Knowledge integration in ultrasound-guided medical interventions

超声引导医疗干预中的知识整合

• 批准号: 402358-2011
• 负责人: Laporte, Catherine
• 金额: $ 1.82万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637674
• 关键词: Knowledge; integration; ultrasound; guided; medical

The objective of this research program is to improve computer assisted medical interventions by providing image analysis tools to extract useful guidance information for the clinician from intra-operative imagery. Ultrasound (US) being flexible, portable, safe and low cost, emerges as a promising intra-operative modality, with freehand 3D US allowing the acquisition of 3D data using a conventional 2D probe widely available in the clinic. The challenges involved in exploiting these data pertain to simultaneous tracking of US transducer motion and tissue deformations, tissue characterisation, segmentation and registration to pre-operative patient imagery. The new tools will solve these problems by integrating information from the images themselves with measurements from position tracking devices as well as prior knowledge regarding the US data acquisition protocol, patient anatomy and tissue biomechanical properties. A key hypothesis is that by explicitly modelling the different sources of knowledge, including application-specific ones, and quantifying their respective reliability, optimal data fusion can be performed, leading to more accurate intra-operative guidance as well as safer and shorter interventions. New algorithms will be developed to track the position and orientation of an ultrasound transducer in 3D while compensating for tissue deformations arising from varying probe pressure. Through a collaboration with the Musculo-Skeletal Deformation Research Group at Sainte-Justine Hospital, these new algorithms will be applied to the context of minimally invasive spine discectomy, where an articulated 3D spine model derived from pre-operative MRI or bi-planar X-ray data must be updated and displayed to reflect global changes in spine shape caused by localised surgical manoeuvres. Here, freehand 3D US offers a non-invasive and practical alternative to landmark digitisation. The model updates thus obtained will assist the surgeon in adapting his strategy, improving the quality of patient treatment and cost to the healthcare system. Additionally, the methods developed within this research program will be adaptable to other types of interventions like image-guided needle biopsy and radiotherapy.

本研究计划旨在透过提供影像分析工具，为临床医师从术中影像中提取有用的指导资讯，以改善电脑辅助医疗介入。超声（US）灵活、便携、安全、低成本，成为一种很有前途的术中方式，徒手3D US允许使用临床广泛使用的传统2D探头获取3D数据。利用这些数据所涉及的挑战涉及同时跟踪US换能器运动和组织变形，组织表征，分割和术前患者图像的注册。通过将图像本身的信息与位置跟踪设备的测量结果以及有关美国数据采集协议、患者解剖结构和组织生物力学特性的先验知识相结合，新工具将解决这些问题。一个关键的假设是，通过明确地建模不同的知识来源，包括特定应用的知识来源，并量化其各自的可靠性，可以进行最佳的数据融合，从而实现更准确的术中指导以及更安全和更短的干预。将开发新的算法来跟踪三维超声换能器的位置和方向，同时补偿由不同探针压力引起的组织变形。通过与Sainte-Justine医院肌肉-骨骼变形研究小组的合作，这些新算法将应用于微创脊柱椎间盘切除术，其中从术前MRI或双平面x射线数据导出的关节三维脊柱模型必须更新和显示，以反映局部手术操作引起的脊柱形状的整体变化。在这里，徒手3D美国提供了一个非侵入性和实用的替代地标数字化。由此获得的模型更新将有助于外科医生调整他的策略，提高患者治疗的质量和医疗保健系统的成本。此外，本研究项目中开发的方法将适用于其他类型的干预，如图像引导针活检和放射治疗。