| Περίληψη: | Breast cancer screening and diagnosis in X-ray mammography rely on the detection and visualization of microcalcifications (μCs) and soft tissue masses. The early detection of breast cancer has been shown to decrease breast cancer mortality. The μCs are composed mainly of calcium with attenuation coefficients greater than that of soft tissue. The detection and visualization of μCs are relatively easy over a uniform tissue background, but limited by the ‘‘clutter’’ due to overlapping tissue background present on the mammogram. The clutter in tissue background arises from the structures of glandular tissue, vessels, and soft tissue masses in the breast. However, depending upon their size and location, the visualization of calcifications in mammograms may be limited by the superimposed anatomical structures even when the calcifications have adequate contrast-to-noise ratio (CNR).
Dual-energy subtraction imaging techniques offer an alternative approach to the detection and visualization of μCs. With this technique, high- and low-energy images are separately acquired and ‘‘subtracted’’ from each other in a weighted fashion to cancel out the cluttered tissue structure so as to decrease the obscurity from overlapping tissue structures.
In this study, computer simulations were developed in order to compute SNR as a function of various imaging parameters (X-ray spectra, μC size, breast thickness, and total exposure- 2 different cases) in Dual Energy Calcification images. Emphasis was placed on the monochromatization of the X-ray spectra with the use of K-edge filtering technique. Also, simulations provided values for various quality parameters of the spectra. Optimization was based on maximization of SNRtc while reducing total entrance exposure of the breast or Coefficient of Variation of the incident photons (CVIinc).
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