USING A STANDARD CLINICAL SLIT LAMP Jeong Oen Lee1,2*, Haeri Park2, Oliver Chen1, Ashwin Balakrishna1, Juan Du3, David W. Sretavan3,
and Hyuck Choo1,2 1Department of Electrical Engineering, 2Department of Medical Engineering, California Institute of
Technology, Pasadena, USA 3Department of Ophthalmology, University of Californian San Francisco, San Francisco, USA
ABSTRACT
Achieving a practical readout distance for implantable intraocular pressure (IOP) sensors is an essential step toward commercialization yet has remained as a major challenge. Using the Zeiss SL-30 slit lamp -- a standard ophthalmic scope widely used by clinicians, we have demonstrated an optical readout distance of 12 cm from a micromachined IOP sensor implanted in an ex-vivo rabbit eye. We show that we have achieved this readout distance by (1) redesigning the sensing area of the IOP sensor and its fabrication steps to significantly improve the signal-to-noise ratio; and (2) incorporating a novel robust detection algorithm, which includes a much-improved opto-mechanical model, that allows us to remove the background noise and instantaneously map the sensor’s optical signal to the corresponding IOP value. A significant increase in readout distance accomplished using a well established ophthalmic clinical scope makes our IOP system a more clinically viable choice. INTRODUCTION
Glaucoma is a leading cause of irreversible blindness with 60 million cases worldwide. An elevated IOP level is identified as a major risk factor, and all the glaucoma therapies are aimed at lowering the IOP level [1]. Because accurate, reliable, and most of all continuous monitoring of IOP in glaucoma patients has proven to be very crucial in large scale studies recently sponsored by National Institute of Health, the use of implantable IOP sensors especially at home has been highly desired for optimal management of the disease.
Numerous efforts have been made to address this need and mostly based on inductively or capacitively sensing micro-electro mechanical systems (MEMS). However, previously reported IOP sensors suffer from a major technical challenge: there is a trade off between the sensor size and readout distance [2,3]. In order to improve the sensor-readout distance, the sensor had to become large, but for a minimally invasive insertion procedure, the sensor must remain small, less than 2 mm. In addition, microwaves exhibit poor penetration depth into tissue (cornea and anterior chamber) and microwave-based devices suffer from the quality factor degradation, which further reduces the signal-to-noise ratio (SNR) and the sensing distance. As a result, even if the size of the sensors reaches 11 mm, which is the maximum size that can be accommodated in the anterior chamber of a human eye, the readout distance remains limited to 0.6-3 cm. At this large size, it is difficult to implant sensors using a minimally invasive procedure, and one must perform a major surgery
to insert the sensor [2]. In addition, many patients will find it difficult to accept such a large sensor in their eyes due to cosmetic and psychological reasons.
To overcome this challenge, we have demonstrated a sub-1mm nanophotonic IOP-sensing implant that delivers IOP readings when interrogated with invisible broadband light in the near-infrared range. The sensor has a hermetically sealed, circular-disk optical cavity formed by two surfaces, one rigid surface and the other flexible membrane. And, the resonance of the cavity changes as a function of the ambient pressure, which can be detected in light reflected from the sensor. We have fabricated the first generation sensor using Parylene membranes and successfully demonstrated our sensing approach at a readout distance of 7 mm [4].
Here, we report our continuing effort on improving the readout distance of our IOP-sensing implant. We have achieved the readout distance of 12 cm using the Zeiss SL-30 slim lamp integrated with our optical detection system. To increase the sensor’s readout distance while maintaining its sub-1mm size, we have (1) quadrupled and optimized the active sensing area of the sensor to enhance the signal-to-noise ratio of the optical resonance and also to better match the slit lamp’s interrogating beam; (2) set the cavity gap between the surfaces to 5~10 μm to track multiple spectral features for improved accuracy; (3) avoided using Parylene membrane and chose a mechanically more robust silicon-nitride membrane that improves optical performance; (4) suppressed the background noises by forming rough silicon surface in the surrounding area as an anti-reflection (AR) structure; and (5) incorporated a novel extrema-matching algorithm with a much improved opto-mechanical model to read out IOP values more accurately. We first tested our IOP-sensing system with the improved hardware and software and verified its highly accurate IOP reading in tabletop ex-vivo rabbit eye measurements. Next, we placed the ex-vivo rabbit eye in the eye socket of a Styrofoam dummy head and emulated the clinical optical readout of the sensor using the Zeiss ophthalmic microscope. DEVICE OPTIMIZATION
Figure 1 shows the conceptual schematic of optomechanical cavity. In a simplified configuration, two optical surfaces form an optical cavity, and one of the surfaces moves when the ambient pressure changes. For a given cavity length, light components of certain wavelengths thrive and “resonate,” and, these components are not present in the reflection spectrum. Since the cavity gap varies as a function of ambient pressure – in this case,
IOP -- and the cavity resonance and the reflection spectrum change accordingly, we can accurately back-calculate the corresponding IOP from the reflection spectrum.
