some iterative methods which have been discussed in the [23,24], require more calibration and implementation effort, we used digital CSE method which sums PMT signals in row/column arrangement, then applies a threshold and a correction factor on the row/column signals before COG. In figure 2 (right), the line spacing is uniform regarding the PMT position, indicating image linearity along X and Y directions although, there is still shrinkage in the edges of the crystal.…”
Section: Discussionmentioning
confidence: 99%
“…A 2D position of interaction is usually obtained by a simple centroid calculation based on the signal from the different PMTs [1]. However, a better spatial resolution and linearity can be obtained using a maximum-likelihood (ML) position estimation [31,32]. Normally, 2D positioning is considered sufficient, but DOI information can be useful, especially if non-parallelhole collimators are used [33,34].…”
Section: Detection Position Estimationmentioning
confidence: 99%
“…Splines made of higher degree polynomials are smoother but more costly to evaluate. Following the approach used in [13,15,22], we have decided to use cubic splines for LRF parameterization as it ensures a smooth first derivative (important for convergence of several minimization algorithms) while still not being too computationally expensive. Besides piecewise polynomial functions, splines can also be presented as a weighted sum of basis functions called B-splines.…”
Section: Splinesmentioning
confidence: 99%