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具有多模态融合算法的 3D 数字乳腺癌模型。

3D digital breast cancer models with multimodal fusion algorithms.

机构信息

INESC TEC, Portugal; University of Porto, Portugal.

Champalimaud Foundation, Portugal; Medical School, Lisbon University, Portugal.

出版信息

Breast. 2020 Feb;49:281-290. doi: 10.1016/j.breast.2019.12.016. Epub 2020 Jan 3.

Abstract

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.

摘要

乳腺癌图像融合包括将患者的同步躯干和放射影像学的不同数据集进行配准和可视化,以构建 3D 模型。通过治疗医生对乳房进行空间解读和可视化,可以通过患者特定的数字乳房模型来增强,该模型将放射影像学图像集成到一起。但是,由于缺乏表面与放射信息之间良好相关性的基准,因此阻碍了潜在临床应用的发展。本研究设计了一种新的图像采集方案,用于获取带有乳房和躯干表面标记的患者乳房磁共振成像(MRI)和 3D 表面扫描数据。通过 MRI/3D 表面扫描融合算法,创建并验证了包含真实乳房躯干和肿瘤位置的患者特定数字乳房模型。该方案用于量化不同模态之间的乳房形状差异,并测量几种 MRI/3D 扫描融合算法变体的目标配准误差。不使用基于生物力学的姿态变换模型进行单乳房融合,可获得可接受的配准误差和准确的肿瘤位置。融合算法的性能不受乳房体积的影响。进一步的研究和虚拟临床接口可以使这项融合技术快速融入临床实践。

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