English:
[0002] Current motion correction and motion compensation algorithms are retrospective. For example, motion-corrupted data may be rejected during the final image reconstruction. It is difficult, however, to determine during the imaging how much motion-corrupted data has been acquired. Thus, final image characteristics (e.g., signal-to-noise) may vary between otherwise identical scans since different amounts of data may be used in the image reconstruction. In addition, for medical imaging processes that require ionizing radiation (e.g., CT imaging) , it is desired to limit the radiation exposure to the patient and any operators near the imaging system. Acquiring corrupted data that will eventually be rejected, however, still exposes the patient to radiation without any added benefit. during the acquisition itself still present the same problem, as the image data must still be acquired first.
[0003] A method for monitoring motion during medical imaging. The method includes initiating an acquisition of image data, measuring physiological signals of a patient, generating a prediction signal by integrating the physiological signals, determining whether patient motion is likely to occur based on the prediction signal and modifying the acquisition of image data, if it is predicted that patient motion is likely to occur.
[0005] A non-transitory computer-readable storage medium including a set of instructions executable by a processor. The set of instructions being operable to initiate an acquisition of image data, measure physiological signals of a patient, generate a prediction signal by integrating the physiological signals, determine whether patient motion is likely to occur based on the prediction signal and modify the acquisition of image data, if it is predicted that patient motion is likely to occur.
[0006] Fig. 1 shows a schematic drawing of a system according to an exemplary embodiment.
[0007] Fig. 2 shows another schematic drawing of the system of Fig. 1.
[0008] Fig. 3 shows a flow diagram of a method according to an exemplary embodiment.
[0009] The exemplary embodiments may be further understood with reference to the following description and the appended drawings, wherein like elements are referred to with the same reference numerals. The exemplary embodiments relate to a system and method for optimizing a medical imaging process. In particular, the exemplary embodiments describe a system and method for predicting a motion of a patient and ceasing the acquisition of an image during the predicted motion period. Some patient motion may be voluntary such as, for example, when a patient moves to shift into a more comfortable position or to attend to an itch. Research has shown that neurophysiological measurements such as EEG, MEG or EMG show patterns can be used to predict and characterize the actual physical motion before the motion occurs. These measurements may correspond to the thought process that leads to the muscle contraction or the conveyance of that signal to the muscle groups that execute the voluntary motion. Involuntary motions may also be anticipated using similar approaches. Thus, by monitoring neurophysiological signals while a patient is being imaged, it is possible to predict some fraction of the patient motion that would adversely affect the imaging data and prevent the acquisition of image data during this period of motion.
[0010] As shown in Figs. 1 and 2, a system 100 according to an exemplary embodiment of the present disclosure predicts a motion of the patient during an imaging process via physiological measurements to avoid the acquisition of motion- corrupted data. The system 100 comprises a processor 102, a user interface 104, a display 106 and a memory 108. The processor 102 predicts the motion of the patient by measuring physiological signals from the patient via a monitoring system 110. The monitoring system 110 includes, for example, an electroencephalograph (EEG) , a magnetoencephalograph (MEG) , an electromyography (EMG) , an electrocardiograph (ECG/EKG) , a camera, a range imaging camera, a thermal probe or camera, pressure sensors or any sensor measuring any physiological state indicative of any upcoming motion, or any combination thereof. A study characteristic extraction module 112 extracts study information related to the requested image study such as, for example, body part (e.g., head, chest), modality (e.g., MRI, CT), protocol, resolution and view. A prediction engine 114 of the processor 102 interprets the signals measured by the monitoring system 110 and determines whether patient motion is likely to occur and the time at which the motion is likely to occur given the extracted study information. When the prediction engine 114 determines that patient motion will occur, the prediction engine 114 sends a stop signal to a scanner control module 116, which will cease the acquisition of image data by a scanner 120 during a given period. Optionally, the system 100 further comprises a restart module 118, which generates and sends a restart signal to the scanner 120 to restart the acquisition of image data. intervention by the user and/or a sensor observation via a restart monitor 124 that indicates that the patient has returned to his or her original position. Alternatively, sensors of the monitoring system 110 are used to detect the return motion of the patient.
