Comparative evaluation of scatter correction techniques in 3D positron emission tomography

Abstract

Abstract. Much research and development has been concentrated on the scatter compensation required for quantitative 3D positron emission tomography (PET). Increasingly sophisticated scatter correction procedures are under investigation, particularly those based on accurate scatter models and iterative reconstruction-based scatter compensation approaches. The main difference among the correction methods is the way in which the scatter component in the selected energy window is estimated. Monte Carlo methods provide further insight and might in themselves offer a possible correction procedure. Five scatter correction methods were compared in this study where applicable: the dual-energy window (DEW) technique, the convolution-subtraction (CVS) method, two variants of the Monte Carlo-based scatter correction technique (MCBSC1 and MCBSC2) and our newly developed statistical reconstruction-based scatter correction (SRBSC) method. These scatter correction techniques were evaluated using Monte Carlo simulation studies, experimental phantom measurements and clinical studies. Accurate Monte Carlo modelling is still the gold standard since it allows the separation of scattered and unscattered events and comparison of the estimated and true unscattered component. In this study, our modified version of Monte Carlo-based scatter correction (MCBSC2) provided a good contrast recovery on the simulated Utah phantom, while the DEW method was found to be clearly superior for the experimental phantom studies in terms of quantitative accuracy at the expense of a significant deterioration in the signal-to-noise ratio. On the other hand, the immunity to noise in emission data of statistical reconstruction-based scatter correction methods makes them particularly applicable to low-count emission studies. All scatter correction methods gave very good activity recovery values for the simulated 3D Hoffman brain phantom, which averaged within 3%. The CVS and MCBSC1 techniques tended to overcorrect while SRBSC undercorrected for scatter in most regions of this phantom. It was concluded that all correction methods significantly improve the image quality and contrast compared to the case where no correction is applied. Generally, it was shown that the differences in the estimated scatter distributions did not have a significant impact on the final quantitative results. The DEW method showed the best compromise between ease of implementation and quantitative accuracy, but entailed a significant deterioration in the signal-to-noise ratio.

Which attenuation coefficient to use in combined attenuation and scatter corrections for quantitative brain SPET?

“…The authors report some interesting results concerning the effect of triple-energy window-based scatter correction on brain perfusion SPET using statistical parametric mapping (SPM) analysis. The research performed is worthwhile and contributes significantly to our understanding of the effect of scatter correction; to the best of our knowledge, it is also the first time that SPM analysis has been used for this purpose instead of conventional qualitative and region of interest (ROI)-based quantitative evaluations [2]. However, we feel that certain relevant issues were not sufficiently addressed by the authors, and we would like to make some comments on this work.…”

“…As mentioned in the Introduction to our paper, there is an excellent paper by Iida et al [2] on the quantitative difference in regional cerebral blood flow with different scatter and attenuation correction methods based on re-…”

“…Attenuation correction with the measured µ map definitely leads to overcorrection, especially in the central part of objects, without scatter correction. On the other hand, Iida et al [2] reported that reconstruction using a uniform µ attenuation map and a narrow µ with scatter correction could be used to assess regional cerebral blood flow with little loss of accuracy in most brain regions. In our study, since we compared images with and without scatter correction, conditions had to be matched as closely as possible.…”

Однофотонная Эмиссионная Вычислительная Томография В Пропорциональной Рассеивающей Среде

“…Это обсто-ятельство обусловило появление большого количества исследований в области взаимодействия биологических тканей с оптическим излучением и трансмиссионной томографии сильнорассеивающих сред (СРС), в том числе трансмиссионной оптической томографии [7,[13][14][15][16]. В то же время в области разработки эмиссионной томографии в СРС исследования практически не про-водятся, за исключением методов частичной коррекции эффектов, связанных с рассеянием [17][18][19][20][21].Основой для разработки томографии рассеивающих сред является УПИ, учитывающее рассеяние. Однако в этом случае прямая задача из-за сложности УПИ общего аналитического решения не имеет, что вынуж-дает использовать различные дополнительные предпо-ложения, позволяющие его упростить.…”

PeneloPET, a Monte Carlo PET simulation tool based on PENELOPE: features and validation

“…The availability of powerful computers has encouraged the use of PET-dedicated simulation codes in the last few years. Examples of areas that benefit from extensive simulations are the design of new PET scanners (Braem et al 2004, Heinrichs et al 2003, the development and assessment of image reconstruction algorithms (Herraiz et al 2006) and of correction techniques (Levin et al 1995), among other applications (Zaidi 2000, Ay and Zaidi 2006, Ortuño et al 2003, Torres-Espallardo et al 2008. Simulations make it possible not only to refine the design parameters of PET scanners, but they also help to identify bottlenecks regarding count rate, resolution, sensitivity, etc.…”