会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 1. 发明授权
    • Method and system for prediction of exposure and dose area product for radiographic x-ray imaging
    • 用于放射X光成像的暴露和剂量面积积预测的方法和系统
    • US06422751B1
    • 2002-07-23
    • US09130779
    • 1998-08-07
    • Richard AufrichtigGary F. RelihanClarence L. Gordon, IIIBaoming Ma
    • Richard AufrichtigGary F. RelihanClarence L. Gordon, IIIBaoming Ma
    • G01D1800
    • H05G1/28
    • A neural network prediction has been provided for predicting radiation exposure and/or Air-Kerma at a predefined arbitrary distance during an x-ray exposure; and for predicting radiation exposure and/or Air-Kerma area product for a radiographic x-ray exposure. The Air-Kerma levels are predicted directly from the x-ray exposure parameters. The method or model is provided to predict the radiation exposure or Air-Kerma for an arbitrary radiographic x-ray exposure by providing input variables to identify the spectral characteristics of the x-ray beam, providing a neural net which has been trained to calculate the exposure or Air-Kerma value, and by scaling the neural net output by the calibrated tube efficiency, and the actual current through the x-ray tube and the duration of the exposure. The prediction for exposure/Air-Kerma further applies the actual source-to-object distance, and the prediction for exposure/Air-Kerma area product further applies the actual imaged field area at a source-to-image distance.
    • 已经提供了神经网络预测用于在x射线曝光期间以预定义的任意距离预测辐射暴露和/或空气凯尔玛; 并用于预测放射线照射和/或Air-Kerma区域产品用于放射X光曝光。 空气凯尔玛水平可以直接从x射线曝光参数预测。 提供该方法或模型以通过提供输入变量来识别X射线束的光谱特征来预测用于任意射线照相X射线曝光的辐射暴露或空气 - 凯尔马,提供已经被训练以计算 曝光或空气凯马值,以及通过校准管效率和通过X射线管的实际电流和曝光持续时间缩放神经网络输出。 对于曝光/空气凯马的预测进一步应用实际的源对象距离,并且曝光/空气 - 凯马面积产品的预测进一步在源到图像距离处应用实际的成像场区域。
    • 2. 发明授权
    • Sampling methods and systems that shorten readout time and reduce lag in amorphous silicon flat panel x-ray detectors
    • 采样方法和系统缩短了非晶硅平板x射线探测器的读出时间并减少滞后
    • US07005663B2
    • 2006-02-28
    • US10646107
    • 2003-08-22
    • Manat MaolinbayPaul GranforsRichard AufrichtigRichard Cronce
    • Manat MaolinbayPaul GranforsRichard AufrichtigRichard Cronce
    • G03B42/08G01T1/105
    • G01T1/2928
    • Sampling methods and systems that shorten readout time and reduce lag in amorphous silicon flat panel x-ray detectors are described. Embodiments comprise: (a) activating a reset switch to discharge any residual signal being held in a feedback capacitor; (b) deactivating the reset switch; (c) activating a field effect transistor; (d) sampling an electrical signal from the amorphous silicon flat panel x-ray detector, while the field effect transistor is activated; (e) activating a reset switch, after the electrical signal has been sampled and while the field effect transistor is still activated, to discharge any residual signal being held in the feedback capacitor; (f) deactivating the field effect transistor, while the reset switch is still activated; (g) deactivating the reset switch; and (h) repeating steps (c)–(g) as necessary to obtain a predetermined radiographic image.
