3D Freehand Ultrasound for Neurological Diagnosis

用于神经系统诊断的 3D 徒手超声

基本信息

项目摘要

The aim of this collaborative project between the Chair for Computer Aided Medical Procedures (TUM) and the Neurological and Neurosurgical Departments at Klinikum Großhadern (LMU) is to introduce the usage of 3D Freehand Ultrasound (3DUS) for diagnosis of neurological movement disorders in clinical neurology. Medical ultrasound (US) is a non-invasive, fast, inexpensive and widely available imaging technique, which has advanced in the past few years, increasing image resolution and improving signal-to-noise ratios. However even today, ultrasound imaging, in particular in 2D, requires special training and expertise for a physician to make correct conclusions from the image, especially due to ultrasound image artifacts, the limited field of view, the lack of anatomical context and resulting difficulties in choosing an optimal cut-plane of the anatomy.In our project, we introduce a novel method for neurological early and differential diagnosis of movement disorders with the help of transcranial 3DUS. In the past decade, research has indicated that transcranial US can help in visualizing pathophysiological changes of the basal ganglia in the midbrain associated with disorders such as Parkinson's Disease (PD) or dystonia. Interestingly, these changes can be observed in US, but are not always visible in other imaging modalities such as MRI. Moreover, recent research has shown a large potential of transcranial US for early diagnosis of PD, before motor symptoms occur. Due to its inexpensiveness, ultrasound can be thus considered as a first technique for screening of patients and early detection of diseases in the pre-clinical stage, creating an enormous potential for this diagnostic method in future.Due to aforementioned shortcomings in 2D ultrasound, however, the method is limited to a few reference groups with expert transcranial sonographers. Significant technical progress is necessary to make the technique more accurate and reproducible, while being available to a much larger group of examiners as it is now the case. We plan to improve the imaging technique by extending the current clinical practice and introducing the usage of 3DUS. The desired outcomes are semi-automated computer-assisted methods for volumetric ultrasound acquisition and analysis, which help in performing transcranial US diagnosis, even if the physician has less experience than current expert sonographers. Also, we plan to introduce a set of novel image segmentation and classification methods which help making the transcranial US examination more objective and reproducible than the current 2D method. As a further outcome of this project, we want to extend and investigate the potential of volumetric transcranial midbrain imaging for neurological diagnosis by using 3DUS and performing spatial analyses of echogenicities for the first time in this field.
计算机辅助医疗程序主席(TUM)和Klinikum Großhadern(LMU)神经和神经外科部门之间的这个合作项目的目的是介绍3D徒手超声(3DUS)在临床神经病学中诊断神经运动障碍的使用。医学超声(US)是一种无创、快速、廉价和广泛可用的成像技术,在过去几年中取得了进展,提高了图像分辨率并改善了信噪比。然而,即使在今天,超声成像,特别是在2D中,需要医生进行特殊的培训和专业知识,以便从图像中得出正确的结论,特别是由于超声图像伪影、有限的视野、缺乏解剖背景以及在选择解剖结构的最佳切割平面时产生的困难。我们介绍一种新的方法,神经早期和鉴别诊断的运动障碍的帮助下,经颅三维超声。在过去的十年中,研究表明,经颅超声可以帮助可视化与帕金森病(PD)或肌张力障碍等疾病相关的中脑基底神经节的病理生理变化。有趣的是,这些变化可以在US中观察到,但在其他成像方式(如MRI)中并不总是可见的。此外,最近的研究表明,在运动症状发生之前,经颅超声对PD的早期诊断具有很大的潜力。由于其价格低廉,超声可以被认为是在临床前阶段筛查患者和早期发现疾病的第一种技术,为这种诊断方法在未来创造了巨大的潜力。然而,由于2D超声的上述缺点,该方法仅限于少数专家经颅超声医师的参考组。需要取得重大的技术进步,使这项技术更加准确和可重复,同时像现在这样可供更多的检查员使用。我们计划通过扩展当前的临床实践和引入3DUS的使用来改进成像技术。期望的结果是用于体积超声采集和分析的半自动计算机辅助方法,其有助于执行经颅US诊断,即使医生的经验比当前的专家超声医师少。此外,我们计划引入一组新的图像分割和分类方法,这有助于使经颅超声检查比当前的2D方法更客观和可重复。作为该项目的进一步成果,我们希望通过使用3DUS并首次在该领域进行回声的空间分析来扩展和研究经颅中脑容积成像用于神经学诊断的潜力。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
3D transcranial ultrasound as a novel intra-operative imaging technique for DBS surgery: a feasibility study
MR imaging differentiation of Fe2+ and Fe3+ based on relaxation and magnetic susceptibility properties
  • DOI:
    10.1007/s00234-017-1813-3
  • 发表时间:
    2017-04-01
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Dietrich, Olaf;Levin, Johannes;Ertl-Wagner, Birgit
  • 通讯作者:
    Ertl-Wagner, Birgit
Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound
  • DOI:
    10.1016/j.cviu.2017.04.002
  • 发表时间:
    2017-11-01
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Milletari, Fausto;Ahmadi, Seyed-Ahmad;Navab, Nassir
  • 通讯作者:
    Navab, Nassir
A baseline study for detection of Parkinson's disease with 3D-transcranial sonography and uni-lateral reconstruction
  • DOI:
    10.1016/j.jns.2018.12.001
  • 发表时间:
    2019-02
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    A. Plate;Juliana Maiostre;J. Levin;K. Bötzel;Seyed-Ahmad Ahmadi
  • 通讯作者:
    A. Plate;Juliana Maiostre;J. Levin;K. Bötzel;Seyed-Ahmad Ahmadi
Coupling Convolutional Neural Networks and Hough Voting for Robust Segmentation of Ultrasound Volumes
  • DOI:
    10.1007/978-3-319-45886-1_36
  • 发表时间:
    2016-09
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Christine Kroll;F. Milletarì;Nassir Navab;Seyed-Ahmad Ahmadi
  • 通讯作者:
    Christine Kroll;F. Milletarì;Nassir Navab;Seyed-Ahmad Ahmadi
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Professor Dr. Kai Bötzel其他文献

Professor Dr. Kai Bötzel的其他文献

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开发基于 AI 的 2D 超声探头徒手 3D 系统
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