IMAGING TABLE-TO-HEAD FRAME ADAPTER
    2.
    发明申请
    IMAGING TABLE-TO-HEAD FRAME ADAPTER 审中-公开
    成像台面对框架适配器

    公开(公告)号:WO2015109086A1

    公开(公告)日:2015-07-23

    申请号:PCT/US2015/011591

    申请日:2015-01-15

    Abstract: An adapter to be used to position and restrain a patient's head movement during magnetic resonance imaging (MRI), computed tomography (CT) imaging, positron emission tomography (PET) imaging, X-ray imaging or other imaging or medical procedures. The adapter has a high degree of adjustability and modularity. The adapter can be fitted to a patient quickly and comfortably by offering translational, rotational and height adjustment during the fitting procedure, thereby reducing the overall time for the MRI. The adapter includes a base, one or more attachment members to selectively affix the base to an imaging table, a pair of upwardly extending spaced-apart supports attached to the base, a stabilizer frame rotatably attached to the pair of supports, and a locking mechanism to selectively prevent rotation of the frame attached to the pair of supports.

    Abstract translation: 用于在磁共振成像(MRI),计算机断层摄影(CT)成像,正电子发射断层摄影(PET)成像,X射线成像或其他成像或医疗程序中定位和限制患者头部运动的适配器。 适配器具有高度的可调整性和模块化。 通过在装配过程中提供平移,旋转和高度调节,适配器可以快速舒适地安装到患者身上,从而减少MRI的总体时间。 适配器包括基座,一个或多个附接构件,用于选择性地将基座固定到成像台,附接到基座的一对向上延伸的间隔开的支撑件,可旋转地附接到该对支撑件的稳定器框架,以及锁定机构 以选择性地防止附接到所述一对支撑件的框架的旋转。

    DEEP BRAIN STIMULATION USING ARTIFICIAL NEURAL NETWORKS

    公开(公告)号:WO2020097618A1

    公开(公告)日:2020-05-14

    申请号:PCT/US2019/060956

    申请日:2019-11-12

    Abstract: Various embodiments of the present technology generally relate to closed loop deep brain stimulation based on inferred sleep stage from physiological data using machine learning classifiers. Some embodiments, for example, may use subthalamic nucleus (STN) deep brain stimulation (DBS) to treat advanced Parkinsons Disease motor symptoms and improve sleep by identifying sleep stages commensurate with clinician-scored polysomnography (PSG). The DBS may be adapted to include novel artificial neural network (ANN) that triggers targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. A feedforward neural network can be trained to prospectively identify sleep stage with PSG-level accuracy. In some embodiments, the machine learning model stored within the DBS may also adapt stimulation during specific sleep stages to treat targeted sleep deficits.

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