3D digital breast cancer models with multimodal fusion algorithms

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Sílvia Bessa (Autor) Outra; Pedro F. Gouveia (Autor) Outra; Pedro H. Carvalho (Autor) Outra; Cátia Rodrigues (Autor) Outra; Nuno L. Silva (Autor) Outra; Fátima Cardoso (Autor) Outra; Jaime S. Cardoso (Autor) FEUP; Hélder P. Oliveira (Autor) FCUP; Maria João Cardoso (Autor) Outra

Revista

Título: Breast

Vol. 49

Páginas: 281-290

ISSN: 0960-9776

Editora: Elsevier

Indexação

ISI Web of Knowledge - 10 Citações

Scopus - 13 Citações

Outras Informações

ID Authenticus: P-00R-HFB

DOI: 10.1016/j.breast.2019.12.016

Abstract (EN): Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient's breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice. (C) 2020 The Authors. Published by Elsevier Ltd.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10