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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Evaluation of optimization in digital mammography through different formulations of Figure of Merit
Gustavo de Carvalho1, Janine H Dias2, Raíssa X Contassot2
1Grupo de Física Médica Experimental e Computacional, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, 90050-170, Brazil; Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, 90410-000, Brazil.
Abstract:
Mammography is a fundamental imaging modality for the early detection of breast cancer. The usage of Figure of Merit (FOM) to assess optimization in digital mammography helps to ensure a balance between image quality and the radiation dose in the procedures. The aim of this work was to find and discuss the best trade-off between dose and image quality in a digital mammography system through different Figure of Merit formulations, using CDMAM Phantom and ACR Phantom. Contrast-detail analyses were carried out with Inverse Image Quality Figure index (IQFinv), for three simulated thickness with PMMA plates (2.5, 3.5 and 4.5 cm), and contrast-to-noise ratio (CNR) for 4.5 cm thick ACR Phantom. The images were taken using the semi-automatic mode (AEC) and fully-automatic mode (OPDOSE) with four peak voltage values (26, 27, 28, and 29 kVp) and target-filter combinations of Mo/Mo, Mo/Rh, and W/Rh. The different FOM formulations took into account IQFinv and CNR as image quality parameters. The Mean Glandular Dose (MGD) was used as the dosimetric parameter in all cases. For all thicknesses, the parameters optimized by FOM, especially those with target-filter W/Rh, showed an improvement in image quality and a reduction in MGD compared to the results obtained with the OPDOSE mode. Furthermore, for the 4.5 cm thickness, the same trend was observed in the definition of the optimized parameters using different image quality metrics. The IQFinv proved to be a more detailed and objective parameter for intra-system evaluation in mammography, enabling similar results to the CNR.

