Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

实时分子成像技术可指导治愈性癌症手术，对于确保清除所有肿瘤细胞至关重要。然而，显微肿瘤灶的可视化仍然具有挑战性。成像仪器和分子标记剂的广泛差异要求使用通用指标来传达系统识别肿瘤细胞的能力。由少量肿瘤细胞组成的微观疾病具有与背景同等的信号，因此在这种关键情况下不适用于信号（或肿瘤）与背景之比。因此，需要一种具有以下能力的度量标准：减去背景，相对于不确定性或噪声源评估信号本身。在这里，我们介绍了信噪比（SNR），以表征成像系统的最终灵敏度并优化像素大小等因素。背景（噪声）的变化归因于电子源，光源和空间源（肿瘤标记物表达，荧光团结合和扩散的异质性）。在这里，我们研究了这些噪声源的影响以及限制其对SNR影响的方法。我们使用经验性肿瘤和噪声测量程序性地生成肿瘤图像，并对微观疾病成像进行蒙特卡洛模拟，以优化诸如像素大小之类的参数。我们研究了这些噪声源的影响以及限制其对SNR影响的方法。我们使用经验性肿瘤和噪声测量程序性地生成肿瘤图像，并对微观疾病成像进行蒙特卡洛模拟，以优化诸如像素大小之类的参数。我们研究了这些噪声源的影响以及限制其对SNR影响的方法。我们使用经验性肿瘤和噪声测量程序性地生成肿瘤图像，并对微观疾病成像进行蒙特卡洛模拟，以优化诸如像素大小之类的参数。