首页> 外文期刊>Physics in medicine and biology. >A multiresolution image based approach for correction of partial volume effects in emission tomography
【24h】

A multiresolution image based approach for correction of partial volume effects in emission tomography

机译:基于多分辨率图像的放射线断层扫描中部分体积效应的校正方法

获取原文
获取原文并翻译 | 示例
       

摘要

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the 'a trous' algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.
机译:部分体积效应(PVE)是发射体层摄影术中有限的空间分辨率的结果。它们导致大小类似于点扩散功能的组织中的信号丢失,并引起区域之间的活动溢出。尽管可以通过使用在一系列感兴趣区域(ROI)中提供正确的放射性浓度的算法来校正PVE,但迄今为止,很少有人关注由于PVE校正而产生改进的图像的可能性。经PVE校正的图像的潜在优势包括能够准确描绘功能量以及改善肿瘤与背景比的能力,从而分别改善了对治疗研究和诊断检查的响应分析。因此,我们研究的目的是开发一种PVE校正的方法,不仅可以使活动浓度准确恢复,还可以生成PVE校正的图像。在这里定义的多分辨率分析中,高分辨率图像H(MRI或CT)的细节被提取,转换并整合到低分辨率图像L(PET或SPECT)中。通过使用“ trous”算法对H和L图像进行离散小波变换,该算法允许以H和L共有的分辨率轻松获得空间频率(细节,边缘,纹理)。模型然后从H的高频细节中推断出L缺乏的细节。对该过程进行了成功的合成和模拟数据测试,证明了获得准确校正的图像的能力。发现定量PVE校正与被视为参考但仅限于ROI分析的方法具有可比性。在两个临床图像示例中也获得了视觉改善和定量校正,第一个示例是对患有淋巴瘤的患者使用PET / CT组合扫描仪,第二个示例是对癫痫患者使用FDG脑PET和相应的T1加权MRI。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号