[0011] As an additional optional feature, the system 100 also further comprises a mental focus device 122, which attracts the mental focus of the patient to prevent the patient from moving. For example, if the patient focuses on the mental focus device, there is less likelihood that attention is directed on other things that may lead to patient motion. The mental focus device 122 is, for example, a light, a video on a screen, an audio signal, or anything else that can capture the attention of the senses of a human being. In this embodiment, the monitoring system 110 is be used to detect the patient's level of attention to the mental focus device 122. As image data is acquired, the data is stored in the memory 108. The memory 108 optionally also stores patient data such as patient information (e.g., identifying information, symptoms, diagnoses), prior scans and an exam order for the image scan. Once all of the image data has been acquired, as necessary, the processor 102 compiles the image data to generate a final image which may be displayed on the display 106 and/or stored to the memory 108. The user may indicate any desired preferences and/or settings via the user interface 104, which includes input devices such as, for example, a keyboard, a mouse and/or a touch display on the display 106.
[0012] Fig. 3 shows a method 200 by which the system 100 predicts the motion of the patient and prevents the acquisition of image data during this period. In a step 210, the study characteristics extraction module 112 extracts study information for the requested scan such as body part, modality, protocol, resolution and view. For example, the study characteristics extraction module 112 analyzes the exam order to extract the study information. The study information is then utilized to determine a motion-tolerance indicator, in a step 220. The motion-tolerance indicator is for example a threshold value or a range of values indicating a tolerated range of motion of the patient. The motion-tolerance indicator is determined via, for example, a look-up table by the type of exam (e.g., MRI , CT) to determine a range of motion that can be tolerated without negatively impacting the image reconstruction. For example, an MRI may have a shorter tolerance while a CT scan may have a longer tolerance.
[0013] In a step 230, the scanner 120 begins to acquire image data. Where the system also includes a mental focus device 122 such as, for example, a light, video on a screen, audio signal, etc., the mental focus device 122 is also activated so that the user focuses on the device 122 while the scanner 120 is acquiring data. Video signals such as interesting news can be used to attract the mental focus such that the patient may be prevented from moving. Before the start of the imaging exam, the patient may choose from a selection of video or audio themes so that the patient can stay as focused as possible during the imaging exam.
[0014] In a step 240, the monitoring system 110 measures physiological signals of the patient. The physiological signals include for example electromagnetic signals (e.g., brain waves or signals emitted from other nervous system elements), skin appearance (e.g., color, texture), temperature, respiration rate, heart rate, perspiration, conductivity or mechanical pressure, or camera-based face expression recognition indicative of unease. Where a mental focus device 122 is also being utilized, the monitoring system 110 can measure and detect the level of attention that the patient is focusing on the mental focus device 122. The higher the level of mental focus, the lower the likelihood that the patient will focus on other things such as, for example, itchy areas, thereby reducing the potential for body motion. This technique may also be used to detect how well the subject is focusing on instructions given by the imaging equipment operator. For example, for cardiac MR acquisitions, where several breath hold acquisitions are performed, the level of mental focus indicates the patient's mental cooperativeness to perform the commands to hold his/her breath.
[0015] In a step 250, the physiological signals are sent to the prediction engine 114 so that the prediction engine 114 can integrate these signals over time and/or across signal types into a prediction signal, which predicts whether patient motion is likely to occur and the time in which it will occur. In one exemplary embodiment, the physiological signals are integrated over time by averaging over a period preceding the instant of the prediction. Alternatively, the minimum, maximum, median, standard deviation or any other statistical measure may be used.