Figure 1. Conceptual schematic of optomechanical cavit: The reduction in length of the optical cavity (∆L1 → ∆L2) results in the spectrum shift as pressure increases. SENSOR IMPROVEMENT – HARDWARE
The proposed IOP sensor is fabricated using standard micro- and nanofabrication techniques. Two micro-discs are individually fabricated on separate silicon (Si) wafers as shown in Figure 2 and assembled hermetically using a medical grade epoxy under a microscope to form a pressure-sensitive optical cavity. More fabrication details can be found in our previous literature, and we will highlight only the improvement in this manuscript.
To improve the performance of the sensor, we increased the size of 100nm-thick gold nanodot arrays from 50×50-µm2 to 200×200-µm2, increasing the effective sensing area by a factor of 16 (Figure 2 and 3). The diameter of the individual dot and the dot-to-dot pitch were 600 and 1000 nm, respectively. To accommodate the larger sensing area, we also increased the size of the 0.4µm-thick silicon nitride membrane to 600 µm on one of the wafers.
Next we increased the cavity gap from 2~3 to 5~10 µm to generate multiple resonance peaks in the returning spectra. This was accomplished by performing a deep reactive ion etching (DRIE) and creating a 7-µm-deep cavity chamber on the other Si wafer. The DRIE was used one more time to create a 100-µm-deep trench (Figure 2) to form an air chamber and improve the sensor linearity. In addition, we used a modified DRIE process to grow silicon grass and create black Si in the areas surrounding the active sensing area. The black Si appears as a black rings surrounding the active area and can be easily seen in Figure 3. It serves as an AR coating and significantly reduces the background bias when making measurements.
Figure 2. Cross-sectional view of before(left) and after(right) the assembly process
Figure 3. Scanning Electron Microscopy (SEM) images of fabricated top and bottom pieces (left top and left bottom respectively) and optical microscopy image after device assembly (right) SENSOR PRE-CHARACTERIZATION
We first tested the assembled device inside an artificial pressure chamber integrated with a hydrostatic pressure controller and a digital gauge (DPGWB-04 by Dwyer Instruments with an accuracy of ±0.5 %). The setup allows us to control the chamber pressure between 0 and 30 mmHg in the resolution of 0.1 mmHg. We acquired the reflected spectra from the sensor using a broadband light source (OSL1 High-Intensity Fiber Light Source by Thorlabs) and a high-sensitivity VIS-NIR spectrometer (Maya 2000 Pro by Ocean Optics with the spectral range of 780nm-1200nm and resolution of 0.22 nm). SENSOR IMPROVEMENT - SOFTWARE
Our software algorithm is composed of four stages: (1) denoising and low pass filtering, (2) spectral feature recognition, (3) fitting the best theoretical spectra for each experimental spectrum, and (4) an air gap and sensor pressure calculation based on best-fit associating with the optomechanical model (OMM). We first pre-process the measured spectra from the pressure chamber test through a denoising filter, and the local extrema with sufficient prominence emerged.
Figure 4. As pressure increases (top to bottom) in a water chamber, the optical spectrum shifts leftward. Peaks (blue) and valleys (red) of experimental spectra track those of theoretical spectra fairly closely.
Next we automatically extracted the locations of the
peaks and the valleys (or the local extrema) using our
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newly developed Extrema-Matching Technique (Figure 4). One can observe in Figure 5 the highly linear optomechanical behavior between the locations of the extrema and the increasing pressure.
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Figure 5. Tracking each set of peaks/valleys with respect to increasing pressure shows a nearly linear shift in wavelength of prominent features.
To quickly map the IOP levels from the reflection spectra, we apply the Extrema-Matching technique combined with optomechanical modeling (OMM) [5]. In our OMM, a mechanics model predicts the deformation of the sensor membrane with increasing pressure while the resonance model generates the reflection spectrum as a function of deformation. Using the OMM, we can predict the corresponding air gap for each measured spectrum as shown in Figure 6. A correlation between the experimental and simulated sensor air gap was found to be -0.9973 (p<0.01). Finally, based on the mechanics model, we map the air gap to the pressure. This approach correctly identifies IOP in 95.5% of measurements within a ±2mmHg error. Figure 7 shows excellent matching between the measurements from our sensor and the reading from the integrated pressure gauge.
