3D Tissue Thickness Evaluation Using Segmented Point Mappings

Solution Overview

Problem

Existing methods for evaluating the thickness of an organ's tissue fail to accurately account for heterogeneous cellular compositions, leading to inaccurate thickness estimations, especially in organs with structural changes due to diseases like cardiac conditions where muscular tissue is replaced by adipocytes or calcified structures.

Innovation Solution

A computer-implemented method that involves receiving point mappings of an organ's tissue, segmenting these points into different classes, determining trajectories between inner and outer borders, computing a thickness function based on these classes, and generating a 3D model with adjusted thickness values for each voxel, considering the spatial arrangement and histological diversity of tissues.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If the distance between inner and outer surface is measured to evaluate tissue thickness, then the measurement process is simple, but the thickness estimation becomes inaccurate when cellular composition is heterogeneous

Engineering Contradiction:

Improvemeasurement simplicityVSAvoidthickness estimation accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The tissue is segmented into multiple classes based on cellular composition (muscular tissue, adipocytes, calcified structures). Each point in the mapping is classified into a specific tissue type, allowing the thickness measurement to account for heterogeneous cellular composition rather than treating the tissue as homogeneous.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different weights or quality factors are assigned to different tissue classes along the trajectory. Muscular tissue may be given higher weight while adipocytes and calcified structures receive lower weights. This local differentiation allows the thickness measurement to reflect the actual functional tissue quality rather than just geometric distance.

Inventive Principle:

Principle #3Local quality

2Device complexity

If traditional thickness measurement is used, then the computational complexity is low, but the medical relevance is reduced when histological diversity is not considered

Engineering Contradiction:

Improvecomputational complexityVSAvoidmedical relevance

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The tissue is segmented into multiple classes based on cellular composition (muscular tissue, adipocytes, calcified structures). Each point in the mapping is classified into a specific tissue type, allowing the thickness measurement to account for heterogeneous cellular composition rather than treating the tissue as homogeneous.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different weights or quality factors are assigned to different tissue classes along the trajectory. Muscular tissue may be given higher weight while adipocytes and calcified structures receive lower weights. This local differentiation allows the thickness measurement to reflect the actual functional tissue quality rather than just geometric distance.

Inventive Principle:

Principle #3Local quality

3Measurement precision

If multiple tissue classes are considered in thickness evaluation, then the accuracy of thickness estimation is improved, but the complexity of the evaluation method increases

Engineering Contradiction:

Improvethickness estimation accuracyVSAvoidevaluation method complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The tissue is segmented into multiple classes based on cellular composition (muscular tissue, adipocytes, calcified structures). Each point in the mapping is classified into a specific tissue type, allowing the thickness measurement to account for heterogeneous cellular composition rather than treating the tissue as homogeneous.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different weights or quality factors are assigned to different tissue classes along the trajectory. Muscular tissue may be given higher weight while adipocytes and calcified structures receive lower weights. This local differentiation allows the thickness measurement to reflect the actual functional tissue quality rather than just geometric distance.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20240087153A1Method for the evaluation of the thickness of a tissue of an organ

Publication Date: 2024.03.14 INHEART

AI summary

A computer-implemented method for the evaluation of an adjusted thickness of a tissue of an organ, the method comprising:step (1) of receiving at least one mapping of points (10) representing said tissue, said mapping of points (10) defining an inner border (102) and an outer border (101) of said tissue;step (2) of segmentation of the mapping of points (10) by classification of each point (11) from the mapping of points (10) into a class belonging to a set of different classes;step (3) of determining a plurality of trajectories (109) each joining one point (108) of the inner border (102) to one point (107) of the outer border (101);step (4) of computing for each trajectory (109) the value of a thickness function depends on said trajectory (109) and the classes assigned to the points of the mapping of points (10) through which said trajectory encompasses; andstep (5) of generation of a 3D model (1000) of said tissue from said mapping of points (10), wherein at least to each voxel of said 3D model (1000) corresponding to the inner border (102) and/or to the outer border (101) of said tissue is assigned an adjusted thickness value determined from the value of the thickness function associated to the trajectory encompassing said voxels.