Dynamic MRI Roadmap Using Pilot Tone Data for Organ Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MRI systems face challenges in creating an accurate dynamic roadmap due to continuous movement of organs like the heart, leading to motion artifacts and mismatch between tracked devices and static 3D roadmaps, which limits precision during medical interventions.
Innovation Solution
A method combining pilot tone technique and high-dimensional datasets to create a dynamic roadmap by determining coordinates based on measured pilot tone data, allowing for real-time representation of organ movement states and selecting appropriate images from a multidimensional dataset to align with actual patient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static 3D roadmap is used for medical workflow, then the roadmap can be acquired once before the procedure and used for planning and guidance, but the roadmap shows limited accuracy due to continuous movement of the heart and device mismatch
Solution Approach 1:
The patent transforms the static roadmap into a dynamic system by continuously updating the roadmap based on real-time physiological data. The roadmap is no longer a fixed 3D volume acquired once before the procedure, but a dynamically updated representation that adapts to the moving heart and devices throughout the intervention, thereby maintaining accuracy without requiring complex additional sensing systems.
Solution Approach 2:
The system implements feedback by continuously monitoring physiological signals (ECG, respiratory signals) and using this information to update the roadmap in real-time. The measured physiological data provides feedback about the current state of the heart and patient, which is then used to adjust and refresh the roadmap, ensuring it remains accurate despite organ movement and device repositioning.
2Measurement precision
If the measurement time is extended to reduce noise artifacts, then noise artifacts are minimized, but motion artifacts increase due to patient movement such as breathing and heartbeat
Solution Approach 1:
The patent applies periodic action by synchronizing the imaging and roadmap updates with the periodic physiological cycles of the patient. By timing the acquisitions and updates to the cardiac and respiratory cycles (using ECG and respiratory signals as triggers), the system captures data at optimal moments in each cycle, reducing motion artifacts while maintaining signal quality without requiring excessively long measurement times.
Solution Approach 2:
The system performs preliminary action by acquiring physiological baseline data and preparing the dynamic roadmap framework before the actual intervention begins. The multidimensional dataset is pre-processed and organized according to physiological states, so that during the procedure, only updates are needed rather than complete re-acquisitions, thereby reducing measurement time and motion artifacts.
3Loss of information
If a multidimensional image dataset with state dimensions is used to represent organ movement, then the actual state of the organ can be captured, but computational efforts and robustness become difficult to obtain
Solution Approach 1:
The patent segments the multidimensional dataset by organizing images according to distinct physiological states (cardiac phases, respiratory phases). Instead of processing the entire continuous dataset simultaneously, the system divides it into discrete state-based segments that can be independently processed and efficiently queried, reducing computational burden while preserving all organ state information.
Solution Approach 2:
The system changes parameters by transforming the raw multidimensional data into a simplified coordinate system based on physiological state parameters (e.g., ECG R-peak position, respiratory phase). This parameter transformation reduces the dimensionality and complexity of the data while maintaining the essential information about organ position and state, thereby improving computational efficiency and robustness.
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
Enables the creation of a dynamic roadmap that accurately represents the actual state of organs during MRI procedures, improving the precision of device tracking and visualization without the need for additional sensors, thus enhancing the accuracy of medical interventions.
Implementation Method 1
A multidimensional image-dataset including a plurality of images of a predefined organ combined with a number of state-dimensions characterizing the movement state of a moving organ is provided. Measured pilot tone data is provided from a continuous pilot tone signal acquisition.
Data Source
AI summary
A method for creating a roadmap for a medical workflow includes providing a multidimensional image-dataset including a plurality of images of a predefined organ combined with a number of state-dimensions characterizing a movement state of a moving organ. Measured pilot tone data is provided from a continuous pilot tone signal acquisition. A coordinate is determined for each state-dimension based on the measured pilot tone data, and an image of the multidimensional image-dataset is selected based on the number of determined coordinates of each state dimension.