CN117425433A - 利用多个运动脉冲x射线源断层合成成像系统的人工智能训练 - Google Patents
利用多个运动脉冲x射线源断层合成成像系统的人工智能训练 Download PDFInfo
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Abstract
本发明公开了用于多个运动脉冲X射线源断层合成成像系统的图像识别人工智能(AI)训练方法。图像识别AI训练可以三种方式来执行:首先,使用现有的获取的带有已知结节的胸部CT数据集来生成合成断层合或图像,不应用X射线辐射;其次,利用带有模拟肺部结节的拟人化胸部体模来拍摄X射线原始图像,仅对体模应用X射线束;第三,使用来自患有真实已知结节和无结节的真实患者的多个运动脉冲源断层合成图像来获取X射线图像。一种X射线图像识别训练网络,其被配置为:接收X射线训练图像,自动地确定接收到的图像是否指示结节或病变状况。在训练之后,图像知识被更新并且被存储在知识数据库处。
Description
本发明要求以下申请的优先权:于2021年4月30日提交的临时申请序列号63182426;于2021年7月28日提交的临时申请序列号63226508;于2021年4月2日提交的临时申请序列号63170288、于2021年4月16日提交的临时申请序列号63175952、于2021年5月27日提交的临时申请序列号63194071;于2021年5月14日提交的临时申请序列号63188919;于2021年7月23日提交的临时申请序列号63225194;于2021年6月11日提交的临时申请序列号63209498;于2021年6月25日提交的临时申请序列号63214913;于2021年7月12日提交的临时申请序列号63220924;于2021年7月16日提交的临时申请序列号63222847;于2021年7月22日提交的临时申请序列号63224521;以及于2021年1月24日提交的美国申请序列17149133,该美国申请继而要求于2020年1月29日提交的临时序列62967325的优先权,上述申请的内容以引用方式并入。
技术领域
本发明整体涉及利用X射线图像进行基于人工智能(AI)的诊断训练的方法和设备,并且更特别地涉及用于利用多个运动脉冲X射线源断层合成成像系统进行对对象的肺部、乳腺疾病或缺陷识别的基于人工智能(AI)的诊断训练的方法和设备。
背景技术
断层合成(也称为数字断层合成(DTS))在与投影放射摄影相当的辐射剂量水平下执行高分辨率有限角度断层摄影。其已被研究用于各种临床应用,包括血管成像、牙科成像、骨科成像、钼靶摄影成像、肌肉骨骼成像和肺部成像。一个优点是DTS X射线剂量水平远低于CT成像剂量水平。DTS也比CT快得多并且成本低得多。尽管其可运行得很快,但其仍然主要依赖人类来用于诊断目的。因此,操作开销会增加,从而减慢诊断过程。
X射线断层合成成像通常由经过训练的X射线专家在临床环境中执行。存在具有临床意义的用于诊断性X射线成像的器官或其他组织的特定视图。临床标准可规定X射线技术人员根据诊断目的应捕获的视图。X射线技术人员通常需要专门训练以正确地操作X射线成像装备,并且以在获取成组X射线断层合成图像时从标准临床视图识别肺部结节或乳腺癌。
尽管如此,由X射线技术人员捕获的X射线断层合成图像通常由医生进行审查,以利用临床标准视图来确定捕获的图像足否充分地指示肺部结节状况或乳腺癌状况。虽然常规的X射线成像系统可能适用于医院或类似临床环境中的大多数患者,但此类系统需要相当大量的训练才能进行操作。这会增加此类X射线成像的总成本,并且进一步限制对于一般患者而言的可用性,因为只有训练有素的专业人员才能正确地操作常规的X射线成像设备。因此,常规的X射线成像系统人员训练需要相当大量的资源。
为了以低成本针对每一个人执行快速肺癌筛查和乳腺癌筛查,因此期望提供用于诊断肺部或乳房状况的具有很大改善的系统和方法。因此,基于人工智能(AI)的诊断技术变得必要。在可执行基于人工智能(AI)的诊断之前,有必要通过机器学习来进行人工智能(AI)诊断训练,以便建立知识数据库。
CT成像传统上已被用作3D X射线图像数据的主要宋源。CT图像也可提供对患者几何形状的准确表示。因此,合成断层合成图像也可从CT图像导出以用于人工智能诊断训练目的。使用带有模拟肺部结节的拟人化胸部体模也是进行人工智能诊断训练的不错选择。也可能有必要获取大量真实人类数据以便进行机器学习。
