Analysing Spectral Imaging at Different Photon Energy Bins for Breast Lesion Contrast Visualisation from Computed Tomography Images
Year: 2024
Type: article
Abstract: The prevalence of breast cancer in Nigeria has become a source of concern as the yearly mortality rate has risen to about 102,000 cases. The National Cancer Control programme in Nigeria (NCCP) is focused on early detection and to extend the life of patients by utilising different screening modalities including the Computed Tomography (CT). However, preliminary work in breast CT has provided a number of compelling aspects that motivates the work featured in this research. These advantages include removal of the need to mechanically compress the breast which is a source of screening non-attendances, and that it provides unique cross-sectional images that removes almost all the overlying clutter seen in two dimensional (2-D) mammography. This renders lesions more visible and hence aids in early detection of malignancy. Work on breast CT to date has been focused on using scaled down versions of standard clinical CT systems. By contrast, this work proposes using a photon counting approach by investigating spectral imaging technology at different photon energy bins from conventional CT images for contrast visualization. It represents an idealized case of noiseless images that do not contain scatter or photon noise in order to study the intrinsic properties of contrast in CT. A breast phantom of diameter 100mm was analysed using a photon counting approach to simulate breast lesions. Investigations carried out in six (6) different experiments for lesion decomposition recorded higher contrasts between 1-60 keV. High contrasts values has been achieved at low energy bins which corresponds to the attenuation of glandular tissues. Photon counting approach has shown promise for the visualization of synthetic images in bins based on contrast investigations.
Source: Nigerian Journal of Physics
Authors: Liambee Alumuku, J. T. Iortile, T. Daniel
Institution: Federal University Wukari
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Funder: University of Surrey
OpenAlex ID: W4408973091