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一种基于多分辨率图像的发射断层成像中部分容积效应校正方法。

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

作者信息

Boussion N, Hatt M, Lamare F, Bizais Y, Turzo A, Cheze-Le Rest C, Visvikis D

机构信息

INSERM U650, Laboratoire du Traitement de l'Information Médicale (LaTIM), CHU Morvan, Brest, France.

出版信息

Phys Med Biol. 2006 Apr 7;51(7):1857-76. doi: 10.1088/0031-9155/51/7/016. Epub 2006 Mar 21.

Abstract

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 "à 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.

摘要

部分容积效应(PVEs)是发射断层扫描中空间分辨率有限的结果。它们导致与点扩散函数大小相似的组织中信号丢失,并引起区域间的活性溢出。尽管可以通过使用在一系列感兴趣区域(ROIs)中提供正确放射性浓度的算法来校正PVE,但到目前为止,对于因PVE校正而创建改进图像的可能性关注甚少。PVE校正图像的潜在优势包括能够准确描绘功能体积以及提高肿瘤与背景的比率,分别导致在治疗反应研究分析和诊断检查方面的相关改进。因此,我们研究的目的是开发一种PVE校正方法,不仅要能够准确恢复活性浓度,还要生成PVE校正图像。在我们在此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、变换并整合到低分辨率图像L(PET或SPECT)中。通过使用“à trous”算法对H和L图像进行离散小波变换,这使得在H和L共同的分辨率水平上容易获得空间频率(细节、边缘、纹理)。然后推断出一个模型,根据H中的高频细节构建L中缺失的细节。该过程在合成数据和模拟数据上成功进行了测试,证明了获得准确校正图像的能力。发现定量PVE校正与被视为参考但仅限于ROI分析的方法相当。在两个临床图像示例中也获得了视觉上的改善和定量校正,第一个示例使用PET/CT联合扫描仪对一名淋巴瘤患者进行检查,第二个示例使用一名癫痫患者的FDG脑PET和相应的T1加权MRI。

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