Diffusion Spectrum Imaging via Joint Reconstruction and Compressed Sensing
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Solution Overview
Problem
Conventional diffusion spectrum imaging (DSI) techniques require prolonged scanning times and suffer from noise in reconstructed MR images, making it difficult for clinicians to obtain useful information.
Innovation Solution
The method employs joint image reconstruction (JIR) and compressed sensing (CS) techniques to acquire and process MR raw data from undersampled q-space locations, exploiting structural correlations and signal sparsity to generate accelerated MR images with improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional DSI techniques are used to acquire complete q-space data, then diffusion information completeness is improved, but scanning time increases significantly
Solution Approach 1:
The patent applies partial action by acquiring only a subset of q-space locations rather than the complete set. Undersampled q-space data is collected at fewer locations than traditionally required, and compressed sensing reconstruction algorithms are used to recover the complete diffusion information from this partial data, thereby reducing scanning time while maintaining information completeness
Solution Approach 2:
The patent substitutes the traditional mechanical/data acquisition approach with a computational approach. Instead of acquiring all q-space locations through extended scanning, compressed sensing reconstruction algorithms computationally reconstruct the complete diffusion information from undersampled data, replacing the mechanical sampling process with intelligent signal processing
2Loss of information
If conventional DSI techniques are used to acquire complete q-space data, then diffusion information completeness is improved, but image noise increases
Solution Approach 1:
The patent replaces traditional reconstruction methods with compressed sensing algorithms that incorporate sparsity constraints and signal modeling. This computational approach inherently denoises the data by exploiting the structured sparsity of diffusion signals in q-space, separating true diffusion information from noise artifacts
Solution Approach 2:
The patent changes the reconstruction parameters and criteria by using compressed sensing optimization that enforces sparsity in the diffusion signal representation. This parameter change in the reconstruction approach allows for noise suppression while preserving the essential diffusion information that would be lost in conventional noisy reconstructions
3Productivity
If undersampled q-space locations are used to reduce scanning time, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent substitutes traditional sampling theory with compressed sensing principles. The compressed sensing reconstruction algorithms use sparsity constraints and signal modeling to accurately recover diffusion measurements from undersampled data, maintaining measurement precision despite reduced sampling density
Solution Approach 2:
The patent applies preliminary action by incorporating prior knowledge about the structure and sparsity of diffusion signals into the reconstruction process. Compressed sensing algorithms use these preliminary constraints to guide the reconstruction from undersampled data, ensuring accurate diffusion measurements even with limited sampling
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 reduces noise and allows for faster scanning times while maintaining image quality, enabling the generation of diffusion maps with higher accuracy and reduced acquisition time.
Implementation Method 1
performing a joint image reconstruction technique on the MR raw data to exploit structural correlations in the MR signals to obtain a series of accelerated MR images
Implementation Method 2
performing, for each image pixel in each accelerated MR image of the series of accelerated MR images, a compressed sensing reconstruction technique to exploit q-space signal sparsity to identify a plurality of diffusion maps
Implementation Method 3
by applying a series of diffusion encoding gradient pulses in multiple directions and strengths, a three-dimensional characterization of the water diffusion process may be generated at each spatial location or image voxel
Implementation Method 4
magnetic resonance imaging (MRI) examinations are based on the interactions among a primary magnetic field, a radiofrequency (RF) magnetic field and time varying magnetic gradient fields with gyromagnetic material having nuclear spins within a subject of interest
Implementation Method 5
The precession of spins of these nuclei can be influenced by manipulation of the fields to produce RF signals that can be detected, processed, and used to reconstruct a useful image
Data Source
AI summary
Systems and methods for generating a magnetic resonance (MR) image of a tissue are provided. A method includes acquiring MR raw data. The MR raw data corresponds to MR signals obtained at undersampled q-space locations for a plurality of q-space locations that is less than an entirety of the q-space locations and the MR signals at the q-space locations represent the three dimensional displacement distribution of the spins in the imaging voxel. The method also includes performing a joint image reconstruction technique on the MR raw data to exploit structural correlations in the MR signals to obtain a series of accelerated MR images and performing, for each image pixel in each accelerated MR image of the series of accelerated MR images, a compressed sensing reconstruction technique to exploit q-space signal sparsity to identify a plurality of diffusion maps.