[0016] In one example of system operation, the physiological signals are integrated across signal types via a mathematical function such as a linear combination of the signal values generated by the monitoring system 110. Alternatively, the combination may be a nonlinear function, the properties of which may be determined through known machine learning or optimization methods. The prediction engine module 114 may be trained with data from prior patients. For example, a collection of training data may be obtained for which the signal values are known and the occurrence or non-occurrence of a subsequent patient motion event within a fixed or adaptive time frame are also known. The parameters of the mathematical combining function (e.g., coefficients, exponents, etc.) may be optimized such that the choice of those parameters leads to a prediction of the patient motion. Methods for defining and generating such a function include but are not limited to artificial neural networks, repeated scans performed, the collected training data may be specific to the current patient. As a further option, the system 100 may further comprise a motion-detecting device such as, for example, a video camera with image analysis capability. The motion-detecting device is synchronized with the scanner 120. The prediction engine module 114 trained with data from prior patients is applied to the current patient to predict the motion of the current patient. If the current patient did not move as predicted, the recorded signals are labeled as belonging to the class of "no motion occurred." If the patient does move, the recorded signals are labeled "motion occurred." Once there is enough training data, the prediction engine module 114 is trained with the data of the current patient either alone or in addition to the training data of the prior patients to increase the performance and accuracy of the prediction engine module 114 in predicting the motion of the patient.
[0017] In another example of system operation, the prediction can be performed at one or more discrete time points and the generated predictions combined over time. If predictions are combined across signal types, the prediction engine module 114 may also predict the type of motion that is to occur. For example, the prediction engine module 114 may predict an anatomical location and scale of the predicted motion.
[0018] In a step 260, the scanner control module 116 receives the prediction signal and/or measured levels of attention to the mental focus device 122 to determine whether or not a motion is predicted to occur. For example, if the prediction signal exceeds a threshold value (e.g., the probability of motion is high) determined in the step 220, the system 100 may determine that a motion is predicted to occur and proceeds to a step 270, which modifies (e.g., ceases) the data acquisition. If the prediction signal is less than or equal to the threshold value, the system 100 may determine that a motion is not predicted to occur or is insufficient to impede the image reconstruction. In this case, the method 200 will return to the step 240 to continue to acquire physiological signals from the patient. As described above in regard to the step 220, the threshold value may vary based on extracted study information. The threshold value may vary depending on the body part. For example, the threshold may vary for scanning of the head compared with the chest compared with the legs. It will be understood by those of skill in the art that the determination of whether or not a motion - significant enough to affect image reconstruction - is likely to occur may be determined in any of a number of different ways using, for example, a range of values or a threshold value, as described above. Threshold values used to determine the level of the patient attention to the mental focus device may differ from the threshold values used to determine whether or not the data acquisition of the scanner 120 should be modi fied .
[0019] In the step 270, the data acquisition is modified when it is determined that the probability of motion exceeds a threshold value. In one example in which the scanner 120 is a CT scanner, the flow of electricity to the X-ray tube may be ceased when the motion is predicted to occur. In another example in which the scanner 120 is an MR scanner, the data acquisition may simply be ceased. In an alternative embodiment, if the movement location is predicted, then the acquisition may be shifted to a less motion sensitive area. For example, for MR scans in which image slices are obtained, the slice selection may be focused on a different part of the anatomy or portions of the MR k-space with less motion sensitivity closer to the center. In a further embodiment, the sensitivity of the system 100 may be adjusted by the user via the user interface 104. For example, the user may adjust the threshold value obtained in the step 220.
[0020] In a step 280, the restart module 118 sends a restart signal to the scanner 120 to resume the data acquisition or return to its original state. The restart signal may be based on a fixed timer, a manual intervention by the user or a sensor observation that indicates that the patient has returned to his/her original position. The sensors that detect this return motion may include elements of the monitoring system 100 or may be distinct. These sensors do not predict a motion but compare the current physical position of the patient with the physical position before the acquisition was modified in the step 270.
[0021] It is noted that the claims may include reference signs/numerals in accordance with PCT Rule 6.2(b) . However, the present claims should not be considered to be limited to the exemplary embodiments corresponding to the reference signs/numerals .
[0022] Those skilled in the art will understand that the above-described exemplary embodiments may be implemented in any number of manners, including, as a separate software module, as a combination of hardware and software, etc. For example, the study characteristics extraction module 112, the prediction engine module 114, the scanner control module 116 and the restart module 118 may be programs containing lines of code that, when compiled, may be executed on a processor.

