List-mode maximum-likelihood reconstruction for the ClearPEM system

Cao, Liji; Bugalho, R.; Matela, Nuno; Martins, M. V.; Almeida, Pedro; Peter, Jessica; Varela, J.

doi:10.1109/nssmic.2011.6153798

Cited by 11 publications

(9 citation statements)

References 8 publications

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“…The images acquired as described in IIA were reconstructed with a list mode Maximum-Likelihood ExpectationMaximization (MLEM) implementation [2], using different voxel sizes and 7 iterations (Fig. 3 shows an example with voxels of 1x1x1 mm 3 ).…”

Section: Image Reconstruction and Calibrationmentioning

confidence: 99%

Calibration and dead time correction for a PET scanner with variable geometry

Almeida

Ferreira

Ortigao

et al. 2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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Abstract-We aim at developing practical ways to measure the parameters necessary for data corrections and calibration of a PET scanner with variable geometry (ClearPEM), in order to achieve accurate quantitative images for a large range of imaging tasks using few phantom measurements. We used a decaying cylindrical phantom to determine calibration and dead time factors for a set of acquisition geometries that was obtained by varying the distance between detectors. New factors for intermediate distances can then be obtained by interpolation. Normalization correction factors were measured with a planar source of activity using the same distances. The results allowed to assess the dead time correction and calibration factors, indicating that with two simple phantom acquisitions it is possible to calibrate the scanner and obtain the data needed to apply data corrections in order to obtain quantitative images for a large range of imaging applications.

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Section: Image Reconstruction and Calibrationmentioning

confidence: 99%

Calibration and dead time correction for a PET scanner with variable geometry

Almeida

Ferreira

Ortigao

et al. 2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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“…ε k is the total efficiency of the kth voxel which is pre-calculated by results of a normalization acquisition in order to largely accelerate the reconstruction process [4]. Since the estimated value of ε k based on experimental acquisition differs to the true sensitivity map calculated by summing up all the LORs by a constant scaling factor, a normalization factor F (n) should be calculated for each iteration step to keep K k=1 x (n+1) k = N via:…”

Section: A Acquisition Strategymentioning

confidence: 99%

“…Such DOI resolution, in addition to the high number of crystals, results in a total of over 3 billion possible lines of responses (LORs). This requires the use of list-mode reconstruction to maintain efficiency and accuracy [3], [4]. Effective correction approach for random coincidences is therewith a challenge for this system, because i) the classic random correction method with direct subtraction of delayed coincidences can induce a high level of noise; and ii) noise reduction for projection data is not directly applicable for listmode data without histogramming into sinograms.…”

Section: Introductionmentioning

confidence: 99%

Random correction using large coincidence window for the clear-PEM system

Cao

Bugalho

Ferreira

et al. 2013

2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC)

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The Clear-PEM system is a dedicated PET scanner optimized for breast imaging. Because of the limited field-of-view (FOV) and the moderate timing resolution, the imaging results of the system might be degraded by random noise. This noise is more pronounced at the region that is near to the torso, which could cause false-positive or inconclusive diagnostics. Because of the high number of lines of responses (LORs) comparing to the number of acquired coincidences, list-mode reconstruction is required to maintain efficiency and accuracy. A new acquisition strategy is presented in this abstract in order to largely increase the statistics of acquired random events, without the requirement of hardware to collect single counts. During data acquisition, a large coincidence window of 90 ns is set in the readout electronics. All data within this window are collected in list-mode and, afterwards, classified by the acquisition software into prompt counts (0-4 ns), ignored counts (4-20 ns) and random counts (20-90 ns). A smooth correction image is estimated using those collected random counts. Reconstruction is afterwards performed considering the correction image with multiplication of the ratio between coincidence window width and random window width. An experimental study was performed on a breast-torso phantom. Results show that this approach can increase the statistics of recorded random coincidences over 17 folds, leading to a pronounced improvement of random correction effect. Because of the adoption of a 90 ns coincidence trigger in the hardware that can filter out most of the counts, it yields a much less computational burden to the acquisition system in comparison to the correction method using single count rate.

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“…Assuming that the total counts on the jth bin, Y j , can be separated into true counts, Yj , and random counts, P j (ignore scatter counts), the reconstruction problem can be formulated as: K Yj = Y j -P j = I C j. kXk· (6) k=1…”

Section: Random Correction With Smooth Correction Imagementioning

confidence: 99%

“…For the specific geometry of a PEM system, method i) induces a high statistical noise especially for the region close to torso due to the low statistics of the detected events. Furthermore, because of the low number of detected coincidence events against the extremely high number of LORs list-mode reconstruction algorithms are suggested for PEM systems [5], [6]. Method ii), however, cannot be applied to list-mode data.…”

Section: Introductionmentioning

confidence: 99%

Random correction method for positron emission mammography using delayed coincidence data

Cao

Bugalho

Ortigao

et al. 2012

2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC)

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A dedicated random correction algorithm is pre sented in this study for positron emission mammography (PEM) systems. PEM refers to a specified PET system that is optimized for breast imaging by its small FOV. Clinical imaging results from such systems, however, may be degraded by strong statistical noise caused from random coincidences, especially in the region that is near to the torso, due to the high amount of activity uptake outside the FOV, the low geometrical sensitivity of the detector elements near to the torso, and the large solid angle acceptance of random coincidence events. Because of the low statistics of detected coincidence events against the extremely high number of LORs, list-mode reconstruction algorithms are suggested for PEM systems. The conventional random correction methods cannot be directly implemented or can induce an even higher statistical noise. The correction method by single count rate requires a high hardware cost to record single events and needs an accurate calibration to reach a non-bias correction.The new random correction algorithm presented in this study can be implemented into list-mode reconstruction without single count acquisition. This algorithm estimates in a first step a smooth correction image with the delayed coincidences data.This correction image is then used to estimate the mean random coincidence rate for each detected event during the iterative list-mode reconstruction routine. The approach is tested on a ClearPEM system developed by the Crystal Clear Collaboration. Experimental data are acquired by two face-to-face detectors at four angular positions with a total acquisition time of 20 min for each breast. Results show that the proposed algorithm can largely suppress the statistical noise in the region near the torso.

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List-mode maximum-likelihood reconstruction for the ClearPEM system

Cited by 11 publications

References 8 publications

Calibration and dead time correction for a PET scanner with variable geometry

Calibration and dead time correction for a PET scanner with variable geometry

Random correction using large coincidence window for the clear-PEM system

Random correction method for positron emission mammography using delayed coincidence data

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