人工智能方法的特征在于,使用X射线图像识别软件包来对利用这种快速多个运动X射线源断层合成成像系统捕获的X射线图像做出诊断决定。X射线图像识别软件包需要巨大的计算能力,并且通常在云AI训练计算网络处运行。X射线断层合成成像系统也可从AI训练网络接收频繁的每月、每周或每日更新,因而在每次更新时接收存储在AI训练网络中的最新开发的X射线图像知识。
发明内容
提出了用于诊断目的的基于人工智能的训练方法以用于多个运动脉冲X射线源断层合成成像系统。训练过程也在使用人工智能诊断训练网络的云计算中执行。
在一个方面,利用现有的CT数据来执行基于人工智能的训练。获取的CT 3D图像来自患有真实已知结节的真实患者。然后,使用断层合成成像系统的几何形状,可以使用CT正向投影数据来生成合成断层合成原始图像数据,就像在X射线平板检测器处那样。通过应用反向投影,CT正向投影数据可被重建成合成断层合成图像数据,就像人们在临床标准视图处看到的那样。使用带有真实带注释结节的合成断层合成图像数据,则可执行基于人工智能的训练并且可更新知识数据库。在此训练过程期间,不涉及X射线束。优点在于。因此,如果可从获取的CT图像导出“伪断层合成图像”或“合成图像”(诸如伪或合成断层合成图像)则足有益的。具体来说,监督学习算法使用已知成集输入数据以及对该数据的已知响应或输出,并且随后训练模型以生成针对对新数据的响应的合理预测。系统的优点可包括以下中的一者或多者。系统生成合成数据以用于训练神经网络和AI,该合成数据是真实的并且利用临床标准视图进行检查以指示肺部结节。数据可用于多种目的,包括模型验证和AI模型训练以及数据增强。可创建合成数据来满足未在真实数据中发现的某些需求或状况。这在很多情况下是有用的。例如,医疗环境中的隐私要求可能限制数据可用性及其可使用的方式。隐私规则和其他法规可能对真实数据的使用施加限制。在其他示例中,对于待测试的产品而言需要数据。然而,此类数据对于测试人员而言不可用或尚不可用。机器学习算法需要训练数据。对于放射或其他医疗用途尤其如此,因为在现实生活中生成此类数据的成本可能很高。生成与真实医疗数据相同的数据的能力可以是用于创建针对测试或开发的场景的无限工具,合成数据可复制在真实数据中发现的统计性质但不泄露任何真实患者数据。数据创建可模拟尚未经历的状况:当真实数据不可用时,合成数据是唯一的出路。这些益处表明,随着数据变得更复杂并且受到更严密的保护,合成数据创建和使用将继续增多。
另一方面,利用带有模拟肺部结节的拟人化胸部体模来执行基于人工智能的训练。在人工智能训练过程期间,通过使用带有已知位置处的模拟肺部结节的拟人化胸部体模来获取X射线断层合成图像。通过比较带有结节和没有结节的体模图像,则可执行基于人工智能的训练,并且也可更新知识数据库。在此训练过程期间,X射线辐射仅作用在体模上,而不作用在真实患者身上。
另一方面,对患有真实肺部结节的真实患者执行人工智能训练。首先,从患有真实已知结节的真实患者获取X射线断层合成图像。其次,从没有真实结节的真实患者获取X射线断层合成图像。通过比较患有结节和没有结节的患者图像,则可执行基于人工智能的训练,并且也可更新知识数据库。在此训练过程期间,需要对患者进行X射线辐射。
X射线图像识别软件包被配置为:接收X射线图像知识,接收来自X射线断层合成成像系统的获取的X射线图像,以及基于X射线图像知识来确定接收到的X射线图像是否揭示肺部结节或乳腺癌状况。接收到的X射线图像被传输到X射线图像识别训练网络,以进一步训练和开发更新的X射线图像知识。
附图说明
结合附图,从以下详细描述中将更充分地理解本申请,其中相同的附图标记指代相同的元件,在附图中:
图1示出了示例性多个运动脉冲X射线源断层合成成像系统。
图2示出了使用多个运动脉冲X射线源断层合成成像系统对患者或物体进行的示例性诊断扫描。
图3示出了带有五个X射线源管的示例性断层合成成像系统扫掠扫描几何形状。
图4示出了使用带有模拟肺部结节的拟人化胸部体模的来自多个运动脉冲源断层合成成像系统的获取的X射线图像的集合。
图5示出了使用从成集CT图像生成的合成断层合成图像进行典型多个运动脉冲X射线源断层合成成像扫描时的AI训练流程图。
图6示出了使用带有模拟肺部结节的拟人化胸部体模的典型多个运动脉冲X射线源断层合成成像系统处的另一替代性AI训练流程图。
图7示出了使用实际患者的典型多个运动脉冲X射线源断层合成成像系统处的另一替代性AI训练流程图。
具体实施方式
在以下段落中,将参考附图通过示例来详细描述本发明。在整个描述中,所示的优选实施例和示例应视为是示例性的而非对本发明的限制。如本文所用,“本发明”是指本文描述的本发明的实施例中的任何一个以及任何等同物。此外,在整个文档中对“本发明”的各种特征的引用并不意味着所有要求保护的实施例或方法必须包括所引用的特征。
然而,本发明可以以许多不同形式体现并且不应被解释为限于本文中所阐述的实施例。提供这些实施例是为了使得本公开将是透彻且完整的,并且将把本发明的范围完整地传达给本领域的普通技术人员。此外,本文叙述本发明的实施例及其具体示例的所有陈述都旨在涵盖结构等同物和功能等同物。此外,旨在是此类等同物包含当前己知的等同物和将来开发的等同物两者,即开发的执行相同功能的任何要素,而不管结构如何。
图1示出了使用多个运动脉冲X射线源来执行高效和超快的3D放射摄影的示例性X射线成像系统。其被称为多个运动脉冲X射线源断层合成成像系统1。多个脉冲X射线源安装在运动中的结构上以形成源阵列。该多个X射线源管6在预定义弧形轨道上以作为组的恒定速度相对于物体同时移动。管组在初级马达工作台3上,并且移动由初级马达2控制。每个X射线源管也可围绕其静态位置以小距离快速移动。每个管都在次级马达工作台5上,并且小振动移动由次级马达4控制。当X射线源具有等于组速度的速度但具有相反的移动方向时,通过外部暴露控制单元来激活X射线源6和X射线平板检测器8以暂时保持静止。此配置导致对于每个X射线源6而言大大减小的源行进距离。3D扫描可在短得多的时间内覆盖广泛得多的扫掠角度,并且也可实时进行图像分析。此类型的X射线机比其他类型的X射线成像机利用更多的X射线源6宋实现高得多的扫描速度。
断层合成成像系统的一个实施例具有四个重要部分:数字放射摄影系统、3D切片投影装备、多个运动中的脉冲源控制系统以及图像识别软件包。3D扫描仪使用用于数据处理的工业计算机、用于信号传递的网络装置、用于数据分析的带有足够存储空间的计算机以及用于显示经处理数据的高性能图形卡。成像系统可连接到医院网络,开且可将图像文件传输到中央医院网络。系统可将患者信息和诊断结果传输到医生的办公室。系统可通过使用多个脉冲源来减少X射线剂量并且加快诊断过程,该多个脉冲源可实现数秒发射并且因此在减少扫描时间的同时减少辐射剂量。
图2示出了对患者或物体7的诊断扫描。新X射线成像系统使用多个运动脉冲X射线源宋执行主要用于肺部成像或乳腺钼靶摄影的X射线成像。此系统不需要拍摄手臂的图像。其可易于在几秒内采用120度或以上宋进行扫描。在图2中,存在多个X射线源6,患者7被置于X射线平板检测器8的前方。多个X射线源6围绕正被成像的身体区域或器官旋转。例如,患者的肺部。X射线成像检测器8被设计成围绕受试者的身体转动来以不同的角度从每个源位置捕获多个源位置中的多个源图像,并且将这些图像存储到计算机存储装置中作为多个源原始图像。来自多个源位置的图像被进一步处理以形成有关结节的更多信息。例如,其可与多视图检测器和剂量减少检测器相组合。进一步的处理可涉及计算机视觉,诸如对数据集的配准、分割、背景建模、物体分类、3D重建、可视化等。计算机视觉将使这些诊断更加自动化并且更具有成本效益。电子X射线成像检测器通常由成排固态图像传感器(有时配备有附加的滤波器)和成排电离室以及在一些情况下甚至是闪烁材料组成。其将具有成排光电二极管(有时由雪崩光电二极管制成)或成排CCD CMOS传感器,它们会将X射线光子转换为电子,电子然后将被转换为与由传感器接收到的辐射的强度相对应的数字值,所有这些都取决于所使用的技术。
诊断放射手术室中的患者7躺在检查台上。患者正在接受通过多个运动脉冲X射线源断层合成成像系统1进行的成像。断层合成成像系统可包括外部支撑件,该外部支撑件将断层合成成像系统定位在患者之上并且支撑机架。
图3示出了带有五个X射线源管6的示例性扫掠扫描几何形状。其例示了示例性五X射线源管配置,该配置通过每个X射线源管行进总距离的仅五分之一宋获取25个投影数据集。在此具体实施中,存在并行工作的五个X射线源管6,并且该五个X射线源管6在不同的角度位置执行总共25次X射线暴露。但每个次级马达工作台5仅需要行进总覆盖角度的五分之一。因此,在多个X射线源管6并行工作的情况下,可在一小部分时间内获取大量的投影数据。X射线平板检测器8充当X射线接收器。肺部数字断层合成使得能够在短暴露时间内从整个视场获取成集断层摄影图像数据。电子信号总足比机械运动进行得更快,因此瓶颈总足来自机械侧。
