Multishot Diffusion MRI Unwrapping via Array Spatial Pseudo-Sensitivity Encoding
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Solution Overview
Problem
Diffusion-weighted MRI images often suffer from motion-induced aliasing and phase inconsistencies due to gradient sensitization, making them less usable and prone to artifacts.
Innovation Solution
A method involving array spatial sensitivity encoding techniques is used to unwrap calibration images, reconstruct unaliased images, and recover pseudo-sensitivity maps, allowing for the correction of motion artifacts and aliasing in diffusion-weighted images by jointly unwrapping multiple shots as additional channel data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diffusion weighted imaging uses gradient sensitization to detect water diffusion, then sensitivity to early pathologic changes is improved, but motion-induced aliasing and phase inconsistencies occur
Solution Approach 1:
The imaging process is divided into multiple segmented acquisitions (e.g., multiple shots or segments) where each segment acquires a portion of the k-space data. By segmenting the acquisition, the patent reduces motion-induced aliasing in each segment while maintaining the overall diffusion weighting capability. The segmented data are then combined through parallel imaging reconstruction to produce the final image with both high sensitivity and reduced artifacts.
Solution Approach 2:
The patent introduces sensitivity encoding information from multiple receiver coils as an intermediary to resolve motion-induced phase inconsistencies. The coil sensitivity profiles serve as a mediator that provides additional spatial encoding information, allowing the system to distinguish between true tissue signal and motion-induced phase errors, thereby improving image reliability while preserving diffusion sensitivity.
2Productivity
If segmented acquisition mode is used for diffusion weighted imaging, then scanning efficiency is improved, but destructive phase inconsistencies and aliasing occur
Solution Approach 1:
The patent segments the k-space acquisition into multiple shots or segments that can be acquired more efficiently. Each segment captures a portion of the data, improving scanning efficiency by allowing parallel or interleaved acquisition. The segmented data are then reconstructed using sensitivity encoding to resolve phase inconsistencies and eliminate aliasing, maintaining image quality despite the segmented approach.
Solution Approach 2:
The patent changes the encoding parameters by incorporating sensitivity encoding information from multiple coils into the reconstruction process. This parameter change allows the system to handle segmented acquisition data differently, using the additional coil sensitivity information to correct phase inconsistencies and remove aliasing artifacts, thereby maintaining high image quality while achieving improved scanning efficiency.
3Reliability
If array spatial sensitivity encoding technique is applied to unwrap calibration images, then image clarity is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary calibration to determine the sensitivity profiles of each receiver coil before the actual imaging acquisition. This preliminary action stores the sensitivity information for later use in the reconstruction process, reducing the computational complexity during image formation. By pre-calculating and storing the sensitivity encoding parameters, the system improves image clarity without excessively increasing processing complexity during the main imaging sequence.
Data Source
AI summary
A method for magnetic resonance imaging includes unwrapping a calibration image based on coil sensitivity data obtained according to an array spatial sensitivity encoding technique and acquiring raw scan data of a plurality of MRI scan shots. The method further includes reconstructing an aliased image for each of the MRI scan shots, reconstructing an unaliased image for each of the MRI scan shots, according to the calibration image, recovering a plurality of pseudo-sensitivity maps from the plurality of unaliased images and from the calibration image, and unwrapping at least one final unaliased image from the plurality of aliased images, according to the plurality of pseudo-sensitivity maps.