Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality
Application of the generalized contrast-to-noise ratio to assess photoacoustic image quality
复制标题
DOI：
10.1364/boe.391026
复制
发表时间：
2020-07-01
影响因子：
3.4
通讯作者：
Bell, Muyinatu A. Lediju
中科院分区：
文献类型：
--
作者：
Kempski, Kelley M.;Graham, Michelle T.;Bell, Muyinatu A. Lediju
广义对比度噪声比（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。虽然gCNR最初是用超声图像证明的，但该指标理论上适用于多种类型的医学图像。在本文中，gCNR的光声图像的适用性进行了研究。针对由基于振幅的（即，延迟求和）和基于相干性（即，短滞后空间相干）波束形成器。将这些gCNR测量结果与三种更传统的图像质量度量（即，对比度、对比噪声比和信噪比）应用于相同的数据集。定性目标可见性的增加通常与gCNR增加相对应。此外，gCNR幅度与光声信号与其背景的可分离性更直接相关，其随着有限带宽伪影的存在和信道噪声水平的增加而劣化。在高gCNR值（即，0.95-1），对比度、对比度噪声比和信噪比的变化分别高达23.7-56.2 dB、2.0-3.4和26.5-7.6x10（20）。因此，当目标完全可检测时，这些传统度量可能经历大的变化，并且这些值的额外增加对光声目标可检测性没有影响。此外，gCNR对通过将最小阈值应用于图像幅度而引入的传统度量的变化具有鲁棒性。与其他光声图像质量度量相结合并且具有0到1的限定范围，gCNR具有提供额外洞察的有希望的潜力，特别是当设计新的波束形成器和图像形成技术时以及当报告定量性能而没有机会定性地评估对应图像（例如，在纯文本摘要中）。(C)根据OSA开放获取出版协议的条款，2020年美国光学学会
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 26.5-7.6x10(20), respectively, for simulated, experimental phantom, and in vivo 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). (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement