Taking advantage of this physical property, Kalender et al. has proposed to use a high-pass filtered version of the first basis material density image (e.g., water) to noise reduce the complimentary basis material density image (e.g., iodine) [ 12].
Cross-contamination is very undesirable as it alters the original density values and introduces false anatomical or pathological information to the complimentary basis material density image.
This technique is based on the knowledge that image noises between the basis material density image pair are anticorrelated [ 1, 12, 17].
Having the identical geometry, the same reconstruction algorithm to reconstruct the single-energy CT images can therefore be applied to the first basis material projection data to obtain the corresponding basis material density image.
Figure 6(a) contains a large amount of edge structures, such as ribs and vertebrae (arrows), suggesting a high likelihood of cross-contamination when the noise mask is used to denoise the complimentary basis material density image.
Then the SNR of iodine in a basis material density image is (2) SN R Δ E (x, y ) ∝ μ L I (x, y ) − w I μ H I (x, y ) σ L 2 (x, y ) + (w I ) 2 σ H 2 (x, y ), where w I is weighting coefficient to produce the iodine density image, and subscript H indicates the high-kVp image.