中文:
[0002]当前的运动校正和运动补偿算法是回顾性的。例如,运动损坏的数据在最终的图像重建期间可能被拒绝。然而,难以在成像期间确定己经采集了多少运动损坏的数据。因此,最终的图像特性(例如信噪比)可能在否则会相同的扫描之间变化,这是因为可能在图像重建中使用不同量的数据。另外,对于需要电离辐射的医学成像过程(例如CT成像),期望限制对患者和接近成像系统的任何操作者的辐射暴露。然而,采集最终将被拒绝的损坏的数据仍将患者暴露于辐射而无任何附加的益处。使用运动补偿方法来在采集期间检测运动的设备本身仍存在同样的问题,因为仍必须首先采集图像数据。
[0003]一种用于监测医学成像期间的运动的方法。所述方法包括:启动对图像数据的采集;测量患者的生理信号;通过整合所述生理信号来生成预测信号;基于所述预测信号来确定患者运动是否可能发生;并且在预测到患者运动可能发生的情况下修改所述对图像数据的采集。
[0005]一种包括能由处理器运行的指令的集合的非瞬态计算机可读存储介质。所述指令的集合能够:启动对图像数据的采集;测量患者的生理信号;通过整合所述生理信号来生成预测信号;基于所述预测信号来确定患者运动是否可能发生;并且在预测到患者运动可能发生的情况下修改所述对图像数据的采集。
[0006]图1示出了根据示范性实施例的系统的示意性绘图;
[0007]图2示出了图1的系统的另一示意性绘图;
[0008]图3示出了根据示范性实施例的方法的流程图。
[0009]参考以下说明和附图,将进一步理解示范性实施例,其中,利用相同的附图标记来指示相同的元件。示范性实施例涉及用于优化医学成像过程的系统和方法。具体而言,示范性实施例描述了用于预测患者的运动并且在预测到的运动时段期间停止对图像的采集的系统和方法。一些患者运动可能是主动的,例如在患者移动以移位到更舒适的位置中或者处理发痒时。研究己经示出,诸如EEG、MEG或EMG的神经生理测量结果示出能够被用于在运动发生之前预测并表征实际身体运动的图样。这些测量结果可能与思维过程相对应,所述思维过程造成肌肉收缩或将该信号传达到执行主动运动的肌肉群。还可以使用类似的方法来预期非主动运动。因此,通过在患者被成像时监测神经生理信号,可能预测不利地影响成像数据的患者运动的一些部分,并且在该运动的时段期间防止对图像数据的采集。
[0010]如图1和图2所示,根据本公开的示范性实施例的系统100经由生理测量结果来预测在成像过程期间的患者的运动,以避免对运动损坏的数据的采集。系统100包括处理器102、用户界面104、显示器106和存储器108。处理器102通过经由监测系统110测量来自患者的生理信号来预测患者的运动。监测系统110包括例如脑电图描记器(EEG)、脑磁图描记器(MEG)、肌电图描记器(EMG)、心电图描记器(ECG/EKG)、相机、范围成像相机、热探头或相机、压力传感器、或者测量指示任何到来的运动的任何生理状态的任何传感器、或它们的任何组合。研究特性提取模块112提取与请求的图像研究有关的研究信息,例如身体部分(例如头部、胸部)、模态(例如MRI、CT)、协议、分辨率以及视图。处理器102的预测引擎114对由监测系统110测得的信号进行解释并且在给定提取出的研究信息的情况下确定患者运动是否可能发生以及运动可能发生的时间。当预测引擎114确定将发生患者运动时,预测引擎114向扫描器控制模块116发送停止信号,所述停止信号将在给定的时段期间停止由扫描器120进行的对图像数据的采集。任选地,系统100还包括重启模块118,所述重启模块118生成并向扫描器120发送重启信号,以重启对图像数据的采集。例如,重启信号基于由用户进行的人工介入和/或经由重启监测器124的传感器观测,所述传感器观测指示患者己经返回到他或她的原始位置。备选地,监测系统110的传感器被用于检测患者的返回运动。
