首页> 外文会议>Conference on Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display Feb 16-18, 2003 San Diego, California, USA >Automatic detection, with confidence, of implanted radiographic seeds at megavoltage energies using an amorphous Silicon imager
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Automatic detection, with confidence, of implanted radiographic seeds at megavoltage energies using an amorphous Silicon imager

机译:使用非晶硅成像仪可自信地自动检测兆伏能量下的放射照相种子

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The premise of image guided radiotherapy is the use of imaging to target the delivery of radiotherapy with high precision. Despite the high resolution of amorphous silicon flat panel imagers the detection of small implanted radiographic gold markers (length: 5mm, diameter: 0.8mm), visualised on portal images with low SNR and the inherent low contrast of mega-voltage photons, remains a significant, safety critical challenge. Convolution/correlation and sum of the squares of the difference (SSD) detection algorithms make use of marker templates to detect radiographic markers. However, direct convolution is not specific enough and SSD techniques fail in low SNR conditions. This report defines a robust SSD measure operating on a model template-to-clinical convolution image and a semi-empirical template self-convolution image, which is used to assign an objective measure of confidence to individual markers and unambiguously determine the separation of true and false detection distributions. The algorithm was tested on 9 clinical pelvic images produced by placing a template with 14 randomly arranged gold markers on patients during portal imaging. Using 95% confidence limits in a localised regional search for each of the 14 seeds, the number of correct detections averaged at 13, while the average number of false detections was less than 1.
机译:图像引导放射治疗的前提是使用成像来精确定位放射治疗的目标。尽管非晶硅平板成像仪具有高分辨率,但是在低信噪比和固有的低电压兆伏光子反差的门户图像上可视化小型植入放射线金标记(长度:5mm,直径:0.8mm)仍然是重要的。 ,对安全至关重要的挑战。卷积/相关性和差异平方和(SSD)检测算法利用标记模板来检测射线照相标记。但是,直接卷积不够具体,SSD技术在低SNR条件下会失败。该报告定义了一种稳健的SSD度量,该度量适用于从模型模板到临床的卷积图像和半经验模板自卷积图像,用于将客观的置信度度量值分配给各个标记,并明确确定真实值和真实值的分离错误的检测分布。通过在门静脉成像过程中将带有14个随机排列的金标记的模板放置在患者身上而产生的9张临床骨盆图像上对该算法进行了测试。使用14%种子中的每一个的局部区域搜索使用95%的置信度限制,正确检测的次数平均为13,而错误检测的平均次数小于1。

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