Respiratory Phase-Resolved 3D MRI Motion Correction
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Solution Overview
Problem
Conventional 4D MRI techniques suffer from limited spatial resolution and poor temporal resolution, leading to inaccurate assessment of tumor motion due to limited use of k-space data for reconstruction, resulting in low signal-to-noise ratio and increased streak artifacts.
Innovation Solution
The method involves iterative motion correction and averaging techniques, where all acquired k-space lines are used for reconstruction, aligning and averaging 3D image sets across respiratory phases to improve signal-to-noise ratio and reduce artifacts, using self-gating signals to divide k-space data into bins and employing symmetric diffeomorphic models for transforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If k-space data is divided into multiple respiratory bins for 4D MRI reconstruction, then respiratory motion resolution is improved, but signal-to-noise ratio deteriorates and streak artifacts increase
Solution Approach 1:
The patent segments k-space data into multiple respiratory bins based on self-gating signals to resolve different phases of the respiratory cycle. This segmentation enables 4D MRI reconstruction with improved respiratory motion resolution while the patent subsequently combines these segmented datasets through motion-corrected averaging to mitigate the noise and artifact problems that would otherwise result from using only a subset of k-space lines for each bin.
2Measurement precision
If only a subset of k-space lines is used for each respiratory bin, then respiratory phase resolution is improved, but image quality deteriorates due to increased noise and artifacts
Solution Approach 1:
The patent merges multiple respiratory bin datasets through motion-corrected averaging, where images from different respiratory phases are aligned using calculated transforms and then averaged together. This combining process allows the system to maintain fine respiratory phase resolution from the binned data while simultaneously improving image quality by reducing noise and streak artifacts through the averaging of multiple datasets.
3Speed
If 2D acquisition is used for real-time imaging, then temporal resolution is improved, but spatial resolution deteriorates
Solution Approach 1:
The patent transitions from 2D acquisition to 3D acquisition, adding a spatial dimension to the imaging process. By acquiring 3D k-space data continuously during free breathing and then sorting this three-dimensional dataset into respiratory bins, the system achieves both high spatial resolution (inherent advantage of 3D MRI) and good temporal resolution (through efficient sorting and reconstruction of the 3D data into 4D images).
Data Source
AI summary
A method for performing 3D body imaging includes performing a 3D MRI acquisition of a patient to acquire k-space data and dividing the k-space data into k-space data bins. Each bin includes a portion of the k-space data corresponding to a distinct breathing phase. 3D image sets are reconstructed from the bins, with each 3D image set corresponding to a distinct k-space data bin. For each bin other than a selected reference bin, forward and inverse transforms are calculated between the 3D image set corresponding to the bin and the 3D image set corresponding to the reference bin. Then, a motion corrected and averaged image is generated for each bin by (a) aligning the 3D image set from each other bin to the 3D image set corresponding to the bin using the transforms, and (b) averaging the aligned 3D image sets to yield the motion corrected and averaged image.