Optical Probe Low Noise Response Detection for IR Tissue Analysis
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Solution Overview
Problem
Existing optical spectroscopic systems for tissue characterization, such as cancer detection, face challenges with amplitude and temporal variations in tunable quantum cascade laser pulses, leading to noise accumulation and reduced signal-to-noise characteristics.
Innovation Solution
The implementation of embedded software low noise response pulse detection techniques, which compare response signal values during and between illumination pulses, and utilize signal pre-processing methods like amplitude threshold setting and temporal offset adjustment to limit noise accumulation and extract meaningful information from tissue samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tunable quantum cascade laser pulses are used for fast IR spectroscopic tissue analysis, then analysis speed is improved, but amplitude and temporal variations cause noise accumulation and reduced signal-to-noise characteristics
Solution Approach 1:
The system performs preliminary calibration by illuminating a reference sample (e.g., distilled water or air) before actual tissue analysis. This establishes a baseline response that captures the laser's amplitude and temporal characteristics. The calibrated baseline is then used to normalize subsequent tissue measurements, compensating for laser variations and reducing noise accumulation.
Solution Approach 2:
The system continuously monitors the laser pulse characteristics and uses this feedback to adjust measurement parameters in real-time. By comparing reference and sample responses, the system dynamically compensates for amplitude and temporal variations, maintaining optimal signal-to-noise characteristics throughout the analysis process.
2Reliability
If low duty cycle illumination pulses are used to limit noise accumulation, then signal-to-noise characteristic is improved, but measurement time increases
Solution Approach 1:
The system employs periodic illumination pulses with optimized duty cycles, systematically varying the pulse timing and duration across multiple measurement cycles. By strategically positioning measurement windows within the periodic pulse sequence and using accumulation techniques, the system achieves both low noise accumulation and efficient data collection within reduced total measurement time.
3Measurement precision
If signal pre-processing techniques are applied to extract meaningful information, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements self-calibration and self-correction capabilities where the measurement process automatically compensates for its own variations. By using the reference sample response to characterize system behavior and applying this characterization to correct sample measurements, the system achieves high precision through automated algorithms rather than complex hardware interventions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the signal-to-noise characteristic, enabling highly specific and accurate fast IR spectroscopic tissue analysis by reducing noise and accounting for amplitude and temporal variations, thereby improving the accuracy of tissue characterization.
Implementation Method 1
One or more tunable quantum cascade laser (QCLs) can be used to perform the IR illumination of the tissue sample
Implementation Method 2
Infrared (IR) illumination of a tissue sample can be used to perform spectroscopic analysis of the tissue sample
Data Source
AI summary
Signal processing techniques that can include an embedded software low-noise response pulse detection, which can help provide an enhanced signal-to-noise characteristic, such as can help permit highly specific fast IR spectroscopic tissue analysis. Using a difference between (1) response signal values during a first time period duration of a response pulse from a tissue sample illuminated by illumination pulse, and (2) response signal values for a similar first time period duration between response pulses, for a low duty cycle (e.g., less than 50%, 10% or even at about 5% duty cycle) illumination pulse, accumulation of noise in the response signal between electromagnetic illumination pulses can be limited. In particular, the described signal pre-processing techniques can help extract meaningful information for performing spectroscopic analysis and characterization of the tissue sample despite amplitude and temporal variations that can be encountered when using the system.