Real-Time Ultrasound Guided Abdominal Interventions Without a Tracking Device

无需跟踪设备的实时超声引导腹部干预

• 批准号: EP/T029404/1
• 负责人: Matthew Clarkson
• 金额: $ 129.37万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT029404%2F1
• 关键词: Real; Time; Ultrasound; Guided; Abdominal

This project aims to improve the ability of clinicians to diagnose and treat cancer, focussing on two specific procedures: laparoscopic liver resection and needle biopsy of the pancreas. Currently, both procedures require a high level of skill, resulting in a shortage of trained personnel, longer waiting lists and consequently delayed diagnosis or treatment, which is critical for the patient. Ultrasound imaging is commonly used to guide a variety of procedures. In laparoscopic liver resection for example, the surgeon will use ultrasound imaging to locate major blood vessels, and plan ahead. In endoscopic biopsy, the endoscopist will use ultrasound to navigate towards the pancreas and locate a specific tumour. However, both of these procedures are difficult, and carry the risk of mistakes. The ultrasound images are 2-dimensional (2D), and it is difficult to understand the location and orientation of the ultrasound image, with respect to the patient's anatomy. In addition, current research methods use expensive tracking devices and the software is difficult to use, so such methods cannot be commercialised or widely adopted, as they simply aren't user friendly. We will develop new technology that will align 2D ultrasound images with 3-dimensional (3D) pre-operative scan data such as Magnetic Resonance (MR), or Computed Tomography (CT). This will give the clinician a much wider context, improve their understanding of the location and orientation of the ultrasound probe, and enable quicker procedures. In the longer term, this will make the procedure easier and quicker to perform, allowing more patients to be examined quicker. The increase awareness and 3D context may potentially lead to fewer mistakes, and lower risk, although this is harder to demonstrate.To achieve this goal, we will exploit recent advances in machine learning to produce an algorithm that is reliable, robust and fast. New software will display the 2D ultrasound image, alongside the 3D scan and show the location of the ultrasound probe. We will deliver a method that does not require any extra equipment, does not hinder the clinical workflow, and does not require the clinician to interact with the software as it will be automatic and hands-free.In the longer term, these methods will be applicable to other ultrasound-based procedures in laparoscopy, endoscopy, fetal surgery, robotic surgery and beyond. The benefit to the general public will be faster and safer procedures, and the technology will enable more clinicians to perform these procedures, resulting in shorter waiting lists, and earlier treatment for the patient.

该项目旨在提高临床医生诊断和治疗癌症的能力，重点是两个特定的程序：腹腔镜肝切除术和胰腺穿刺活检。目前，这两种程序都需要高水平的技能，导致训练有素的人员短缺，等待名单延长，从而延误了对病人至关重要的诊断或治疗。超声成像通常用于指导各种手术。例如，在腹腔镜肝脏切除术中，外科医生将使用超声成像来定位主要血管，并提前计划。在内窥镜活检中，内窥镜医生将使用超声导航到胰腺并定位特定的肿瘤。然而，这两个过程都很困难，并且有出错的风险。超声图像是二维（2D）的，并且难以理解超声图像相对于患者解剖结构的位置和取向。此外，目前的研究方法使用昂贵的跟踪设备，软件难以使用，因此这些方法无法商业化或广泛采用，因为它们对用户不友好。我们将开发新技术，将二维超声图像与三维（3D）术前扫描数据（如磁共振（MR）或计算机断层扫描（CT））对齐。这将为临床医生提供更广泛的背景，提高他们对超声探头位置和方向的理解，并实现更快的手术。从长远来看，这将使程序更容易和更快地执行，使更多的病人更快地接受检查。意识和3D环境的增加可能会导致更少的错误和更低的风险，尽管这很难证明。为了实现这一目标，我们将利用机器学习的最新进展来产生一种可靠，强大和快速的算法。新软件将显示2D超声图像，以及3D扫描，并显示超声探头的位置。我们将提供一种不需要任何额外设备，不妨碍临床工作流程，不需要临床医生与软件交互的方法，因为它将是自动和免提的。从长远来看，这些方法将适用于腹腔镜，内窥镜，胎儿手术，机器人手术等其他基于超声的手术。对公众的好处将是更快和更安全的程序，该技术将使更多的临床医生能够执行这些程序，从而缩短等待名单，并为患者提供早期治疗。

项目成果

A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Models

• DOI: 10.59275/j.melba.2023-fbe4
• 发表时间: 2023-08
• 期刊: ArXiv
• 作者: Yunguan Fu;Yiwen Li;Shaheer U. Saeed;M. Clarkson;Yipeng Hu

Deep Generative Models - Third MICCAI Workshop, DGM4MICCAI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings

深度生成模型 - 第三届 MICCAI 研讨会，DGM4MICCAI 2023，与 MICCAI 2023 同期举行，加拿大不列颠哥伦比亚省温哥华，2023 年 10 月 8 日，会议记录

• DOI: 10.1007/978-3-031-53767-7_1
• 发表时间: 2024
• 作者: Fernandez V

Trackerless Freehand Ultrasound with Sequence Modelling and Auxiliary Transformation Over Past and Future Frames

• DOI: 10.1109/isbi53787.2023.10230773
• 发表时间: 2022-11
• 期刊: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
• 作者: Qi Li;Ziyi Shen;Qian Li;D. Barratt;T. Dowrick;M. Clarkson;Tom Kamiel Magda Vercauteren;Yipeng Hu

Cancer Prevention Through Early Detection - First International Workshop, CaPTion 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

通过早期检测预防癌症 - 第一届国际研讨会，CapTion 2022，与 MICCAI 2022 联合举行，新加坡，2022 年 9 月 22 日，会议记录

• DOI: 10.1007/978-3-031-17979-2_15
• 发表时间: 2022
• 作者: Gayo I

Evaluation of a calibration rig for stereo laparoscopes.

• DOI: 10.1002/mp.16310
• 发表时间: 2023-05
• 期刊: MEDICAL PHYSICS
• 影响因子: 3.8
• 作者: Dowrick, Thomas;Xiao, Guofang;Nikitichev, Daniil;Dursun, Eren;van Berkel, Neils;Allam, Moustafa;Koo, Bongjin;Ramalhinho, Joao;Thompson, Stephen;Gurusamy, Kurinchi;Blandford, Ann;Stoyanov, Danail;Davidson, Brian R.;Clarkson, Matthew J.
• 通讯作者: Clarkson, Matthew J.