Multiparametric Breast MRI Mapping for Non-Contrast Tumor Detection
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Solution Overview
Problem
Current breast MRI methods are qualitative, leading to high inter- and intra-operator variability, are uncomfortable for patients, and struggle to detect small tumors, especially in dense breasts, without the use of ionizing radiation.
Innovation Solution
A method using quantitative non-contrast MRI scans with various pulse sequences to generate metric maps, such as T1, T2*, PDFF, and ADC, to create composite and heterogeneity maps, identifying contiguous clusters and comparing them to a database for abnormal tissue detection and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative non-contrast MRI scans with multiple pulse sequences are used, then measurement precision and reliability are improved, but device complexity and scanning time increase
Solution Approach 1:
The breast tissue characterization process is segmented into multiple independent quantitative measurements (T1 mapping, T2* mapping, PDFF mapping, ADC mapping), each obtained through specific pulse sequences. This segmentation allows each parameter to be optimized independently while collectively providing comprehensive tissue characterization, resolving the contradiction between precision and complexity by organizing the complex process into manageable segments.
Solution Approach 2:
The patent employs periodic action through multiple distinct pulse sequences acquired in a structured sequence (T1-weighted, T2*-weighted, PDFF-weighted, ADC-weighted sequences). Each pulse sequence periodically targets a specific tissue parameter, allowing comprehensive characterization through repeated measurements with different weighting schemes, thereby improving precision while managing system complexity through systematic periodic acquisition.
2Reliability
If quantitative MRI metric maps and heterogeneity analysis are implemented, then tumor detection capability is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent generates visual metric maps where different tissue properties are represented by distinct color codes or intensity levels. T1, T2*, PDFF, and ADC values are mapped to visual representations that highlight abnormal tissue characteristics. This color-coding system transforms complex quantitative data into intuitive visual patterns, improving tumor detection reliability while reducing the difficulty of interpretation for clinicians.
Solution Approach 2:
The patent transitions from conventional 2D anatomical imaging to 3D quantitative parametric space by generating metric maps that visualize tissue properties in an additional dimension. Heterogeneity maps and composite scores add another layer of dimensional analysis, allowing tumors to be detected through their unique signature in multi-dimensional parameter space rather than relying solely on anatomical appearance, thereby improving detection reliability.
3Object-affected harmful factors
If non-contrast MRI sequences are used, then harmful factors are reduced, but loss of information increases
Solution Approach 1:
The patent employs multiple non-contrast pulse sequences, each designed to be sensitive to different tissue properties (T1 relaxation, T2* relaxation, proton density fat fraction, apparent diffusion coefficient). This multi-functional approach allows a single non-contrast MRI protocol to extract comprehensive tissue characterization information across multiple parameters, matching or exceeding the information obtained from contrast-enhanced sequences while eliminating contrast agent exposure.
Solution Approach 2:
The patent changes the measurement parameters from contrast-dependent signal enhancement to intrinsic tissue property measurements (relaxation times, diffusion coefficients, fat fraction). By shifting to parameter-based quantitative mapping that relies on inherent tissue characteristics rather than exogenous contrast agents, the method eliminates harmful factors while preserving comprehensive tissue information through multiple independent quantitative parameters.
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
Provides standardized, patient-comfortable, and effective breast tissue analysis, reducing variability and improving tumor detection, especially in dense breasts, without the need for contrast agents.
Implementation Method 1
acquiring a plurality of quantitative non-contrast MRI scan images of at least part of the breast area using different MR pulse sequences
Implementation Method 2
determining a plurality of MRI metrics from the acquired quantitative MRI scans; using the determined metrics to generate a plurality of MRI metric maps
Implementation Method 3
calculating at least one of: a breast composite map and a heterogeneity map from the metric maps to show tissue characteristics
Implementation Method 4
The MRI metrics comprise one or more of T1, corrected T1, T2, T2*, PDFF and ADC
Data Source
AI summary
A method and apparatus for analysing MRI data of breast tissue is described. The method comprising the steps of: acquiring a plurality of quantitative non-contrast MRI scan images of at least part of the breast area using different MR pulse sequences for each MRI scan image; determining a plurality of MRI metrics from the acquired quantitative MRI scans; using the determined metrics to generate a plurality of MRI metric maps; calculating at least one of: a breast composite map and a heterogeneity map, from the metric maps to show tissue characteristics of fat and non-fat tissue in the MRI image; determining breast tissue heterogeneity characteristics from at least one of the breast composite map and the heterogeneity map; and identifying clusters of data from at least one of the breast composite map, breast hetereogeneity map and the breast tissue hetereogeneity characteristics.