A PET camera simulator with multispectral data acquisition capabilities

Cadorette

1995

IEEE Trans. Nucl. Sci.

Self Cite

We have shown in previous works that distinct non-stationary analytical scatter kernels can be extracted from line source measurements and used to independently subtract object scatter and subtract or restore detector scatter in high resolution PET. In this work, the dependence of the scatter components on energy threshold and source position I was investigated. Line source measurements were acquired in multispectral mode using the Sherbrooke PET simulator. Scatter parameters were extracted from data summed in energy windows with a lower threshold varying from 129 keV to 516 keV in steps of 42 keV, and a fixed upper threshold of 644 keV. Decreasing the lower threshold from 344 keV to 129 keV increases the trues by only 25%. but increases object scatter by 136% and almost triples detector scatter. A gain in efficiency by a factor of 2 or more would result from recovering the latter by restoration in the broad window. The intensity and shape of the scatter functions for both object and detector are shown to have a significant dependence on energy and position. This dependence needs to be taken into accwnt in the design of kernels for accurate scatter correction over a broad energy range.

Section: Discussionmentioning

confidence: 96%

“…All measurements were performed using the Sherbrooke PET simulator [15], set up to simulate a small animal PET imaging system [ 161. Projections were obtained from a "Na line source of diameter 0.85 mm placed at the edge of a Plexiglas cylinder of diameter 110 mm.…”

Section: Methodsmentioning

confidence: 99%

Object and detector scatter-function dependence on energy and position in high resolution PET

Bentourkia

Cadorette

1995

IEEE Trans. Nucl. Sci.

Self Cite

“…Complete tomographic data sets were obtained by scanning one of the two detector arrays and by rotating the object in a predetermined sequence [23]. The data acquired by energy windows above the threshold ( k , k 3) were rebinned into projections and interpolated to a sample density of 0.95 mm before reconstruction by filtered backprojection.…”

Section: A Phantom Measurementsmentioning

confidence: 99%

“…Each detector is capable of acquiring data in sixteen energy windows. One detector array and the object are rotated in a pre-determined sequence for tomographic data acquisition[23].…”

mentioning

confidence: 99%

Effects of energy space smoothing and projection space normalization on multispectral PET image quality

Yao

Bentourkia

1996

IEEE Trans. Nucl. Sci.

Self Cite

Independent processing of multispectral positron emission tomography (MSPET) data in individual energy frames has the potential to improve system sensitivity and the accuracy of energy dependent scatter correction. However, statistical fluctuations due to the use of multiple energy windows and low system detection efficiency severely undermine this potential. These limitations have been overcome without resolution loss by smoothing data in the energy space to suppress statistical fluctuations and by normalizing detector efficiency in the spatial domain to minimize systematic errors. The effectiveness of these corrections was evaluated by comparing images acquired in different energy frames with and without energy space smoothing. Smoothing improved the sharpness and contrast and decreased noise of images. The FWHM and FWTM evaluated from line source images confirmed an earlier postulate which stated that smoothing in the energy space has no effect on image resolution since such process does not move counts across lines of response (LORs). It was concluded that smoothing in the energy space in conjunction with normalization in the projection space is a prerequisite for subsequent energy-dependent data processing such as scatter correction. avoid aberrant deviations. These limitations partially explain why such pre-processing has generally been limited to the measured random data [16-181. We postulate that smoothing in the energy domain can minimize statistical fluctuations in MSPET data without experiencing the limitations above since the process does not move counts across lines of response (LORs). The aim of this paper is to verify the hypothesis and its implications on image quality. THEDRY A. DefinitionFig. 1 is a schematic diagram of multispectral energy and projection space. The coincident energy windows (k,l) of each line of response (LOR) form a 2-D energy spectrum [2].Like in conventional PET, the set of LORs associated with each window pair (U) can be rebinned into a sinogram in the projection space. This multi-dimensional data set can be reconstructed into a series of spectral images which will be called energy frames.

“…All measurements were made with the Sherbrooke multispectral PET simulator composed of two opposing arrays of eight detectors each (Lecomte et al 1993). With this setup, the projection space is an 8 × 8 matrix formed by the 64 coincidence LORs between the two detector arrays.…”

Section: Phantom Measurementsmentioning

confidence: 99%

Pre-processing variance reducing techniques in multispectral positron emission tomography

Yao

1997

Phys. Med. Biol.

Stochastic fluctuations and systematic errors severely restrict the potential of multispectral acquisition to improve scatter correction by energy-dependent processing in high-resolution positron emission tomography (PET). To overcome this limitation, three pre-processing approaches which reduce stochastic fluctuations and systematic errors without degrading spatial resolution were investigated: statistical variance was reduced by smoothing acquired data in energy space, systematic errors due to nonuniform detector efficiency were minimized by normalizing the data in the spatial domain and the overall variance was further reduced by selecting an optimal pre-processing sequence. Selection of the best protocol to reduce stochastic fluctuations entailed comparisons between four smoothing algorithms (prior constrained (PC) smoothing, weighted smoothing (WS), ideal low-pass filtering (ILF) and mean median (MM) smoothing) and permutations of three pre-processing procedures (smoothing, normalization and subtraction of random events). Results demonstrated that spectral smoothing by WS, ILF and MM efficiently reduces the statistical variance in both the energy and spatial domains without observable spatial resolution loss. The ILF algorithm was found to be the most convenient in terms of simplicity and efficiency. Regardless of the position of subtraction of randoms in the sequence, reduction of the systematic errors by normalization followed by spectral smoothing to suppress statistical noise produced the best results. However, subtraction of random events first in the sequence reduces computation load by half since the need to pre-process this distribution before subtraction is removed. In summary, normalizing data in the spatial domain and smoothing data in energy space are essential steps required to reduce systematic errors and statistical variance independently without degrading spatial resolution of multispectral PET data.