Evaluation of optimization in digital mammography through different formulations of Figure of Merit.

Authors

Gustavo de Carvalho, Janine H Dias, Raíssa X Contassot, Brenda Candeia, Rochelle Lykawka, Maurício Anés, Alexandre Bacelar, João V B Valença

Published in

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine. Volume 239. Pages 112962. Oct 01, 2026. Epub Oct 01, 2026.

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 (IQF_inv), 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 IQF_inv 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 IQF_inv proved to be a more detailed and objective parameter for intra-system evaluation in mammography, enabling similar results to the CNR.

PMID:
42828788
Bibliographic data and abstract were imported from PubMed on 04 Oct 2026.

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