Kempski Kelley M, Graham Michelle T, Gubbi Mardava R, Palmer Theron, Lediju Bell Muyinatu A
Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Electrical & Computer Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA.
Biomed Opt Express. 2020 Jun 10;11(7):3684-3698. doi: 10.1364/BOE.391026. eCollection 2020 Jul 1.
The generalized contrast-to-noise ratio (gCNR) is a relatively new image quality metric designed to assess the probability of lesion detectability in ultrasound images. Although gCNR was initially demonstrated with ultrasound images, the metric is theoretically applicable to multiple types of medical images. In this paper, the applicability of gCNR to photoacoustic images is investigated. The gCNR was computed for both simulated and experimental photoacoustic images generated by amplitude-based (i.e., delay-and-sum) and coherence-based (i.e., short-lag spatial coherence) beamformers. These gCNR measurements were compared to three more traditional image quality metrics (i.e., contrast, contrast-to-noise ratio, and signal-to-noise ratio) applied to the same datasets. An increase in qualitative target visibility generally corresponded with increased gCNR. In addition, gCNR magnitude was more directly related to the separability of photoacoustic signals from their background, which degraded with the presence of limited bandwidth artifacts and increased levels of channel noise. At high gCNR values (i.e., 0.95-1), contrast, contrast-to-noise ratio, and signal-to-noise ratio varied by up to 23.7-56.2 dB, 2.0-3.4, and 26.5-7.6×10, respectively, for simulated, experimental phantom, and data. Therefore, these traditional metrics can experience large variations when a target is fully detectable, and additional increases in these values would have no impact on photoacoustic target detectability. In addition, gCNR is robust to changes in traditional metrics introduced by applying a minimum threshold to image amplitudes. In tandem with other photoacoustic image quality metrics and with a defined range of 0 to 1, gCNR has promising potential to provide additional insight, particularly when designing new beamformers and image formation techniques and when reporting quantitative performance without an opportunity to qualitatively assess corresponding images (e.g., in text-only abstracts).
广义对比噪声比（gCNR）是一种相对较新的图像质量指标，旨在评估超声图像中病变可检测性的概率。尽管gCNR最初是在超声图像中得到验证的，但该指标在理论上适用于多种类型的医学图像。本文研究了gCNR在光声图像中的适用性。针对基于幅度（即延迟求和）和基于相干性（即短延迟空间相干）波束形成器生成的模拟和实验光声图像计算了gCNR。将这些gCNR测量结果与应用于相同数据集的另外三个传统图像质量指标（即对比度、对比噪声比和信噪比）进行了比较。定性目标可见性的增加通常与gCNR的增加相对应。此外，gCNR的大小与光声信号与其背景的可分离性更直接相关，这种可分离性会因有限带宽伪像的存在和通道噪声水平的增加而降低。在高gCNR值（即0.95 - 1）时，对于模拟、实验体模和数据，对比度、对比噪声比和信噪比分别变化高达23.7 - 56.2 dB、2.0 - 3.4和26.5 - 7.6×10。因此，当目标完全可检测时，这些传统指标可能会有很大变化，并且这些值的进一步增加对光声目标可检测性没有影响。此外，gCNR对于通过对图像幅度应用最小阈值引入的传统指标变化具有鲁棒性。与其他光声图像质量指标一起，并在0到1的定义范围内，gCNR有潜力提供额外的见解，特别是在设计新的波束形成器和图像形成技术时，以及在没有机会定性评估相应图像（例如在纯文本摘要中）而报告定量性能时。