图4示出了使用带有模拟肺部结节的拟人化胸部体模从多个运动脉冲X射线源断层合成成像系统1获取的X射线图像。X射线断层合成图像的显示带有临床标准视图或临床期望视图。这种图像显示为大多数放射科医师和医生所接受。在扫描开始时获取的X射线断层合成图像9在顶部,并且在扫描结束时获取的X射线断层合成图像10在底部。其揭示了断层合成肺部图像实际上足视角有限的3D图像。在肺部断层合成中,X射线管在肺部之上呈弧形移动,从不同的角度捕获肺部的多个图像。这些数字图像然后通过计算机被重建或“合成”为成集三维图像。肺部数字断层合成是一种有限角度图像断层摄影,其与放射摄影成像相比改善了解剖结构的可视性。由于肺部数字断层合成的有限获取角度,其有潜力显著增加患者监视的时间分辨率,代价是在一个方向上的降低的分辨率。肺部数字断层合成与常规的放射摄影相比在识别肺结节方面的效率高若干倍,并且与例行的胸部计算机断层摄影(CT)检查相比剂量和成本更低。多个运动脉冲X射线源断层合成成像系统1能够在非常短的时间内产生大量的高质量3D X射线成像数据。尽管这些X射线图像可由放射科医师逐一查看和检查,但此机器实际上针对人工智能(AI)而设计以进行读取并且做出快速诊断决策。
X射线断层合成成像系统通常产生成集三维3D X射线图像,每个X射线图像表示患者的器官或其他组织的薄切片。X射线断层合成成像系统通常获取附加的视图,包括前、后和侧面图像。当观察方向围绕患者旋转时,成集3D X射线图像可按次序显示或组合成单个合成视图。例如,肺癌筛查需要捕获显示患者肺部的至少一个完整肺叶的充分可视化的X射线图像。对临床可接受性的确定可发生变化一存在不同的系统,无论其基于解剖结构还是在某一模式下扫描图像。
X射线检测器8具有X射线敏感元件阵列,该X射线敏感元件阵列感测X射线并且提供指示感兴趣区域的位置的信息。信息可被数字化或以其他方式处理以确定此位置。存储装置存储表示通过断层合成成像系统生成的图像的数据。此存储数据包括:通过断层合成成像获取的图像信息,以及指示每个获取的图像相对于数据集中的其他图像的位置的对应切片位置数据。这些数据用于训练人工智能算法,诸如神经网络。人工智能系统由耦接到存储器的至少一个处理元件构成。处理元件可被配置为通过用户界面从至少一个用户接收输入数据。输入数据可包括:医学X射线图像和/或表示在患者的肺部或胸部中检测到的结节或肿瘤的图像。此外,处理元件可被配置为执行本文所述的功能。例如,其可被配置为将图像与参考图像或预定诊断标准进行比较。合适的处理元件可由执行软件、硬件或它们的组合的通用计算机来提供。
为了增加从患者扫描识别潜在医疗问题的速度和准确度,应用人工智能(AI)。为了改善性能并且降低成本,优选实施例应用AI作为诊断工具来执行数据获取并且做出诊断决策。在AI可用作多个运动脉冲源断层合成成像系统中的决策者之前,必须首先对AI进行训练。AI程序必须首先经过训练。AI软件使用以下来获得训练数据集:通过从来自断层合成成像系统的原始数据进行重建而创建的虚拟图像,或者带有受刺激肺部结节(包括磨玻璃密度影(GGO)结节和慢性阻塞性肺疾病(COPD)结节)的拟人化胸部体模。拟人化体模由具有与正常生物有机体类似的组织特性的材料制成。由于其有限的可用性以及与实际患者的相似性,拟人化体模可用于各种任务。
人工智能(AI)或机器学习模型足一种数学算法,其被训练为得出与人类专家在被提供相同信息时将得出的相同的结果或预测。深度学习神经网络或人工神经网络试图通过数据输入、权重和偏好的组合来模仿人恼。这些要素共同起作用以对数据内的对象进行准确的识别、分类和描述。深度学习为机器学习的子集,机器学习本身为人工智能(AI)的子集。深度神经网络表示当系统使用许多层节点从输入信息导出高阶函数时的机器学习类型。深度学习中的“深度”足指神经网络随时间推移累积的该许多层,其中随着网络变得更深性能得到改善。卷积神经网络是一种深度学习算法,其可接受输入X射线断层合成图像,通过可学习的权重和偏好来为X射线断层合成图像中的各种方面或对象指派重要性,并且能够区分彼此。卷积神经网络(CNN)是最流行的神经网络架构之一。它们在X射线断层合成图像处理中极其有用。机器学习关乎计算机能够在较少人工干预的情况下进行思考和行动;深度学习关乎计算机学习使用基于人脑建模的结构来进行思考。深度学习可以机器学习无法轻松做到的方式来分析X射线断层合成图像。
利用可处理X射线断层合成图像以便检测癌性病变或任何其他类型的病变的经训练AI软件,放射科医师对X射线扫描图像的审查可显著减少以减少医生工作量。带有AI结果识别功能的断层合成成像将节省时间和成本,尤其是对于年龄太大或病重而无法进行CT扫描的那些患者。为了训练AI系统,图5至图7中公开了三种方法,如下详述。
作为AI训练的方法一,图5示出了使用从CT图像生成的合成断层合成图像进行典型多个运动脉冲X射线源断层合成成像扫描时的AI训练流程图。CT可在高费用和高X射线剂量的情况下得到全面得多的数据。CT带注释正向投影将创建虚拟体模。对于人工智能(AI)图像识别训练,如果不存在可用的实际患者原始数据,则可以使用带有已知结节的带注释CT肺部图像。CT肺部图像的正向投影可充当针对断层合成成像系统几何形状的虚拟物体体模。正向投影数据相当于X射线检测器处的原始数据。然后,根据正向投影数据,可以进行反向投影以产生3D断层合成成像作为带有临床标准视图的训练数据集。
