Device and method for near real-time prediction of exposure maps for interventional radiology
JP2024541833A5Pending Publication Date: 2025-10-27INST NAT DE LA SANTE & DE LA RECHERCHE MEDICALE (INSERM) +4
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Patent Information
- Application Number
- JP2024522083
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2021-10-22
- Filing Date
- 2022-10-17
- Publication Date
- 2025-10-27
AI Technical Summary
Technical Problem
Current methods for predicting radiation exposure maps during interventional radiology are not sufficiently accurate or real-time, failing to account for patient-specific anatomy and leading to inadequate dose control during procedures.
Method used
A method utilizing a multilayer neural network, trained with a simulation module and patient data, generates real-time exposure maps by correlating radiographic images with acquisition parameters, allowing for dynamic dose control during surgery.
Benefits of technology
The method provides near real-time, accurate exposure maps that enable physicians to adjust radiation doses based on patient-specific anatomy, reducing the risk of deterministic and non-deterministic side effects.
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Abstract
1. A method for acquiring an exposure map of a patient during interventional radiology, comprising: a learning phase consisting of submitting to a neural network (130) a learning set comprising associations between a first input tensor comprising data of a first radiographic image (31) of an intervention area of a patient and first acquisition parameters (32) of an interventional radiology device and labels corresponding to an exposure map (33) acquired by simulation (12) from said first radiographic image (31) and said first acquisition parameters (32); and a prediction phase for a given patient comprising acquiring a stream of second acquisition parameters (32) of said interventional radiology device (21), creating a second input tensor comprising data of a second radiographic image (31) and the second acquisition parameters of said given patient, submitting said second input vector to said neural network (130) and deriving an exposure map prediction (34).
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