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磁共振引导高强度聚焦超声治疗计划的分割方法。

Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning.

机构信息

Tecnologico de Monterrey, Campus Estado de México, Atizapán de Zaragoza, MEX, Mexico.

Tecnologico de Monterrey, Campus Santa Fe, Álvaro Obregón, Ciudad de México, Mexico.

出版信息

J Healthc Eng. 2017;2017:5703216. doi: 10.1155/2017/5703216. Epub 2017 Jun 22.

Abstract

High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.

摘要

高强度聚焦超声(HIFU)是一种微创治疗方式,其中超声束在焦点区域集中,在焦点体积内产生温度升高和选择性消融,而周围组织保持完整。HIFU 已被提议用于安全消融恶性和良性组织,并作为药物输送的手段。磁共振成像(MRI)已被提议作为治疗的指导和监测方法。在 HIFU 治疗计划中,识别感兴趣的区域是一个关键步骤。该过程在磁共振图像中进行。本研究工作的目的是实现一种基于分水岭分割算法的高效且功能齐全的分割方案,用于 HIFU 治疗计划中使用的磁共振图像。实现具有功能结果的分割过程是可行的,但需要进行初步的图像处理步骤,以便为分割算法定义标记。此外,通过使用线程池,将分割方案并行应用于磁共振图像数据集,实现接近实时的执行,并有助于解决 HIFU 治疗计划中耗时的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d1db/5498923/77dcb5c21b33/JHE2017-5703216.001.jpg

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