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3D digital breast cancer models with multimodal fusion algorithms
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 inte...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375583/ https://www.ncbi.nlm.nih.gov/pubmed/31986378 http://dx.doi.org/10.1016/j.breast.2019.12.016 |
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author | Bessa, Sílvia Gouveia, Pedro F. Carvalho, Pedro H. Rodrigues, Cátia Silva, Nuno L. Cardoso, Fátima Cardoso, Jaime S. Oliveira, Hélder P. Cardoso, Maria João |
author_facet | Bessa, Sílvia Gouveia, Pedro F. Carvalho, Pedro H. Rodrigues, Cátia Silva, Nuno L. Cardoso, Fátima Cardoso, Jaime S. Oliveira, Hélder P. Cardoso, Maria João |
author_sort | Bessa, Sílvia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7375583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73755832020-07-29 3D digital breast cancer models with multimodal fusion algorithms Bessa, Sílvia Gouveia, Pedro F. Carvalho, Pedro H. Rodrigues, Cátia Silva, Nuno L. Cardoso, Fátima Cardoso, Jaime S. Oliveira, Hélder P. Cardoso, Maria João Breast Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi 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. Elsevier 2020-01-03 /pmc/articles/PMC7375583/ /pubmed/31986378 http://dx.doi.org/10.1016/j.breast.2019.12.016 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi Bessa, Sílvia Gouveia, Pedro F. Carvalho, Pedro H. Rodrigues, Cátia Silva, Nuno L. Cardoso, Fátima Cardoso, Jaime S. Oliveira, Hélder P. Cardoso, Maria João 3D digital breast cancer models with multimodal fusion algorithms |
title | 3D digital breast cancer models with multimodal fusion algorithms |
title_full | 3D digital breast cancer models with multimodal fusion algorithms |
title_fullStr | 3D digital breast cancer models with multimodal fusion algorithms |
title_full_unstemmed | 3D digital breast cancer models with multimodal fusion algorithms |
title_short | 3D digital breast cancer models with multimodal fusion algorithms |
title_sort | 3d digital breast cancer models with multimodal fusion algorithms |
topic | Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375583/ https://www.ncbi.nlm.nih.gov/pubmed/31986378 http://dx.doi.org/10.1016/j.breast.2019.12.016 |
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