Zhou Zhuhuang, Huang Chih-Chung, Shung K Kirk, Tsui Po-Hsiang, Fang Jui, Ma Hsiang-Yang, Wu Shuicai, Lin Chung-Chih
Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
PLoS One. 2014 Apr 23;9(4):e96195. doi: 10.1371/journal.pone.0096195. eCollection 2014.
Phacoemulsification is a common surgical method for treating advanced cataracts. Determining the optimal phacoemulsification energy depends on the hardness of the lens involved. Previous studies have shown that it is possible to evaluate lens hardness via ultrasound parametric imaging based on statistical models that require data to follow a specific distribution. To make the method more system-adaptive, nonmodel-based imaging approach may be necessary in the visualization of lens hardness. This study investigated the feasibility of applying an information theory derived parameter - Shannon entropy from ultrasound backscatter to quantify lens hardness. To determine the physical significance of entropy, we performed computer simulations to investigate the relationship between the signal-to-noise ratio (SNR) based on the Rayleigh distribution and Shannon entropy. Young's modulus was measured in porcine lenses, in which cataracts had been artificially induced by the immersion in formalin solution in vitro. A 35-MHz ultrasound transducer was used to scan the cataract lenses for entropy imaging. The results showed that the entropy is 4.8 when the backscatter data form a Rayleigh distribution corresponding to an SNR of 1.91. The Young's modulus of the lens increased from approximately 8 to 100 kPa when we increased the immersion time from 40 to 160 min (correlation coefficient r = 0.99). Furthermore, the results indicated that entropy imaging seemed to facilitate visualizing different degrees of lens hardening. The mean entropy value increased from 2.7 to 4.0 as the Young's modulus increased from 8 to 100 kPa (r = 0.85), suggesting that entropy imaging may have greater potential than that of conventional statistical parametric imaging in determining the optimal energy to apply during phacoemulsification.
超声乳化术是治疗晚期白内障的一种常见手术方法。确定最佳超声乳化能量取决于所涉及晶状体的硬度。先前的研究表明,基于需要数据遵循特定分布的统计模型,通过超声参数成像来评估晶状体硬度是可行的。为使该方法更具系统适应性,在晶状体硬度可视化方面可能需要基于非模型的成像方法。本研究探讨了应用从超声背向散射得出的信息论参数——香农熵来量化晶状体硬度的可行性。为确定熵的物理意义,我们进行了计算机模拟,以研究基于瑞利分布的信噪比(SNR)与香农熵之间的关系。在体外将猪晶状体浸泡在福尔马林溶液中人工诱发白内障后,测量其杨氏模量。使用35兆赫的超声换能器对白内障晶状体进行扫描以进行熵成像。结果表明,当背向散射数据形成对应于1.91的信噪比的瑞利分布时,熵为4.8。当我们将浸泡时间从40分钟增加到160分钟时,晶状体的杨氏模量从约8千帕增加到100千帕(相关系数r = 0.99)。此外,结果表明熵成像似乎有助于可视化不同程度的晶状体硬化。随着杨氏模量从8千帕增加到100千帕,平均熵值从2.7增加到4.0(r = 0.85),这表明在确定超声乳化术中应用的最佳能量方面,熵成像可能比传统的统计参数成像具有更大的潜力。