首页> 外文会议>IASTED international conference on biomedical engineering;BioMED 2010 >DIFFERENTIAL PROCESSING OF STRUCTURED LIGHT PROJECTIONS FOR DYNAMIC OPTICAL BODY SURFACE SENSING DURING RADIATION THERAPY
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DIFFERENTIAL PROCESSING OF STRUCTURED LIGHT PROJECTIONS FOR DYNAMIC OPTICAL BODY SURFACE SENSING DURING RADIATION THERAPY

机译:辐射治疗过程中动态光学体表面传感的结构化光投射的微分处理

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Complex radiotherapy techniques now rely on the positional integrity of the patient. Limited radiological image guidance for internal targeting is now being joined by optical surface sensing, which can measure the body surface throughout treatment. Fourier profilometry has dynamic surface reconstruction capability and provides a dense array of measured points without interpolation. In common with other surface sensing methods the effects of background illumination and skin texture need to be removed prior to surfaced reconstruction. Direct background measurement and subtraction is ideal for this, in a static environment. However, motion is challenging. This paper compares background subtraction and a new partial differential approach to processing structured light images for Fourier profilometry. Results are presented for an anthropomorphic test phantom and patient undergoing treatment. It is shown that in the static test case the differential approach produces surfaces comparable to the ideal case of background subtraction to better than 0.5mm with a reproducibility of 0.1mm under clinical conditions. Patient results show that the differential approach produces body reconstructions without motion artifacts in the presence of free breathing for data gathered at 25Hz, whereas background subtraction fails.
机译:现在,复杂的放射治疗技术依赖于患者的位置完整性。现在通过光学表面感应加入了用于内部目标的有限放射图像引导,该感应可以在整个治疗过程中测量身体表面。傅里叶轮廓仪具有动态表面重建功能,无需插值即可提供密集的测量点阵列。与其他表面感应方法一样,在表面重建之前需要消除背景照明和皮肤纹理的影响。在静态环境中,直接进行本底测量和相减是理想的选择。但是,运动具有挑战性。本文比较了背景减法和一种新的偏微分方法来处理傅里叶轮廓分析的结构化光图像。给出了拟人化测试体模和正在接受治疗的患者的结果。结果表明,在静态测试情况下,差分方法产生的表面可与理想的背景扣除情况相媲美,且表面质量优于0.5mm,在临床条件下的可重复性为0.1mm。病人的结果表明,对于以25Hz频率采集的数据,在自由呼吸的情况下,差分方法可产生没有运动伪影的身体重建,而背景减法则失败。

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