Free-Breathing T1 Mapping via High-Contrast Image Registration
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Solution Overview
Problem
Current T1 mapping techniques for myocardial extracellular volume estimation are limited by sensitivity to confounders like T2, magnetization transfer, and off-resonance, and lack precision due to poor tissue-blood contrast, especially in free-breathing cardiac imaging where respiratory motion complicates accurate registration.
Innovation Solution
A method involving the acquisition of magnetic resonance imaging data during free breathing, generating high-tissue-blood contrast images, and using non-rigid image registration to align images, improving registration performance and maintaining T1 accuracy with optimized saturation recovery times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If saturation-recovery based sequences (SASHA, SMART1Map, SAPPHIRE) are used to reduce sensitivity to confounders, then robustness to T2, magnetization transfer, and off-resonance is improved, but measurement precision deteriorates due to reduced dynamic range and signal-to-noise
Solution Approach 1:
The patent combines saturation-recovery and inversion-recovery sequences into a unified T1 mapping approach. This merging allows the method to achieve robustness against T2 and magnetization transfer confounders (from saturation-recovery) while maintaining adequate signal-to-noise ratio and dynamic range (from inversion-recovery), thereby resolving the precision-robustness contradiction.
Solution Approach 2:
The patent employs a composite pulse sequence that integrates elements of both saturation-recovery and inversion-recovery techniques. This composite approach creates a hybrid method that leverages the advantages of both sequences: the confounder robustness of saturation-recovery and the precision characteristics of inversion-recovery, thus resolving the technical contradiction between robustness and measurement precision.
2Manufacturing precision
If respiratory navigator triggering is used to address respiratory motion during free-breathing T1 acquisitions, then image alignment is improved, but productivity deteriorates due to reduced navigator gating efficiency and longer acquisition time
Solution Approach 1:
The patent extracts the respiratory motion compensation requirement from the T1 mapping sequence itself and handles it separately through post-acquisition image registration. By separating these functions, the T1 mapping can proceed without navigator gating constraints, improving acquisition efficiency, while image alignment is achieved through dedicated registration algorithms applied to the acquired data.
Solution Approach 2:
The patent performs image registration as a preliminary step before T1 parameter calculation. By pre-aligning the images using registration algorithms that account for respiratory motion, the subsequent T1 mapping can use all acquired images without loss of efficiency to navigator gating, thereby resolving the contradiction between alignment precision and acquisition efficiency.
3Manufacturing precision
If direct image registration of saturation recovery images is performed, then motion correction is achieved, but measurement precision deteriorates due to poor tissue-blood contrast
Solution Approach 1:
The patent introduces an intermediary approach by using a different image contrast (inversion-recovery or non-saturation images) as the basis for registration, rather than directly registering the low-contrast saturation-recovery images. This intermediary contrast provides better tissue-blood differentiation, enabling accurate motion correction that can then be applied to the saturation-recovery data for precise T1 measurement.
Solution Approach 2:
The patent segments the imaging process into distinct contrast-weighted acquisitions: one set of images optimized for registration (with better tissue-blood contrast) and another set for T1 parameter calculation. This segmentation allows each task to use the optimal contrast characteristics, resolving the contradiction between motion correction accuracy and registration precision.
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
This approach enhances the precision and robustness of T1 mapping, achieving higher precision than existing methods like MOLLI, particularly in free-breathing acquisitions, and is more suitable for patients with respiratory challenges, maintaining accurate T1 values and reducing variability.
Implementation Method 1
T1 mapping with gadolinium contrast can also be used to estimate the extracellular volume fraction (ECV)... Accurate T1 mapping techniques without systematic confounders are intuitively desirable... Saturation-recovery based sequence such SAturation-recovery single-SShot Acquisition (SASHA)
Implementation Method 2
acquiring magnetic resonance imaging data for a first plurality of images of the heart of a subject during free breathing of the subject
Implementation Method 3
selecting a subset of images from the first plurality of images, based upon a pre-determined quality metric of image similarity, to be used for non-rigid image registration... aligning the subset of images by non-rigid image registration, using a combination of the first plurality of images and the second plurality of images
Data Source
AI summary
In one aspect, the disclosed technology relates to a method which, in one example embodiment, includes acquiring magnetic resonance imaging data for a plurality of images of the heart of a subject during free breathing of the subject. The method also includes generating an additional plurality of images with high tissue-blood contrast over the region of interest, and selecting a subset of images from the plurality of images, based upon a pre-determined quality metric of image similarity, to be used for non-rigid image registration. The method also includes aligning the subset of images by non-rigid image registration using a combination of the plurality of images and the additional plurality of images, and creating a parametric map from the aligned images.