Free-Breathing 3D Diffusion MRI via Stack-of-Stars Readout
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Solution Overview
Problem
Diffusion-weighted MRI at high field strengths faces challenges such as signal loss due to body motions, limited spatial resolution, and image distortion, particularly in body imaging, where conventional sequences like single-shot EPI are compromised by cardiac and respiratory motions.
Innovation Solution
A method for free-breathing three-dimensional diffusion imaging using a motion robust, non-Cartesian k-space ordering like stack-of-stars or stack-of-spirals, combined with a first-order moment (M1)-compensated diffusion preparation module and alternative readout schemes like SSFP, FLASH, or TSE, which minimizes signal loss and distortion by adjusting gradient moments and applying diffusion gradients along all axes simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional single-shot EPI diffusion sequence is used, then diffusion-weighted imaging can be performed, but signal loss occurs due to body motions (breathing, heart beating) and image distortion increases at high field strength
Solution Approach 1:
The patent segments the readout into multiple segments and applies a stack-of-stars sampling scheme where k-space is sampled radially in multiple shots. This segmentation allows motion compensation between segments and reduces the impact of body motion on overall image quality, directly addressing the signal loss problem from breathing and heart beating.
Solution Approach 2:
The patent implements dynamic motion compensation by tracking respiratory and cardiac motions and applying real-time corrections to the diffusion gradients and readout timing. This dynamic adaptation allows the sequence to maintain reliability despite ongoing body motions during free-breathing acquisition.
2Measurement precision
If conventional EPI sequence is used at high field strength, then imaging speed is maintained, but spatial resolution is limited and image distortion increases
Solution Approach 1:
The patent transitions from Cartesian k-space sampling to radial/stack-of-stars sampling geometry. This dimensional change in the sampling pattern enables better resolution of fine structures while maintaining efficient data acquisition, simultaneously improving spatial resolution without sacrificing imaging speed through the use of parallel imaging techniques.
3Reliability
If readout-segmented EPI sequence is used, then some motion challenges are alleviated, but additional navigator acquisition is required and efficiency is compromised
Solution Approach 1:
The patent merges the diffusion preparation module with the stack-of-stars readout sequence, eliminating the need for separate navigator acquisitions. The motion compensation is integrated directly into the diffusion-weighted imaging sequence itself, achieving motion robustness without the time penalty of additional navigator scans.
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 high-resolution, motion-robust three-dimensional diffusion imaging under free-breathing conditions, improving image quality and reducing artifacts, as demonstrated by improved delineation of tissues and organs with minimal distortion and phase-error related artifacts.
Implementation Method 1
Diffusion-sensitizing gradients can lead to substantial signal loss in the targeted organ due to body motions
Implementation Method 2
applying, via an image data processor, a motion robust, non-Cartesian k-space ordering
Implementation Method 3
diffusion/T2 preparation, comprising: generating diffusion contrast
Data Source
AI summary
Embodiments can provide a computer-implemented method for free breathing three dimensional diffusion imaging, the method comprising initiating, via a k-space component processor, diffusion/T2 preparation, comprising generating diffusion contrast; and adjusting one or more of amplitude, duration, and polarity to set a first order moment; applying, via an image data processor, a stack of stars k-space ordering, comprising acquiring a radial/spiral view for all members of a plurality of partitions in a partition-encoding direction; increasing an azimuthal angle until a complete set of radial/spiral views are sampled; and applying diffusion gradients along each of three axis simultaneously; and calculating, via the image data processor, an apparent diffusion coefficient map.