基于图像的输入函数估计的单输入双输出建模。
Single-input-dual-output modeling of image-based input function estimation.
机构信息
Department of Radiology, Washington University School of Medicine, 510 South Kingshighway Boulevard, Campus Box 8225, St. Louis, MO 63110, USA.
出版信息
Mol Imaging Biol. 2010 Jun;12(3):286-94. doi: 10.1007/s11307-009-0273-5. Epub 2009 Dec 1.
Abstract
PURPOSE
Quantification of small-animal positron emission tomography (PET) images necessitates knowledge of the plasma input function (PIF). We propose and validate a simplified hybrid single-input-dual-output (HSIDO) algorithm to estimate the PIF.
PROCEDURES
The HSIDO algorithm integrates the peak of the input function from two region-of-interest time-activity curves with a tail segment expressed by a sum of two exponentials. Partial volume parameters are optimized simultaneously. The algorithm is validated using both simulated and real small-animal PET images. In addition, the algorithm is compared to existing techniques in terms of area under curve (AUC) error, bias, and precision of compartmental model micro-parameters.
RESULTS
In general, the HSIDO method generated PIF with significantly (P < 0.05) less AUC error, lower bias, and improved precision of kinetic estimates in comparison to the reference method.
CONCLUSIONS
HSIDO is an improved modeling-based PIF estimation method. This method can be applied for quantitative analysis of small-animal dynamic PET studies.
摘要
目的
小动物正电子发射断层扫描（PET）图像的定量分析需要了解血浆输入函数（PIF）。我们提出并验证了一种简化的混合单输入双输出（HSIDO）算法来估计 PIF。
过程
HSIDO 算法将两个感兴趣区时间活动曲线的输入函数峰值与由两个指数和表示的尾部段集成在一起。同时优化部分容积参数。该算法使用模拟和真实的小动物 PET 图像进行了验证。此外，该算法在曲线下面积（AUC）误差、偏差和隔室模型微参数精度方面与现有技术进行了比较。
结果
一般来说，与参考方法相比，HSIDO 方法生成的 PIF 的 AUC 误差显著降低（P<0.05），偏差更低，动力学估计的精度更高。
结论
HSIDO 是一种改进的基于模型的 PIF 估计方法。该方法可应用于小动物动态 PET 研究的定量分析。
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