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Figure 6. OMM predicted air gap values for given pressures plotted against air gap values extracted using extrema-matching algorithm in a water pressure chamber. r = -0.9973 with p<0.01. TABLE-TOP EX VIVO MEASUREMENT
For the table-top ex-vivo testing, the fabricated sensor
was embedded onto a flexible PVC strip and inserted into an ex-vivo rabbit eye through an incision of 2 mm. The PVC strip stabilized the sensor position (Figure 8), and the pressure inside the rabbit eye was controlled through a 18-gauge needle connected to a hydrostatic pressure controller with ±0.1mmHg accuracy and the pressure gauge. Our sensor successfully read out IOPs between 3mmHg to 40mmHg and showed excellent peak and valley location agreement between bench testing and the ex-vivo testing. The estimated pressure in ex-vivo testing is plotted as a function of the pressure reading from the integrated gauge in Figure 10.
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Figure 7. Algorithm estimated pressure plotted against measured pressure for water chamber data.
Figure 8. Ex-vivo test platform (a) micro IOP sensor on a PVC strip (b) implanted device, top view (c) side view EX VIVO MEASUREMENT USING A SLIT-LAMP AT 12 CM DISTANCE
A slit lamp is a clinical microscope that is the most widely used ophthalmic instrument for comprehensive eye examinations. Being able to use the slit lamp for IOP readout will make our technology more accessible to clinicians. The optical configuration of the remote sensing setup is shown in Figure 10. An optical fiber and CCD camera were connected to the Zeiss slit-lamp using custom-built aluminum adaptors (Figure 11). The slit lamp’s built-in light bulb was used as a light source. The reflection spectra from the sensor is collected through objective lens (L1), magnification lens (L2) and beam
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splitter and relayed to the spectrometer. To emulate an eye examination on human patients, we implanted the fabricated IOP sensor in a fresh ex-vivo rabbit eye and placed the eye inside the eye socket of a Styrofoam dummy head as shown in Figure 11. Robust optical resonance was captured remotely over a 12-cm distance from our IOP sensor implanted in the anterior chamber of an ex-vivo rabbit eye, and it is shown in Figure 12 along with the table-top microscope measurement made at 2 cm. They match very closely.
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Figure 9. Ex-vivo calculated pressure plotted against measured pressure.
Figure 10. Schematic of remote monitoring of IOP using white-light source and implanted micro-sensor
Figure 11. optical detector system integrated onto a slit-lamp (Zeiss SL-30)
CONCLUSION Using the Zeiss SL-30 slim lamp integrated with our
optical detection system, we have achieved the readout distance of 12 cm from a micromachined IOP sensor implanted in an ex-vivo rabbit eye. To maximize the sensor’s readout distance while maintaining its sub-1mm size, we have optimized the optical and mechanical designs and developed automated algorithms for more accurate reading of IOP estimation. We verified the performance of the system in a tabletop testing as well as in ex-vivo experiments. This improvement – accomplishing accurate IOP measurements at a significantly increased readout distance using a well established ophthalmic clinical scope -- makes our IOP system a more clinically viable choice.
Figure 12. Black-line indicates reference measurement from typical microscope-based spectrometer (Mitutotyo 20, f=2cm) and red-line indicates the slit-lamp measurement (Zeiss SL-30, f=12cm).
ACKNOWLEDGEMENTS
The project has been funded by the Caltech CI2 program, Powell Foundation, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A6A3A03026384) and BWF.
REFERENCES [1] www.glaucoma.org (Glaucoma Research Foundation) [2] Katuri, Kalyan C., Sanjay Asrani, and Melur K.
[3] Chen, Po-Jui, et al. "Microfabricated implantable parylene-based wireless passive intraocular pressure sensors." J. Micromech. Syst. vol 17. pp.1342-1351, 2008.
[4] Lee, J.O., et al., “Nanoarray-enhanced Implantable Intraocular Pressure Sensor with Remote Optical Readout”, Hilton Head 2014 Technical Digest (2014).
[5] Eaton, W. P., Bitsie, F., Smith, J. H., & Plummer, D. W. “A new analytical solution for diaphragm deflection and its application to a surface micromachined Pressure sensor”, International Conference on Modeling and Simulation, MSM. 1999.