典型多个运动脉冲X射线源断层合成成像系统的几何形状包括:X射线源到检测器距离、X射线源到物体距离、X射线扫描扫掠角度、X射线平板检测器尺寸和增量角度等,并且锥形束投影几何形状可在图1中识别。可使用这些几何形状参数来以数学方式执行正向投影。
使用CT正向投影数据和反向投影生成断层合成图像是基于可使用类似的组织衰减来得到断层合成图像的事实。在一种方法中,过程使用带有厚度校正的正向投影来创建CT扫描图像集,并且使用反向投影重建来将这些数据集重建成断层合成图像格式。此方法的一个优点是,由于正向投影成像的高准确度,可在准确了解X射线剂量的情况下创建断层合成图像,并且另一个优点是断层合成成像的速度相对较快。训练数据集被发送到智能(AI)图像识别训练网络作为输入。训练输出然后被存储作为图像知识数据库。在方法一中,在断层合成成像系统处不存在实际X射线束活动。
接下来详述从CT图像数据集创建虚拟患者的过程。在一个实施例中,可使用基于少量解剖特征的点对点匹配将CT图像和3D体模CT图像的配准配准到拟人化体模。例如,体模上的边缘有助于将CT图像配准到参考体积,通常每个患者四个或五个点将足以进行配准。然而,可使用更多点来改善准确度。此过程也应适用于可变形模板而不是固定标志。此步骤的输出将是与每个患者的CT图像数据集相对应的成组患者特定目标体积。对于每个目标体积将存在正向投影数据集以及反向投影数据集。这些目标体积本质上是每个患者的CT图像数据集的替代物,表示相同的几何形状,但其材料的特征在于由CT扫描过程产生的体素密度的不同水平。可估计目标体积中的所有体素的材料值。可能期望使目标体积平滑,以获得对骨骼与软组织之间的界面处的材料值的准确估计。
作为AI训练的方法二,图6示出了使用带有模拟肺部结节的拟人化胸部体模的典型多个运动脉冲X射线源断层合成成像系统处的另一替代性AI训练流程图。肺部结节包括磨玻璃密度影(GGO)结节和慢性阻塞性肺疾病(COPD)结节以得到训练数据集。拟人化体模由具有与正常生物有机体类似的组织特性的材料制成。由于其有限的可用性以及与真实患者的相似性,拟人化体模可用于多种任务。GGO结节和COPD结节的主要任务是得到图像识别软件以及正确的诊断。胸部体模可在标准或规定视图中,诸如前后胸部视图。使用这些模拟结节,系统可针对断层合成成像系统建立若干数据库图像,以得到针对每个训练数据集的带有不同数量的结节和不同位置的结节的不同训练数据集。使用最大似然估计(MLE)来确定将导致获取的图像的正常结构的对应数量。MLE用于通过形成获取过程的数学模型宋识别针对获取的X射线图像的统计上可能的参数。确定其最有可能落入哪种统计分布并且评估给定假设模型的图像的可能性,然后可将此知识库反馈到系统中以供将宋对X射线图像进行解译并且做出决策。此类经训练的人工智能学习网络然后可与放射科医师一起或者以防止误诊所需的任何附加能力独立地运行。所提出的方法要求在对通过所提出的方法生成的图像做出临床决策之前满足预先指定的标准。拟人化胸部体模训练数据集被发送到智能(AI)图像识别训练网络作为输入。必须真实地生成体模患者数据宋训练深度学习系统。训练输出然后被存储作为图像知识数据库。在方法二中,X射线束活动仅发生在体模处,而不发生在人身上。可对CT结果和体模结果进行比较以改善机器学习。
作为AI训练的方法三,图7示出了使用实际患者的典型多个运动脉冲X射线源断层合成成像系统1处的另一替代性AI训练流程图。使用实际患者来取得训练数据集将需要相当大的努力。如果存在例如需要1000个患者,则期望500个患者患有已知结节并且500个患者没有结节。挑战在于可能没有足够的患者。训练数据集被发送到智能(AI)图像识别训练网络作为输入。训练输出然后被存储作为图像知识数据库。在方法三中,X射线束活动实际上会发生在人身上。通常,从没有结节的真实患者获取X射线断层合成图像可能需要相当多的努力和大量的资源。这些数据将通过将它们分割为若干组(诸如abcdefg等)宋被进一步处理成训练数据集。然后每个组将具有经正向投影和反向投影分割的图像abcdefg等。在此过程之后,它们可被标记为abcdefg等。在对图像进行标记之后,它们被分成A组和B组,其包含磨玻璃密度影GGO结节和慢性阻塞性肺疾病COPD结节两者,并且可用作针对X射线图像识别软件的训练数据集,并且也可用于开发针对AI系统的X射线图像知识。