Patient-Specific 3D Organ Modeling for Structural Accuracy
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Solution Overview
Problem
Current organ models lack the nuanced tissue variations and complex internal and external features of real organs, failing to replicate the mechanical and structural accuracy necessary for effective surgical and diagnostic training.
Innovation Solution
A novel process involving MRI data convolution with a second-order ranked tensor matrix encoded with patient-specific physiological information, using additive layer printing to create accurate 3D cardiovascular models that capture cellular structure and mechanical properties, enabling precise reproduction of organ features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional organ model creation methods are used, then production simplicity is maintained, but manufacturing precision and structural accuracy deteriorate
Solution Approach 1:
The patent segments the organ model production process into distinct stages: MRI data acquisition, tensor matrix encoding with physiological parameters, convolution processing, and additive layer printing. This segmentation allows each stage to be optimized independently, achieving high structural accuracy through precise control of printing parameters while managing overall process complexity through systematic organization.
Solution Approach 2:
The patent transitions from conventional 2D imaging representations to 3D physical models with full spatial fidelity. By using additive layer printing to build three-dimensional structures layer by layer, the system reproduces complex internal and external features in all spatial dimensions, achieving manufacturing precision that cannot be obtained through traditional modeling methods.
2Reliability
If simple organ models are produced, then ease of manufacture is maintained, but reliability and training effectiveness deteriorate
Solution Approach 1:
The patent applies local quality by encoding patient-specific physiological information into a second-order ranked tensor matrix that varies spatially throughout the organ model. Different regions of the organ receive customized material properties and structural characteristics based on local tissue variations, ensuring high reliability and training effectiveness while managing production complexity through automated computational processing.
Solution Approach 2:
The patent systematically changes multiple parameters including MRI scanning parameters, tensor matrix encoding parameters, convolution processing parameters, and additive printing parameters. By optimizing these parameters in coordination, the system achieves high reliability for surgical training while maintaining ease of manufacture through automated parameter management and standardized production workflows.
3Manufacturing precision
If detailed physiological data is incorporated, then manufacturing precision improves, but loss of time in data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing MRI data into standardized formats, pre-encoding physiological parameters into the tensor matrix structure, and pre-planning the additive manufacturing paths. These preliminary computations are performed offline before the actual printing process, allowing high manufacturing precision to be achieved while minimizing the time loss during the critical production phase.
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
The solution provides highly accurate, mechanically and spatially precise 3D organ models that enhance surgical and diagnostic training by replicating the original organ's mechanical and structural features, improving training efficacy for medical professionals.
Implementation Method 1
an additive layer printer driven by software instructions generated by the unique software process of the present invention
Data Source
AI summary
The methods, process, and apparatus of the present invention produces a structurally representative organ model using magnetic resonance imaging scan information of a organ and a patient's medical history and physiology. This is accomplished by mathematically convolving the scan information with a second order ranked tensor matrix encoded with the patient's physiological information as it relates to the scanned organ and their medical profile. The convolved scan information and encoded matrix are computer processed to produce a 3D printer driver file which is used to print a structurally representative organ model conforming to the patient's physiology.