Bullseye Plot Generation from Cardiac MR Imaging
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Solution Overview
Problem
Current methods for generating bullseye plots from cardiac magnetic resonance imaging (CMRI) data face challenges in accurately determining myocardial segments and reconstructing anatomically realistic plots, which is crucial for clinical decision-making.
Innovation Solution
A system utilizing processors to obtain MR slices from CMRI scans, identify landmark points, and employ artificial neural networks for segmenting the myocardium and determining segment locations, thereby generating accurate bullseye plots by arranging slices sequentially based on distances to the apex and using metadata for orientation correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate bullseye plots from CMRI data, then the process is simpler, but the accuracy of myocardial segment determination and anatomical realism deteriorate
Solution Approach 1:
The patent segments the complex task of bullseye plot generation into distinct modules: CMRI data acquisition, landmark point detection, myocardial segmentation, slice arrangement, and plot generation. Each module handles a specific aspect of the process, improving overall accuracy while making the complexity manageable through modular design.
Solution Approach 2:
The patent introduces intermediary elements such as landmark points (apex, base, septum) and transformation matrices that mediate between the raw CMRI data and the final bullseye plot. These intermediaries enable accurate anatomical correspondence without requiring direct complex mapping.
2Reliability
If MR slices are arranged without considering anatomical orientation, then the process is faster, but the anatomical realism and reliability of the bullseye plot deteriorate
Solution Approach 1:
The patent performs preliminary actions by detecting landmark points and determining anatomical orientation before arranging the MR slices. This preliminary setup ensures that subsequent slice arrangement follows correct anatomical sequences, improving reliability without significant time penalty.
Solution Approach 2:
The patent replaces manual or simple mechanical slice arrangement with an automated system that uses transformation matrices and coordinate systems to mathematically determine the correct anatomical sequence of slices, ensuring accuracy while reducing manual intervention time.
3Measurement precision
If standard segmentation methods are used without anatomical reference, then the segmentation process is simpler, but the accuracy of myocardial segment identification deteriorates
Solution Approach 1:
The patent applies local quality by using different segmentation strategies for different regions of the myocardium based on their anatomical characteristics. Landmark points define local reference frames that guide segmentation in specific regions, improving accuracy while managing complexity through localized approaches.
Solution Approach 2:
The patent changes parameters such as transformation matrices and coordinate systems based on detected landmark positions. These parameter adjustments enable accurate segment identification by adapting the segmentation criteria to the specific anatomical configuration of each patient's heart.
Data Source
AI summary
A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.