Diffusion Tensor Imaging Reconstruction Using Interleaved Projection Views
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diffusion weighted imaging (DWI) techniques require a large number of scans to acquire images in multiple directions, leading to lengthy scan times and potential image artifacts due to undersampling.
Innovation Solution
A method using interleaved projection views and a highly constrained backprojection reconstruction technique that employs a composite image to accurately distribute signal samples across pixels, reducing the number of required views while minimizing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diffusion weighted imaging techniques are used to acquire images in multiple directions, then image quality and directional information are improved, but scan time increases significantly
Solution Approach 1:
The patent segments the k-space sampling process into multiple radial projection views at different angles. Instead of acquiring complete Cartesian k-space data for each diffusion direction separately, the method divides the sampling into interleaved radial projections that can be combined to reconstruct images in multiple directions, thereby reducing the total number of scans required
Solution Approach 2:
The patent creates a universal k-space sampling scheme where a single set of radial projection views at multiple angles serves multiple diffusion weighting directions simultaneously. By acquiring projection data at various angles and using constrained backprojection, the same data can be used to reconstruct images for different diffusion directions, making the acquisition process multi-functional
2Loss of time
If the number of projection views is reduced to decrease scan time, then scan time is improved, but image artifacts increase due to undersampling
Solution Approach 1:
The patent employs constrained backprojection reconstruction that uses feedback from the acquired projection data to iteratively refine the image reconstruction. The method incorporates knowledge of the expected image characteristics and uses this feedback to correct undersampling artifacts, allowing for accurate reconstruction from fewer views than traditional methods
Solution Approach 2:
The patent changes the reconstruction parameters and algorithms used in the backprojection process. By modifying the reconstruction approach to include constraints based on signal contour information and normalization, the method can achieve accurate images from undersampled data, transforming the reconstruction problem to accommodate reduced sampling density
3Productivity
If standard backprojection methods are used with insufficient views, then reconstruction speed is improved, but streak artifacts are produced
Solution Approach 1:
The patent performs preliminary actions by acquiring projection data at multiple angles before performing the final reconstruction. By pre-acquiring the radial projection views and organizing them according to their angular positions, the method prepares the data in advance for constrained backprojection, enabling faster and more accurate reconstruction without streak artifacts
Solution Approach 2:
The patent creates a composite reconstruction approach that combines multiple projection views at different angles into a unified image. By integrating the information from all radial projections and using constrained backprojection with normalization, the method produces a composite image that eliminates streak artifacts while maintaining reconstruction speed
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 significantly reduces scan time for acquiring DWI images with fewer views, producing high-quality images by leveraging a priori knowledge of signal contours and normalizing signal distribution, thereby minimizing clinically objectionable artifacts.
Implementation Method 1
Magnetic resonance imaging uses the nuclear magnetic resonance (NMR) phenomenon to produce images
Implementation Method 2
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field
Implementation Method 3
If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or 'tipped', into the x-y plane to produce a net transverse magnetic moment Mt
Implementation Method 4
A signal is emitted by the excited spins, and after the excitation signal B1 is terminated, this signal may be received and processed to form an image
Data Source
AI summary
Highly undersampled diffusion weighted image data sets are acquired for a plurality of different directions using a projection reconstruction pulse sequence. The acquired projection views are interleaved and are combined to form a more highly sampled data set that is used to reconstruct a composite image. A DWI image is reconstructed from each undersampled data set for each direction using a highly constrained backprojected method that employs the composite image. Diffusion tensor values are calculated from the DWI images.