Department of Nuclear Medicine & PET Research, VU University Medical Center, PO Box 7057, 1007 MB Amsterdam, The Netherlands.
Eur J Nucl Med Mol Imaging. 2011 Dec;38(12):2136-44. doi: 10.1007/s00259-011-1899-5. Epub 2011 Aug 20.
Delineation of tumour boundaries is important for quantification of [(18)F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) studies and for definition of biological target volumes in radiotherapy. Several (semi-)automatic tumour delineation methods have been proposed, but these methods differ substantially in estimating tumour volume and their performance may be affected by imaging parameters. The main purpose of this study was to explore the performance dependence of various (semi-)automatic tumour delineation methods on different imaging parameters, i.e. reconstruction parameters, noise levels and tumour characteristics, and thereby the need for standardization or inter-institute calibration.
Six different types of delineation methods were evaluated by assessing accuracy and precision in estimating tumour volume from simulations and phantom experiments. The evaluated conditions were various tumour sizes, iterative reconstruction algorithm settings and image filtering, tumour to background ratios (TBR), noise levels and region growing initializations.
The accuracy of all automatic delineation methods was influenced when imaging parameters were varied. The performance of all tumour delineation methods depends on variation of TBR, image resolution and image noise level, and to a lesser extent on number of iterations during image reconstruction or the initialization method of the region generation. For sphere sizes larger than 20 mm diameter a contrast-oriented method provided the most accurate results, on average, over all simulated conditions. For threshold-based methods the accuracy of tumour delineation improved after image denoising/filtering.
The accuracy and precision of all studied tumour delineation methods was affected by physiological and imaging parameters. The latter illustrates the need for optimizing imaging parameters and/or for careful calibration and optimization of delineation methods.
肿瘤边界的描绘对于 [(18)F]氟代-2-脱氧-D-葡萄糖（FDG）正电子发射断层扫描（PET）研究的定量以及放射治疗中生物靶区的定义都非常重要。已经提出了几种（半）自动肿瘤描绘方法，但这些方法在估计肿瘤体积方面有很大的差异，并且它们的性能可能受到成像参数的影响。本研究的主要目的是探索各种（半）自动肿瘤描绘方法对不同成像参数（即重建参数、噪声水平和肿瘤特征）的性能依赖性，从而需要标准化或机构间校准。
通过从模拟和体模实验中评估肿瘤体积估计的准确性和精密度，评估了六种不同类型的描绘方法。评估的条件包括各种肿瘤大小、迭代重建算法设置和图像滤波、肿瘤与背景比（TBR）、噪声水平和区域生长初始化。
当成像参数发生变化时，所有自动描绘方法的准确性都受到影响。所有肿瘤描绘方法的性能都取决于 TBR、图像分辨率和图像噪声水平的变化，并且在一定程度上取决于图像重建期间的迭代次数或区域生成的初始化方法。对于直径大于 20 毫米的球体大小，基于对比度的方法在所有模拟条件下平均提供了最准确的结果。对于基于阈值的方法，图像去噪/滤波后，肿瘤描绘的准确性得到了提高。
所有研究的肿瘤描绘方法的准确性和精密度都受到生理和成像参数的影响。后者说明了需要优化成像参数和/或仔细校准和优化描绘方法的必要性。
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