可对体模结果和真实患者结果进行比较。获取有限数量的训练数据集,以便改善后续临床应用中的自动化结节检测的准确度和假阴性率。我们也利用更多的可用临床图像来训练深度学习模型,这可提供更好的性能。因此,本发明中存在若干优点,诸如消除了用于对每个原始CT图像进行注释以从模拟患者进行正向投影训练的工时成本,这可提供高得多的分辨率并且模拟在临床情形中将发生的情况。例如,对于心脏CT病例,我们只有用几小时工作时间才能得到图像,在使用心脏CT病例的情况下需要更多个小时来进行注释,而对于模拟患者,您只需要几分钟,并且在使用深度学习网络以通过从更多临床案例进行训练来实现更好的结果的情况下,对于应完成多少次没有限制。
外部暴露控制单元可被配置为:从训练网络接收数据,并且将辐射剂量调整至提供对基于AI的X射线图像识别训练网络的最佳开发的水平。外部暴露控制单元可以为能够执行对辐射剂量的期望调整的任何单元或单元的组合。例如,外部暴露控制单元可以为成像系统的集成部分,或者其可以为连接到成像系统的未示出的独立单元。通过有线或无线连接,多个运动脉冲源断层合成成像系统的实施例包括基于人工智能的X射线图像识别训练网络,该网络被配置为接收X射线训练图像并且开发X射线图像知识库。基于接收到的X射线训练图像,X射线成像系统从患者获取X射线图像,并且断层合成成像系统包括X射线图像识别软件包。X射线图像识别软件包被配置为:接收X射线图像知识,并且从X射线成像系统接收获取的X射线图像。基于X射线图像知识,其确定临床标准视图足否指示正常功能。接收到的X射线图像被传输到X射线图像识别训练网络,以用于进一步训练和开发更新的X射线图像知识库。
使用一种或多种方法的单个断层合成成像系统可接收关于所开发的X射线图像知识的输入。输入信息可包括关于知识的任何信息,诸如但不限于从已通过训练网络被分析的图像获得的训练知识。这样,X射线图像识别软件包结合获取的X射线图像知识可确定带有临床标准视图的图像足否指示肺部正常健康状况。在一些实施例中,X射线数据信息系统包括X射线图像识别训练网络,该网络被配置为:接收X射线训练图像,并且基于接收到的X射线训练图像宋开发X射线图像知识。
断层合成成像系统用于对患者的多个运动脉冲源X射线图像获取。获取的图像包括投影图像,其中每个投影图像表示来自特定获取角度的获取的图像的基于X射线的正向投影。通过使用投影图像作为训练数据集来生成虚拟体模,以开发X射线图像知识和图像识别神经网络模型。所开发的图像识别神经网络模型可用于任选地重建断层合成图像。人工智能软件包接收所生成的断层合成图像并且获取更新的X射线图像知识,以任选地生成进一步更新的断层合成图像。所生成的断层合成图像被传输到人工智能软件包,以使用图像识别神经网络模型和/或人工智能软件包宋更新图像识别神经网络模型。X射线技术人员可选择适当的获取角度来提供患者的所选断层合成图像,该图像指示患者的临床标准视图。
利用经验证的训练数据,可训练AI系统。在操作期间,患者数据被输入到放射摄影X射线获取系统。系统被配置为:在至少一个投影平面中从患者获取至少一个X射线图像。其中X射线图像为正向投影和/或反向投影和/或从由X射线成像装置获取的至少一个图像体积重建中的至少一者。断层合成图像重建模块被配置为:从患者的该至少一个X射线图像重建器官或其他组织的X射线图像。X射线图像分析模块被配置为:确定肺部或其他组织中的结节的分布。人工智能模块被配置为:基于确定的结节分布宋创建X射线图像知识。在计算机系统上运行的软件包包括人工智能AI训练网络,该网络被配置为:接收X射线训练图像,并且基于接收到的X射线训练图像来开发X射线图像知识。软件包也包括X射线图像识别软件包。X射线图像识别软件包被配置为:接收X射线图像知识,接收从X射线成像装置获取的X射线图像,并且基于X射线图像知识来确定临床标准视图是否指示正常功能。
所得系统可用于加快诊断。例如,当捕获肺癌筛查X射线图像时,放射技术人员通常获取示出患者的整个左肺的图像。放射科医师然后将审查X射线图像,并且如有必要则让X射线技术人员开发示出患者的整个右肺的附加X射线图像。因为此过程很耗时,所以其可能显著延迟对肺癌的诊断。X射线图像的适当组合将指示患者的肺部的哪些部分足清晰的并且没有任何异常,诸如结节。使用上述过程利用拟人化胸部体模(也称为合成体模)进行训练的AI系统可改善诊断成像装备的操作。
