4.5 Article

3D digital breast cancer models with multimodal fusion algorithms

Journal

BREAST
Volume 49, Issue -, Pages 281-290

Publisher

CHURCHILL LIVINGSTONE
DOI: 10.1016/j.breast.2019.12.016

Keywords

Breast cancer; 3D breast model; Fusion; Magnetic resonance imaging; Surface; Multimodal registration

Funding

  1. ERDF-European Regional Development Fund through the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement
  2. ERDF-European Regional Development Fund through the Portuguese National Innovation Agency (ANI) [BCCT.PlaneNORTE-01-0247-FEDER-01768]
  3. Fundacao para a Ciencia e a Tecnologia (FCT) [SFRH/BD/115616/2016]
  4. Fundação para a Ciência e a Tecnologia [SFRH/BD/115616/2016] Funding Source: FCT

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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.

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