[0011]作为额外的任选特征,系统100还包括精神集中设备122,所述精神集中设备122吸引患者的精神集中以防止患者移动。例如,如果患者专注于精神集中设备,则注意力较不可能被引导到可能引起患者运动的其他事情上。精神集中设备122例如是光、屏幕上的视频、音频信号、或能够吸引人类的感官注意的任何其他事物。在该实施例中,监测系统110被用于检测患者对精神集中设备122的注意力水平。在采集图像数据时,数据被存储在存储器108中。存储器108任选地也存储患者数据,例如患者信息(例如识别信息、症状、诊断)、针对图像扫描的先前扫描或检查医嘱(examorder)。一旦己经按需要采集了所有图像数据,处理器102将图像数据进行编译以生成最终的图像,所述最终的图像可以被显示在显示器106上和/或被存储到存储器108。用户可以经由用户界面104来指示任何期望的偏好和/或设定,所述用户界面104包括诸如键盘、鼠标和/或显示器106上的触摸显示器的输入设备。
[0012]图3示出了方法200,系统100通过所述方法200来预测患者的运动并且防止在该时段期间的对图像数据的采集。在步骤210中,研究特性提取模块112提取针对所请求的扫描的研究信息,例如身体部分、模态、协议、分辨率以及视图。例如,研究特性提取模块112分析检查医嘱以提取研究信息。在步骤220中,研究信息接着被用于确定运动容忍度指标。运动容忍度指标例如是指示对患者的运动的容忍范围的阈值或者值的范围。例如,运动容忍度指标是按检查的类型(例如MRI、CT)经由查找表来确定的,以确定能够被容忍而不会负面地影响图像重建的运动的范围。例如,MRI可能具有较短的容忍度,而CT扫描可能具有较长的容忍度。
[0013]在步骤230中,扫描器120开始采集图像数据。在系统还包括精神集中设备122(例如光、屏幕上的视频、音频信号等)的情况下,精神集中设备122也被激活,使得当扫描器120采集数据时用户专注于设备122上。诸如有趣的新闻的视频信号能够被用于吸引精神集中,使得可以防止患者移动。在开始成像检查之前,患者可以从视频或音频主题的选集中进行选择,使得患者能够在成像检查期间尽可能地保持专注。
[0014]在步骤240中,监测系统110测量患者的生理信号。生理信号例如包括电磁信号(例如从其他神经系统元发射的脑波或信号)、皮肤外观(例如颜色、纹理)、温度、呼吸率、心率、汗液、导电性或机械压力、或者指示不安的基于相机的面部表情识别。在也采用精神集中设备122的情况下,监测系统110可以测量并检测患者正在专注于精神集中设备122的注意力水平。精神集中的水平越高,患者专注于其他事情(例如痒的区域)的可能性就越低,从而降低身体运动的可能性。该技术也可以被用于检测对象有多么专注于由成像装备操作者给出的指令。例如,对于心脏MR采集,在执行若干屏气采集的情况下,精神集中的水平指示患者对执行屏住他/她的呼吸的命令的精神合作性。
[0015]在步骤250中,生理信号被发送到预测引擎114,使得预测引擎114能够随时间和/或跨信号类型地来将这些信号整合为预测信号,所述预测信号预测患者运动是否可能发生以及其将发生的时间。在一个示范性实施例中,通过在预测的时刻之前的时段上求平均来随时间整合生理信号。备选地,可以使用最小值、最大值、中值、标准差或任何其他统计学量度。
[0016]在系统操作的一个范例中,经由数学函数来跨信号类型地整合生理信号,所述数学函数例如是对由监测系统110生成的信号值的线性组合。备选地,所述组合可以是非线性函数,所述非线性函数的属性可以通过己知的机器学习或优化方法来确定。可以利用来自先前患者的数据来训练预测引擎模块114。例如,可以获得训练数据的集合,针对所述训练数据的集合的信号值是己知的,并且在固定的或自适应的时间框架内后续的患者运动事件的发生或未发生也是已知的。数学组合函数的参数(例如系数、指数等)可以被优化为使得对那些参数的选择引起对患者运动的预测。