Normalized Standard Deviation Dosimetry for Laser Treatment
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Solution Overview
Problem
Current dosimetry monitoring for laser treatments, particularly in eye diseases, faces challenges due to complex response signals with substantial background noise, making it difficult to accurately interpret the completion of laser treatment using acoustic or optical detection methods.
Innovation Solution
The implementation of normalized standard deviation transition-based dosimetry monitoring, which involves receiving response signals from laser pulses, calculating standard deviation, and deriving a normalized standard deviation to estimate the number of remaining pulses needed for treatment completion, allowing for automated control of the laser source.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic or optical detection methods are used to monitor laser treatment, then real-time dosimetry can be performed, but the response signals contain substantial background noise making interpretation difficult
Solution Approach 1:
The patent transforms the raw response signals into a different parameter domain by calculating the first derivative of the signal. This parameter transformation converts the difficult-to-interpret noisy signals into derivative signals that clearly show treatment completion through distinct transitions, thereby resolving the contradiction between measurement precision and interpretation difficulty
Solution Approach 2:
The patent introduces an intermediary processing step that computes the first derivative of the response signal. This intermediary transformation acts as a mediator between the noisy raw signal and the final interpretation, filtering out noise while preserving the essential treatment completion information through derivative transitions
2Productivity
If laser treatment is performed without robust completion metrics, then treatment can be delivered, but premature cessation or over-treatment may occur
Solution Approach 1:
The patent implements a feedback mechanism where the first derivative of the response signal is continuously monitored during laser treatment. When the derivative signal shows a specific transition pattern indicating treatment completion, the system provides feedback to stop further treatment, thereby preventing both premature cessation and over-treatment while maintaining high reliability
Solution Approach 2:
The patent performs preliminary analysis of the response signal characteristics by calculating its first derivative before making treatment decisions. This preliminary transformation establishes a clear criterion for treatment completion based on derivative transitions, enabling reliable and efficient treatment delivery without trial and error
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 provides a robust metric for determining the completion of laser treatment, reducing the risk of premature cessation or over-treatment by filtering out noise and power level drift, enabling more precise control and improved treatment outcomes.
Implementation Method 1
dosimetry may be performed using acoustic detection or reflectometry
Implementation Method 2
dosimetry may be performed using acoustic detection or reflectometry, where the intensity of reflections of the laser pulse may be measured
Implementation Method 3
Laser beams generate heat at the treatment site
Implementation Method 4
Laser beams generate heat at the treatment site
Implementation Method 5
Laser beams generate heat at the treatment site, which in turn may result in formation of bubbles (through the expansion of fluids transforming into gases)
Implementation Method 6
formation of bubbles (through the expansion of fluids transforming into gases)
Data Source
AI summary
Technologies are generally described for normalized standard deviation transition based dosimetry monitoring for laser treatment. In some examples, a response signal may be generated based on a physical response to a laser pulse detected through acoustic or optical means. Each response signal may be a time series of data with a number of points. Standard deviation may be determined for each response signal and normalized using a mean or comparable normalization factor. Thus, a robust distribution may be computed from the response to each laser pulse. A change in the normalized standard deviation from each single pulse's time domain response data may be used to determine how many laser pulses remain before completion of the treatment (similar to event onset response). Thus, laser treatment may be continued based on an estimation of remaining pulses for completion or ceased if completion is reached.