接下来描述用于多个运动脉冲源断层合成图像识别和学习算法的方法。一种用于使用人工智能的多个运动中的脉冲源断层合成图像识别的方式包括:从X射线成像装置接收多个X射线图像。在X射线源跨物体扫描作为训练数据的同时获取该多个X射线图像。系统向多个运动脉冲X射线源断层合成图像识别系统提供训练数据。使用人工智能作为针对多个运动脉冲X射线源断层合成成像系统的诊断工具使用人工智能(AI)宋尽可能多地捕获断层合成图像,并且将它们发送到AI模块,该模块例如应用各种算法,诸如单个结节检测器、肿瘤集群检测和拒绝错误检测。
从以上描述可以看出,本发明的实施例使用多个运动脉冲X射线源断层合成成像系统1和人工智能训练网络以用于X射线图像识别方法。过程由两个部分构成。一个部分足基于X射线训练图像宋开发X射线图像知识,另一个部分足通过经由对使用先前的X射线训练图像开发的X射线图像知识的更新来继续识别新的和获取的X射线图像的过程来创建此类更新的X射线图像知识。通过创建可靠的X射线图像知识库,该方法可进一步增加后续诊断过程的效率。此外,随着更多的X射线图像被接收到,所开发的X射线图像知识被不断地更新,并且可因此针对解剖结构和生理中的变化进行调适。
尽管上文就各种示例性实施例和具体实施而言描述了本发明,但应理解,在单独的实施例中的一个或多个实施例中描述的各种特征、方面和功能性不限于它们对利用其来描述它们的特定实施例的适用性,但相反可单独地或以各种组合应用于本发明的其他实施例中的一个或多个实施例。无论是否描述了此类实施例,也无论此类特征是否被呈现为所例示的实施例的一部分。因此,本发明的广度和范围不应受上述示例性实施例中的任一示例性实施例限制。
Claims (20)
1.一种利用多个运动脉冲X射线源断层合成成像系统进行人工智能训练的方法,所述方法包括:
选择来自患有已知结节的真实患者的获取的CT胸部3D图像数据集来进行人工智能训练;
使用带有几何形状参数的CT正向投影数据从成集多个运动脉冲X射线源断层合成成像系统生成合成断层合成原始图像数据;
使用反向投影重建从原始图像数据生成合成断层合成图像;
传输到图像识别软件包作为图像输入;
基于X射线图像知识来确定胸部图像是否指示结节或病变状况;以及
将输出X射线图像存储到知识数据库。
2.根据权利要求1所述的方法,其中所述人工智能训练包括:实现深度学习网络或卷积神经网络中的至少一者。
3.根据权利要求1所述的方法,其还包括:响应于确定接收到的X射线断层合成图像是否指示结节或病变,将反馈信号传输到CT正向和反向投影过程。
4.一种利用多个运动脉冲X射线源断层合成成像系统进行人工智能训练的方法,所述方法包括:
使用带有模拟肺部结节的拟人化胸部体模从成集多个运动脉冲X射线源断层合成成像系统获取X射线原始图像来进行人工智能训练;
重建以变成为带有临床标准视图的断层合成图像;
传输到图像识别软件包作为输入图像;
基于X射线图像知识来确定所述断层合成图像是否指示结节或病变状况;以及
将输出X射线图像存储在知识数据库中。
5.根据权利要求4所述的方法,其中所述人工智能训练包括:实现深度学习网络或卷积神经网络中的至少一者。
6.根据权利要求4所述的方法,其还包括:
响应于确定接收到的X射线断层合成图像是否指示结节或病变状况,将反馈信号传输到X射线断层合成成像系统。
7.根据权利要求4所述的方法,其还包括:
基于反馈信号来激活X射线断层合成成像系统中的反馈元件,以向所述X射线断层合成成像系统的用户提供反馈效果。
8.根据权利要求4所述的方法,其还包括:
响应于确定接收到的X射线断层合成图像是否指示结节或病变,将反馈信号传输到X射线断层合成成像系统。
9.一种设备,其包括:
成集多个运动脉冲X射线源断层合成成像系统,其用于获取人类患者或物体的X射线图像来进行人工智能训练;以及
成集X射线图像识别软件包,其被配置为:
接收获取的X射线断层合成图像;
自动地确定接收到的X射线图像是否指示人体中的结节或病变或者物体中的缺陷。
10.根据权利要求9所述的设备,其中所述人工智能训练包括:实现深度学习网络或卷积神经网络中的至少一者。
11.根据权利要求9所述的设备,X射线成像系统包括图形用户界面(GUI),所述GUI可操作以接收对带有临床标准视图的多个X射线断层合成图像的选择。
12.根据权利要求9所述的设备,其中X射线图像识别软件包可操作以自动地确定接收到的X射线断层合成图像是否指示结节或病变状况。
13.根据权利要求9所述的设备,其中X射线图像识别软件包还被配置为:响应于确定接收到的X射线断层合成图像是否指示结节或病变状况,向X射线断层合成成像系统提供反馈信号。
14.根据权利要求9所述的设备,X射线断层合成成像系统包括反馈元件,所述X射线断层合成成像系统被配置为:基于反馈信号来激活所述反馈元件,以向所述X射线断层合成成像系统的用户提供反馈效果。
15.根据权利要求9所述的设备,其还包括:
