Prostate MRI Volume Fraction Maps for Cancer Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques for prostate cancer detection and characterization are limited by low spatial resolution and reliance on phenomenological models, leading to undetected smaller lesions and inadequate characterization of cancer aggressiveness, necessitating histological reference standards.
Innovation Solution
A method involving multi-parametric MRI data acquisition with diffusion weighting and magnetization relaxation parameters to generate volume fraction maps of prostate tissue components, enabling non-invasive detection and grading of cancer by transforming MRI data into prostatic tissue composition maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-parametric MRI data is acquired with multiple diffusion weighting and magnetization relaxation parameters, then tissue composition characterization is improved, but imaging time and data processing complexity increase
Solution Approach 1:
The patent segments the prostate gland into multiple tissue compartments (epithelium, stroma, lumen) and acquires MRI data with multiple diffusion weighting and magnetization relaxation parameters for each compartment. This segmentation allows independent characterization of each tissue type's composition and properties, improving measurement precision while managing complexity through structured analysis of distinct tissue regions
2Productivity
If clinical MRI spatial resolution is used, then scanning time is reduced, but glandular structures cannot be resolved leading to undetected smaller lesions
Solution Approach 1:
The patent changes the MRI acquisition parameters by using multiple diffusion weighting values (b-values) and multiple magnetization relaxation parameters (TE values) to enhance the contrast and detectability of glandular structures. This parameter optimization allows clinical MRI to achieve improved lesion detection capability while maintaining acceptable scanning times through efficient multi-parameter acquisition sequences
3Device complexity
If phenomenological models are used for MRI analysis, then analysis simplicity is maintained, but cancer detection accuracy decreases for smaller lesions
Solution Approach 1:
The patent transitions from single-parameter phenomenological models to multi-dimensional analysis by simultaneously evaluating multiple diffusion weighting parameters and multiple magnetization relaxation parameters. This dimensional expansion enables comprehensive characterization of tissue composition and microstructure, significantly improving cancer detection accuracy for smaller lesions while maintaining practical analysis through integrated multi-parameter modeling
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 non-invasive detection of tumors and grading of cancer without biopsy, providing simultaneous imaging of tissue volume fractions and measuring properties of each compartment, improving the detection and characterization of prostate cancer.
Implementation Method 1
multi-parametric magnetic resonance (MR) imaging data parameterized by a diffusion weighting or perfusion weighting parameter
Implementation Method 2
magnetization relaxation parameter
Data Source
AI summary
A non-transitory storage medium stores instructions readable and executable by at least one electronic processor to perform an imaging method). The method includes: obtaining multi-parametric magnetic resonance (MR) imaging data parameterized by a diffusion weighting or perfusion weighting parameter and a magnetization relaxation parameter for a region of interest (ROI) of a patient; determining volume fraction maps of the ROI for each of a plurality of tissue types from the multi-parametric MR imaging data; and controlling a display device to display a tissue composition map comprising or generated from the determined volume fraction maps.