Image-guided non-invasive tracking for radiotherapy using machine learning

使用机器学习进行图像引导的放射治疗无创跟踪

• 批准号: 286491894
• 负责人: Professor Dr. Mattias Heinrich
• 金额: --
• 依托单位: Institut für Medizinische Informatik (IMI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/286491894?language=en
• 关键词: Image; guided; non; invasive; tracking

Physiological motion of tumour patients is a prevailing uncertainty that leads to inaccurate delivery of radiation in both conventional radiotherapy and therapeutic ultrasound. Intra-operative guidance by magnetic resonance imaging or ultrasound, complemented with sophisticated image analysis is going to play a vital role in providing reliable, accurate and realtime information of tumour motion.The aim of this project is to advance the current state-of-the-art of intra-operative motion estimation by employing recent approaches from machine learning together with training data obtained with highly accurate keypoint registration. Instead of relying on classical statistical motion models, we plan to learn a cascade of nonlinear regression functions between image features and previously seen motion. Initial patient-specific image sequences acquired before the start of radiation delivery will be employed to generate training data using accurate but more time-consuming image registration. This will yield correspondences for relevant keypoints that are located on organ surfaces, vessels and the tumour itself. Building upon recent advances from the field of computer vision and machine learning, these correspondences are used to train a nonlinear model that links motion with specifically adapted robust image features that are learned for each modality using deep convolutional networks. By using shape augmentation together with population data and an online learning procedure, we will be able to limit the required number of frames in the training sequence.Incorporating the prior knowledge of pre-acquired images into the learned model will enable us to not only accurately track visible tumour or important vessel structures, but importantly also the shape and position of organs at risk, which will help to avoid radiation to healthy tissues and therefore side-effects of radiotherapy. The learned model may furthermore help to estimate the position of anatomies that are temporarily out of view or occluded. Due to the high computational efficiency of the planned regression model processing times of few milliseconds per image frame are expected.

肿瘤患者的生理运动是一种普遍存在的不确定性，导致传统放射治疗和治疗超声中的辐射传递不准确。通过磁共振成像或超声进行的术中引导，辅以复杂的图像分析，将在提供可靠、准确和实时的肿瘤运动信息方面发挥至关重要的作用。该项目的目的是通过采用最新的机器学习方法以及通过高精度关键点配准获得的训练数据来推进当前最先进的术中运动估计。我们计划学习图像特征和先前看到的运动之间的一系列非线性回归函数，而不是依赖经典的统计运动模型。在放射治疗开始之前获取的初始患者特定图像序列将用于使用准确但更耗时的图像配准来生成训练数据。这将产生位于器官表面、血管和肿瘤本身的相关关键点的对应关系。基于计算机视觉和机器学习领域的最新进展，这些对应关系用于训练非线性模型，该模型将运动与使用深度卷积网络针对每种模态学习的专门适应的鲁棒图像特征联系起来。通过将形状增强与人口数据和在线学习程序结合使用，我们将能够限制训练序列中所需的帧数。将预先获取的图像的先验知识纳入学习模型中，将使我们不仅能够准确跟踪可见的肿瘤或重要的血管结构，而且重要的是还能跟踪处于危险中的器官的形状和位置，这将有助于避免对健康组织的辐射，从而避免放射治疗的副作用。学习的模型还可以帮助估计暂时看不见或被遮挡的解剖结构的位置。由于计划回归模型的计算效率很高，预计每个图像帧的处理时间为几毫秒。

项目成果

Semantically Guided Large Deformation Estimation with Deep Networks

• DOI: 10.3390/s20051392
• 发表时间: 2020-03-01
• 期刊: SENSORS
• 影响因子: 3.9
• 作者: Ha, In Young;Wilms, Matthias;Heinrich, Mattias
• 通讯作者: Heinrich, Mattias

Comparing Deep Learning Strategies and Attention Mechanisms of Discrete Registration for Multimodal Image-Guided Interventions

• DOI: 10.1007/978-3-030-33642-4_16
• 发表时间: 2019-10
• 作者: I. Ha;M. Heinrich

Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions

• DOI: 10.1109/tbme.2018.2837387
• 发表时间: 2019-02-01
• 期刊: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
• 影响因子: 4.6
• 作者: Ha, In Young;Wilms, Matthias;Heinrich, Mattias P.
• 通讯作者: Heinrich, Mattias P.

Modality-agnostic self-supervised deep feature learning and fast instance optimisation for multimodal fusion in ultrasound-guided interventions

超声引导干预中多模态融合的模态不可知自监督深度特征学习和快速实例优化

• DOI: 10.1016/j.cmpb.2021.106374
• 发表时间: 2021
• 期刊: Computer methods and programs in biomedicine
• 影响因子: 6.1
• 作者: In Young Ha;Mattias P. Heinrich
• 通讯作者: Mattias P. Heinrich

Semantically Guided 3D Abdominal Image Registration with Deep Pyramid Feature Learning

通过深度金字塔特征学习进行语义引导的 3D 腹部图像配准

• DOI: 10.1007/978-3-658-33198-6_6
• 发表时间: 2021
• 作者: Mona Schumacher;Daniela Frey;In Young Ha;Ragnar Bade;Andreas Genz;Mattias P. Heinrich
• 通讯作者: Mattias P. Heinrich