Entropic imaging of cataract lens: an in vitro study.
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.
Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
Zhuhuang
Z
Zhou
Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
Chih-Chung
CC
Huang
NIH Resource on Medical Ultrasonic Transducer Technology, Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America.
K Kirk
KK
Shung
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Po-Hsiang
PH
Tsui
Ph.D. Program in Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan.
Jui
J
Fang
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Hsiang-Yang
HY
Ma
Biomedical Engineering Center, College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
Shuicai
S
Wu
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
Chung-Chih
CC
Lin
D000818:Animals; D002386:Cataract / Q000139:chemically induced / Q000000981:diagnostic imaging* / Q000473:pathology; D003198:Computer Simulation; D004195:Disease Models, Animal; D019277:Entropy; D005240:Feasibility Studies; D005557:Formaldehyde; D066298:In Vitro Techniques; D007908:Lens, Crystalline / Q000000981:diagnostic imaging* / Q000473:pathology; D015233:Models, Statistical; D059629:Signal-To-Noise Ratio; D013552:Swine; D014463:Ultrasonography
D005557:Formaldehyde