Abstract
Dual energy methods can suppress the contrast between adipose and glandular tissues in the breast and therefore enhance the visibility of calcifications. In this study, a dual energy method based on analytical modeling was developed for the detection of minimum microcalcification thickness. To this aim, a modified radiographic X-ray unit was considered, in order to overcome the limited kVp range of mammographic units used in previous DE studies, combined with a high resolution CMOS sensor (pixel size of 22.5 μm) for improved resolution. Various filter materials were examined based on their K-absorption edge. Hydroxyapatite (HAp) was used to simulate microcalcifications. The contrast to noise ratio (CNRtc) of the subtracted images was calculated for both monoenergetic and polyenergetic X-ray beams. The optimum monoenergetic pair was 23/58 keV for the low and high energy, respectively, resulting in a minimum detectable microcalcification thickness of 100 μm. In the polyenergetic X-ray study, the optimal spectral combination was 40/70 kVp filtered with 100 μm cadmium and 1000 μm copper, respectively. In this case, the minimum detectable microcalcification thickness was 150 μm. The proposed dual energy method provides improved microcalcification detectability in breast imaging with mean glandular dose values within acceptable levels.
Highlights
Breast cancer is one of the most common causes of cancer death in the United States [1]
The Dual energy (DE) method modeled in this study provides improved calcification detectability in breast imaging
The optimum pair of beam energies was selected by applying the criterion of CNRtc maximization
Summary
Breast cancer is one of the most common causes of cancer death in the United States [1] Diagnosis of this type of cancer in its early stage makes the treatment more effective [2,3,4]. Dual energy (DE) X-ray imaging can suppress the contrast between adipose and glandular tissues improving the detectability of microcalcificat ions (μCs) [10, 11]. With this technique, images that are acquired with two different X-ray spectra are subtracted, resulting in an image with enhanced microcalcification information [12]
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More From: Computational and Mathematical Methods in Medicine
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