用于定义和生成这样的函数的方法包括但不限于人工神经网络、贝叶斯估计器,支持向量机以及最近整数分类器。这些方法然后产生在固定或自适应的时间框架内发生运动事件的概率。在另外的实施例中,在当前患者己经执行了重复的扫描的情况下,收集到的训练数据可以是特定于当前患者的。作为另外的选项,系统100还可以包括运动检测设备,例如具有图像分析能力的视频相机。所述运动检测设备与扫描器120同步。利用来自先前患者的数据训练的预测引擎模块114被应用到当前患者,以预测当前患者的运动。如果当前患者没有如所预测地移动,则记录下的信号被标记为属于“没有发生运动”的类别。如果患者移动了,则记录下的信号被标记为“发生了运动”。一旦存在足够的训练数据,则单独地或者附加于先前患者的训练数据来利用当前患者的数据训练预测引擎模块114,从而以增加预测引擎模块114在预测患者的运动中的性能和准确性。
[0017]在系统操作的另一范例中,可以在一个或多个离散时间点处执行预测,并且生成的预测随时间而被组合。如果预测是跨信号类型组合的,则预测引擎模块114也可以预测要发生的运动的类型。例如,预测引擎模块114可以预测所预测的运动的解剖结构位置和规模。
[0018]在步骤260中,扫描器控制模块116接收预测信号和/或测得的对精神集中设备122的注意力的水平,以确定是否预测到运动要发生。例如,如果预测信号超过在步骤220中确定的阈值(例如运动的概率是高的),则系统100可以确定预测到运动要发生并且前进到步骤270,所述步骤270修改(例如停止)数据采集。如果预测信号小于或等于阈值,则系统100可以确定没有预测到运动要发生或者运动不足以妨碍图像重建。在这种情况下,方法200将返回到步骤240以继续采集来自患者的生理信号。如以上关于步骤220所描述的,阈值可以基于提取到的研究信息而改变。阈值可以取决于身体部分而改变。例如,与胸部相比,与腿部相比,阈值可以针对对头部的扫描而改变。本领域技术人员将理解,对足够明显而影响图像重建的运动是否可能发生的确定可以是使用若干不同方式中的任何方式来确定的,例如如以上所描述的,所述不同方式使用值的范围或阈值。用来确定患者对精神集中设备的注意力的水平的阈值可以不同于用来确定扫描器120的数据采集是否应当被修改的阈值。
[0019]在步骤270中,当确定运动的概率超过阈值时,对数据采集进行修改。在扫描器120是CT扫描器的一个范例中,当预测到运动要发生时,可以停止到X射线管的电流。在扫描器120是MR扫描器的另一范例中,可以简单地停止数据采集。在备选的实施例中,如果预测到移动位置,则可以将采集移位到较不运动敏感的区域。例如,对于在其中获得图像切片的MR扫描,切片选择可以集中于解剖结构的不同部分或者MRk空间的更接近中心的具有较小的运动敏感度的部分。在另外的实施例中,可以由用户经由用户界面104来调节系统100的敏感度。例如,用户可以调节在步骤220中获得的阈值。
[0020]在步骤280中,重启模块118向扫描器120发送重启信号,以恢复数据采集或者返回到其原始状态。重启信号可以基于固定的计时器、用户的人工介入、或者指示患者己经返回到他/她的原始位置的传感器观测。检测该返回运动的传感器可以包括监测系统100的元件,或者可以是独特的。这些传感器不预测运动,但是将患者的当前身体位置与采集在步骤270中被修改之前的身体位置进行比较。
[0021]应该注意到,根据PCT条款6.2(b),权利要求可以包括附图标记/数字。然而,本发明的权利要求不应被认为限于与附图标记/数字相对应的示范性实施例。
[0022]本领域技术人员将理解,可以以任何数量的方式来实现以上描述的示范性实施例,包括作为单独的软件模块、作为硬件和软件的组合等。例如,研究特性提取模块112、预测引擎模块114、扫描器控制模块116以及重启模块118可以是含有代码行的程序,所述代码行在被编译时可以在处理器上运行。