    • 描述了缩短非晶硅平板X射线检测器的读出时间和减少滞后的采样方法和系统。 实施例包括:(a)激活复位开关以放电保持在反馈电容器中的任何残留信号; (b)禁用复位开关; (c)激活场效应晶体管; (d)在场效应晶体管被激活的同时,从非晶硅平板x射线检测器取样电信号; (e)在电信号被采样之后并且当场效应晶体管仍被激活时激活复位开关,以放电保持在反馈电容器中的任何残留信号; (f)当复位开关仍然被激活时,去激活场效应晶体管; (g)停用复位开关; 和(h)根据需要重复步骤(c) - (g)以获得预定的放射照相图像。
    • 4. 发明授权
    • Spatially-selective edge enhancement for discrete pixel images
    • 离散像素图像的空间选择性边缘增强
    • US06175658B1
    • 2001-01-16
    • US09113653
    • 1998-07-10
    • Kenneth S. KumpRichard Aufrichtig
    • Kenneth S. KumpRichard Aufrichtig
    • G06K940
    • G06T5/003G06T5/50G06T2207/10116G06T2207/20016G06T2207/20192G06T2207/30004
    • A discrete pixel image is enhanced to bring out particular features of interest such as edges. The enhancement includes decomposition of the original processed image data, enhancement by application of gain images to the decomposed images, and reconstruction of the enhanced image. The decomposition proceeds through a series of low-pass filters to arrive at decomposed images of progressively lower spatial frequencies. These decomposed images are then multiplied by gain images during the enhancement phase. Gain images for at least the higher spatial frequency level images are derived from lower spatial frequency level images. These spatial frequency-based gain images may be based upon operator inputs, including a spatial sensitivity function and an edge enhancement value. Lower spatial frequency level images may be processed by application of predetermined gain values. The reconstruction sequence recombines the decomposed images after application of the gains to arrive at an enhanced image of the same dimensions as the original image.
    • 离散像素图像被增强以产生诸如边缘的特定的感兴趣的特征。 该增强包括原始处理的图像数据的分解,通过对分解的图像应用增益图像的增强以及增强图像的重建。 分解通过一系列低通滤波器进行,以得到逐渐降低的空间频率的分解图像。 然后在增强阶段将这些分解图像乘以增益图像。 至少较高的空间频率水平图像的增益图像从较低的空间频率水平图像导出。 这些基于空间频率的增益图像可以基于操作者输入,包括空间灵敏度函数和边缘增强值。 可以通过应用预定增益值来处理较低空间频率电平图像。 重建序列在施加增益之后重组分解的图像,以获得与原始图像相同尺寸的增强图像。
    • 5. 发明授权
    • Radiographic testing system with learning-based performance prediction
    • 射线检测系统与基于学习的性能预测
    • US6151383A
    • 2000-11-21
    • US224242
    • 1998-12-30
    • Ping XueRichard AufrichtigMichael Andrew Juhl
    • Ping XueRichard AufrichtigMichael Andrew Juhl
    • H05G1/26A61B6/00H05G1/44
    • A61B6/542A61B6/00H05G1/44
    • Disclosed herein is a radiographic imaging system which performs system performance monitoring by (1) using automatic exposure control (AEC) components to predict the average image gray level; (2) obtaining measured average image gray levels from the portions of the X-ray detector situated in the X-ray shadow of the AEC components; and then (3) comparing the predicted and measured values. The predicted values are determined by use of a prediction model which is modified by a learning system over successive exposures to provide more accurate predictions. After the learning system has sufficiently developed the prediction model, the error between the predicted and measured gray level values may be monitored in later exposures and an error routine can be activated if the error exceeds a predetermined threshold. In this case, the error may indicate that system components in the imaging chain (e.g., the detector or AEC components) require maintenance.
    • 本文公开了一种射线成像系统,其通过(1)使用自动曝光控制(AEC)分量来预测平均图像灰度级来执行系统性能监视; (2)从位于AEC组件的X射线阴影中的X射线检测器的部分获得测量的平均图像灰度; 然后(3)比较预测值和测量值。 预测值通过使用由学习系统对连续曝光进行修改以提供更准确的预测的预测模型来确定。 在学习系统充分发展了预测模型之后,可以在稍后的曝光中监视预测和测量的灰度值之间的误差,并且如果误差超过预定阈值,则可以激活错误例程。 在这种情况下,错误可能表明成像链中的系统组件(例如,检测器或AEC组件)需要维护。