3D Surface Map Guidance for Medical Imaging Positioning
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Solution Overview
Problem
Current medical imaging systems face challenges in accurately positioning patients and imaging devices, particularly for unskilled operators, leading to inefficient and less accurate imaging procedures due to the difficulty in interpreting and adjusting ultrasound or X-ray images to capture desired anatomical features.
Innovation Solution
A guidance system that uses 3D data from patient sensors to generate a 3D surface map, patient model, and determine desired positions for imaging hardware, providing real-time feedback to operators to align anatomical features, allowing for automated or assisted positioning of patients and imaging devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual positioning methods are used where operators adjust imaging devices by viewing displayed images, then the system requires less complex hardware, but positioning accuracy decreases and time consumption increases
Solution Approach 1:
The patent replaces manual mechanical positioning with an automated computer vision system that uses machine learning algorithms to detect anatomical landmarks and calculate optimal imaging positions. The system substitutes operator judgment with algorithmic processing of 3D surface maps and 2D image analysis, achieving higher precision without requiring complex mechanical positioning hardware.
Solution Approach 2:
The system creates a digital 3D surface map copy of the patient's body surface and virtual anatomical models that replicate real anatomy. These digital copies are used for positioning calculations without requiring physical markers or complex mechanical guides on the patient, simplifying the physical system while improving accuracy through sophisticated digital modeling.
2Device complexity
If manual positioning methods are used where operators adjust imaging devices by viewing displayed images, then the system structure remains simple, but the time required for positioning increases
Solution Approach 1:
The system performs preliminary 3D surface scanning and anatomical landmark detection before the actual imaging procedure. By pre-processing the patient's body surface data and pre-calculating optimal positions using machine learning models, the system eliminates time-consuming trial-and-error adjustments during the imaging procedure itself, reducing total positioning time while keeping the imaging hardware simple.
Solution Approach 2:
The patent replaces time-consuming manual visual assessment with automated computer vision analysis that processes 3D surface maps and 2D images simultaneously. The machine learning algorithms rapidly identify anatomical landmarks and compute optimal positions in seconds, substituting slow manual operator decision-making with fast automated computational processes without requiring complex mechanical positioning devices.
3Ease of operation
If unskilled operators perform positioning using displayed images, then the system requires less specialized training, but positioning accuracy deteriorates
Solution Approach 1:
The system performs self-positioning by automatically detecting anatomical landmarks and calculating optimal imaging positions without requiring operator expertise. The machine learning model independently analyzes 3D surface maps and 2D images to determine correct probe placement and imaging parameters, enabling unskilled operators to achieve expert-level positioning accuracy through the system's autonomous intelligence.
Solution Approach 2:
The patent substitutes human operator expertise with machine learning-based computer vision algorithms. The system uses trained neural networks to recognize anatomical features and determine optimal positions, replacing the need for skilled human interpretation of images. This allows unskilled operators to achieve high positioning accuracy by relying on the automated intelligent system rather than their own judgment.
Data Source
AI summary
A method includes generating a three-dimensional (3D) surface map associated with a patient from a patient sensor, generating a 3D patient space from the 3D surface map associated with the patient, determining a current pose associated with the patient based on the 3D surface map associated with the patient, comparing the current pose with a desired pose associated with the patient with respect to an imaging system, determining a recommended movement based on the comparison between the current pose and the desired pose, and providing an indication of the recommended movement. The desired pose facilitates imaging of an anatomical feature of the patient by the imaging system and the recommended movement may reposition the patient in the desired pose.