Non-Cartesian MR Imaging Data Volume Reduction

Solution Overview

Problem

Magnetic resonance (MR) imaging using non-Cartesian sampling methods results in large data volumes, requiring significant computational resources for image reconstruction, which hinders efficient and real-time imaging processes.

Innovation Solution

The method involves obtaining k-space data from an MR scanner using non-Cartesian sampling, regridding the data to generate intermediate k-space data, calibrating this data by removing edge portions to create a smaller calibrated field of view, and reconstructing the MR image using compressed sensing or parallel imaging algorithms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If non-Cartesian sampling is used for MR data acquisition, then imaging speed and motion insensitivity are improved, but data volume and computational complexity increase

Engineering Contradiction:

Improveimaging speedVSAvoiddata volume

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

The patent extracts only the essential central portion of the k-space data needed for image reconstruction, discarding the redundant edge portions. This is achieved by identifying the central region containing the majority of useful imaging information and isolating it from the complete non-Cartesian sampled data, thereby reducing computational load while preserving image quality

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the complete k-space data into distinct regions: a central portion containing essential imaging information and edge portions containing redundant data. By processing only the segmented central region, the system achieves faster reconstruction speeds without sacrificing critical diagnostic information

Inventive Principle:

Principle #1Segmentation

2Speed

If non-Cartesian sampling is used for MR data acquisition, then imaging speed and motion insensitivity are improved, but computational complexity in image reconstruction increases

Engineering Contradiction:

Improveimaging speedVSAvoidcomputational complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent extracts only the essential central portion of the k-space data needed for image reconstruction, discarding the redundant edge portions. This is achieved by identifying the central region containing the majority of useful imaging information and isolating it from the complete non-Cartesian sampled data, thereby reducing computational load while preserving image quality

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the complete k-space data into distinct regions: a central portion containing essential imaging information and edge portions containing redundant data. By processing only the segmented central region, the system achieves faster reconstruction speeds without sacrificing critical diagnostic information

Inventive Principle:

Principle #1Segmentation

3Area of stationary object

If the complete field of view is processed for reconstruction, then image coverage is maximized, but processing time and computational resources increase

Engineering Contradiction:

Improvefield of viewVSAvoidprocessing time

Core Design Contradiction:

Area of stationary objectVSLoss of time

Solution Approach 1:

The patent applies partial action by processing only the central portion of the k-space data that contains the most critical imaging information, rather than processing the entire field of view. This selective approach achieves acceptable image quality with significantly reduced processing time, as the central region contributes the majority of diagnostic information

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent extracts only the essential central portion of the k-space data needed for image reconstruction, discarding the redundant edge portions. This is achieved by identifying the central region containing the majority of useful imaging information and isolating it from the complete non-Cartesian sampled data, thereby reducing computational load while preserving image quality

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11592506B2System and method for magnetic resonance imaging

Publication Date: 2023.02.28 SHANGHAI UNITED IMAGING HEALTHCARE

AI summary

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining first k-space data collected from a subject in a non-Cartesian sampling manner. The method may also include generating second k-space data by regridding the first k-space data. The method may further include generating third k-space data by calibrating the second k-space data, wherein a calibrated field of view (FOV) corresponding to the third k-space data is constituted by a central portion of an intermediate FOV corresponding to the second k-space data. The method may still further include reconstructing, using at least one of a compressed sensing algorithm or a parallel imaging algorithm, a magnetic resonance (MR) image of the subject based at least in part on the third k-space data.