初级马达工作台,所述初级马达工作台在带有预定形状的弧形导轨上自由移动;
初级马达,所述初级马达与所述初级马达工作台接合并且控制所述初级马达工作台的速度;
多个X射线源,每个X射线源安装在所述初级马达工作台处;
支撑框架结构,所述支撑框架结构为所述初级马达工作台提供壳体;以及
X射线平板检测器,所述X射线严板检测器用于接收X射线并且传输X射线成像数据。
16.根据权利要求9所述的设备,其包括:在X射线源的每个X射线管上的成对电偏转板。
17.根据权利要求9所述的设备,其包括:在X射线源的X射线管中的每个X射线管上的一个或成对磁偏转线圈轭。
18.根据权利要求9所述的设备,其中使用预定方案来激活所述X射线源中的一个或多个X射线源,并且初级马达工作台的初始空间位置是通过软件可调整的。
19.根据权利要求9所述的设备,其包括用于以下的代码:
选择来自患有已知结节的真实患者的获取的CT胸部3D图像数据集来进行人工智能训练;
使用带有多个运动脉冲X射线源断层合成几何形状的CT正向投影数据来生成合成断层合成原始图像数据;
使用反向投影重建从原始图像数据生成合成断层合成图像;
传输到图像识别软件包作为图像输入;
基于X射线图像知识来确定胸部图像是否指示结节或病变状况;以及
将输出X射线图像存储到知识数据库。
20.根据权利要求9所述的设备,其包括用于以下的代码:
使用多个运动X射线源断层合成成像系统以X射线全视图对物体进行扫描;
在初始扫描之后使用人工智能来定位感兴趣区域;
确定针对X射线管中的每个X射线管的动态准直图;
将动态准直图应用于所述X射线管的运动控制;以及
在X射线射束仅限制在所述感兴趣区域上的情况下使用多叶片动态准直器来重新扫描所述物体。
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CN202280032904.5A Pending CN117280201A (zh) | 2021-04-02 | 2022-01-21 | 使用带有c臂的运动中的多个脉冲x射线源的快速3d放射摄影 |
CN202280026082.XA Pending CN117769389A (zh) | 2021-04-02 | 2022-01-21 | 通过使用电磁场偏转管电子束的带有多个脉冲x射线源的快速3d放射摄影 |
CN202280031396.9A Pending CN117615712A (zh) | 2021-04-02 | 2022-01-26 | 利用运动中的多个脉冲x射线源管的快速3d放射摄影 |
CN202280030967.7A Pending CN117241734A (zh) | 2021-04-02 | 2022-01-26 | 带有运动补偿式多个脉冲x射线源的使用x射线柔性曲面板检测器的快速3d放射摄影 |
CN202280034802.7A Pending CN117561026A (zh) | 2021-04-02 | 2022-03-02 | 利用多个运动脉冲x射线源断层合成成像系统的基于人工智能的诊断 |
CN202280031326.3A Pending CN117425433A (zh) | 2021-04-02 | 2022-03-14 | 利用多个运动脉冲x射线源断层合成成像系统的人工智能训练 |
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CN202280026082.XA Pending CN117769389A (zh) | 2021-04-02 | 2022-01-21 | 通过使用电磁场偏转管电子束的带有多个脉冲x射线源的快速3d放射摄影 |
CN202280031396.9A Pending CN117615712A (zh) | 2021-04-02 | 2022-01-26 | 利用运动中的多个脉冲x射线源管的快速3d放射摄影 |
CN202280030967.7A Pending CN117241734A (zh) | 2021-04-02 | 2022-01-26 | 带有运动补偿式多个脉冲x射线源的使用x射线柔性曲面板检测器的快速3d放射摄影 |
CN202280034802.7A Pending CN117561026A (zh) | 2021-04-02 | 2022-03-02 | 利用多个运动脉冲x射线源断层合成成像系统的基于人工智能的诊断 |
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