Dg. Kruger et al., A REGIONAL CONVOLUTION KERNEL ALGORITHM FOR SCATTER CORRECTION IN DUAL-ENERGY IMAGES - COMPARISON TO SINGLE-KERNEL ALGORITHMS, Medical physics, 21(2), 1994, pp. 175-184
Single kernel scatter correction algorithms are based on the model tha
t the scatter field can be predicted by convolution of the primary int
ensity (I-prim) with a spatially invariant scatter point-spread functi
on (PSF). Practical limitations (I-prim unknown) suggest the substitut
ion of the total detected intensity (I-det) for I-prim as the source i
mage in the convolution. In regions of high scatter fraction (SF), I-d
et is a poor approximation of I-prim, thereby causing an overestimatio
n of scatter originating in the region. This contributes to errors in
estimating detected scatter in the mediastinum and neighboring regions
. A technique using a regionally variable point-spread function that s
ignificantly reduces RMS error in estimation of the primary image as c
ompared to the single PSF method is investigated. The regionally varia
ble convolution method employs a larger PSF in the mediastinum and a s
maller PSF in the lungs to reduce the error in estimating the scatter
throughout the image. The method to allow for patient differences has
also been expanded and various implementations of these methods have b
een compared. Results show that the dual-kernel algorithm is always mo
re effective than an equivalent single-kernel algorithm. The dual-kern
el algorithm using a predicted scatter fraction curve gives an overall
RMS error in the primary of as low as 20.8% which is equivalent to 8.
7% RMS error in the scatter. The dual-kernel method using a predicted
scatter fraction curve approaches the accuracy of the single-kernel me
thod using patient specific scatter measurements. Because using indivi
dual scatter measurements is a less desirable method for clinical use,
we feel that the dual-kernel algorithm which uses two region specific
convolution kernels and a variable scatter fraction curve is the pref
erable method.