Digital breast tomosynthesis: new strategies for optimizing acquisition geometry
Digital breast tomosynthesis (DBT) is a medical imaging modality that has been applied in breast cancer screening as it allows a more detailed analysis of breast structures compared to conventional digital mammography. Unlike the latter, in which two-dimensional (2D) images are obtained from two dif...
| Autor: | |
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| Formato: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | Brasil |
| Recursos: | Universidade de São Paulo (USP) |
| Repositorio: | Biblioteca Digital de Teses e Dissertações da USP |
| Idioma: | inglés |
| OAI Identifier: | oai:teses.usp.br:tde-21052025-154500 |
| Acesso em linha: | https://www.teses.usp.br/teses/disponiveis/18/18152/tde-21052025-154500/ |
| Access Level: | acceso abierto |
| Palavra-chave: | artefatos de reconstrução deep neural networks digital breast tomosynthesis ensaios clínicos virtuais image restoration interpolação de quadros de vídeo reconstruction artifacts redes neurais profundas restauração de imagem tomossíntese digital mamária video frame interpolation virtual clinical trials |
| Resumo: | Digital breast tomosynthesis (DBT) is a medical imaging modality that has been applied in breast cancer screening as it allows a more detailed analysis of breast structures compared to conventional digital mammography. Unlike the latter, in which two-dimensional (2D) images are obtained from two different views of a three-dimensional (3D) volume, DBT is a technique in which multiple X-ray projections are acquired at different angles and processed for the pseudo-3D reconstruction of the breast volume, minimizing tissue overlap. The quality of DBT images is strongly influenced by acquisition parameters such as angular range and the number of projections. Higher angular sampling inside the angular span can reduce undersampling artifacts, but this comes at the cost of distributing the total radiation dose across more projections. As a result, each individual projection receives a lower dose, leading to increased image noise and potential degradation in overall image quality. To address this trade-off and enhance DBT imaging without increasing patient radiation exposure, this work explores two innovative approaches: an image restoration pipeline to compensate for the reduced per-projection dose in high-angular-sampling DBT acquisition, and a deep learning-based video frame interpolation (VFI) model that generates synthetic projections to improve angular sampling. These interpolated images are added to the existing projections set, effectively increasing angular sampling without modifying the acquisition protocol or increasing workload. To validate these approaches, virtual phantom images were generated using virtual clinical trial (VCT) software, enabling controlled testing of different acquisition parameters. The restoration approach demonstrated improvement in contrast-to-noise ratio (CNR) for simulated breast lesions, with reduced noise and fewer artifacts in the reconstructed volume compared to a standard DBT acquisition with lower angular sampling. Similarly, the interpolation approach enhanced the CNR of calcifications and improved overall reconstruction quality, reducing artifacts. These findings highlight the potential of advanced computational techniques to refine DBT imaging, potentially improving lesion detection and diagnostic accuracy without additional radiation exposure to patients. |
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