Multi-Sensor Radiation Therapy With Motion-Adaptive Fluence
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Solution Overview
Problem
Current radiation therapy systems face challenges in accurately delivering therapeutic radiation to a tumor while minimizing exposure to surrounding healthy tissue, particularly due to tumor motion caused by physiological processes and patient movements.
Innovation Solution
The use of sensor data from target region sensors to calculate shift-invariant firing filters, which are applied to sensor data during radiation delivery sessions to adjust the radiation fluence map in real-time, thereby accounting for patient and tumor motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high levels of radiation are applied to the tumor, then tumoricidal effect is improved, but radiation exposure to surrounding healthy tissue increases
Solution Approach 1:
The patent applies different radiation doses to different spatial locations by defining a motion envelope that identifies the tumor's movement boundaries. The radiation therapy system delivers higher radiation doses within the motion envelope region where the tumor may be located, while reducing doses to surrounding healthy tissue outside this envelope, thereby achieving localized quality differentiation in radiation delivery.
Solution Approach 2:
The patent performs preliminary imaging and motion analysis before radiation delivery to establish the motion envelope. By acquiring images at multiple time points and determining the tumor's motion characteristics in advance, the system pre-calculates the radiation fluence map that accounts for anticipated tumor movement, ensuring accurate radiation delivery without requiring real-time adjustments during treatment.
2Measurement precision
If radiation therapy is delivered based on planning images, then treatment plan accuracy is improved, but tumor location discrepancies occur due to patient movement during treatment session
Solution Approach 1:
The patent implements feedback by acquiring images during the treatment session to monitor actual tumor position and comparing it with the planned position. The system uses this feedback information to update the motion envelope and adjust the radiation fluence map in real-time, ensuring that radiation delivery remains accurate despite patient movement or physiological changes during treatment.
Solution Approach 2:
The patent transitions from static treatment planning based on initial images to dynamic treatment delivery that adapts to changing tumor position. By calculating the motion envelope that encompasses the tumor's movement range and updating the radiation fluence map accordingly, the system dynamically adjusts radiation delivery to maintain accuracy throughout the treatment session.
3Reliability
If motion envelope is defined to account for tumor motion, then radiation delivery to moving tumor is improved, but irradiation of healthy tissue within motion envelope increases
Solution Approach 1:
The patent applies the local quality principle by delivering differentiated radiation doses within the motion envelope based on the probability of tumor presence. The radiation fluence map is calculated to provide higher doses in regions where the tumor is most likely to be located (based on motion analysis) and progressively lower doses toward the boundaries of the motion envelope, thereby minimizing healthy tissue irradiation while ensuring adequate tumor coverage.
4Measurement precision
If image acquisition is performed at the beginning of treatment session, then initial tumor location is determined, but changes in tumor location during treatment session are not captured
Solution Approach 1:
The patent performs preliminary motion analysis by acquiring images at multiple time points before treatment to establish the tumor's motion characteristics and define the motion envelope. This preliminary action captures the range of tumor movement without requiring continuous real-time imaging during treatment, thus determining the updated tumor location while avoiding excessive time loss during the actual radiation delivery session.
Data Source
AI summary
Disclosed herein are methods for radiotherapy treatment planning and delivery that use sensor data from one or more target sensors. One variation of a radiotherapy treatment planning method comprises generating a sensor characterization image based on a sensor characterization probability density function (PDF) of a target sensor and calculating a set of firing filters that may be applied to sensor images generated from sensor data acquired during a radiation-delivery session. Additionally, a variation of a radiotherapy treatment planning method comprises generating multiple sensor characterization images based on multiple sensor characterization PDF of multiple target sensors and calculating multiple sets of firing filters for each of the multiple target sensors. The firing filters may be used with sensor images generated from target sensor data acquired from one or more target sensors during a radiation-delivery session to calculate a radiation fluence for delivering therapeutic radiation to a target region.