MRI SENSE Reconstruction Using Multiple Coil Sensitivity Maps

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Solution Overview

Problem

Existing SENSE reconstruction techniques for MRI images are prone to aliasing and wrap-around artifacts, particularly when dealing with undersampled data.

Innovation Solution

The method involves determining multiple coil sensitivity maps per receiver coil using an autocalibration protocol like ESPIRIT, rearranging k-space data on an adjusted trajectory by stretching it according to the stitching factor, and performing an iterative optimization with regularization and data-consistency operations within the SENSE reconstruction algorithm.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If SENSE reconstruction is used with undersampled data to reduce acquisition time, then productivity is improved, but image quality deteriorates due to aliasing and wrap-around artifacts

Engineering Contradiction:

Improveacquisition timeVSAvoidimage quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent segments the coil sensitivity information by determining multiple coil sensitivity maps (CSMs) per receiver coil using autocalibration protocols. This segmentation allows the reconstruction algorithm to distinguish between different spatial frequencies and resolve aliased structures, thereby maintaining image quality while using undersampled data for faster acquisition

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extends the traditional SENSE reconstruction by incorporating multiple CSMs in the image domain, effectively adding a dimensional aspect to the sensitivity encoding. This approach transforms the reconstruction problem by utilizing additional sensitivity information across multiple dimensions, enabling artifact reduction while maintaining accelerated acquisition

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Manufacturing precision

If multiple coil sensitivity maps are used to reduce artifacts, then image quality is improved, but device complexity increases

Engineering Contradiction:

Improveimage qualityVSAvoidreconstruction complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary determination of multiple coil sensitivity maps using autocalibration protocols before the actual image reconstruction. This preliminary action prepares the necessary sensitivity information in advance, allowing the main reconstruction algorithm to operate more efficiently with pre-computed data, thereby managing complexity while improving image quality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The autocalibration protocol enables the system to self-determine its coil sensitivity maps without requiring external calibration phantoms or additional calibration scans. This self-service approach integrates the calibration process into the regular imaging workflow, reducing the need for separate calibration procedures and managing system complexity

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12329507B2Multiple coil sensitivity maps of coils of a receiver array of a magnetic resonance imaging apparatus and sense reconstruction

Publication Date: 2025.06.17 SIEMENS HEALTHINEERS AG

AI summary

Various examples relate to SENSitivity Encoding (SENSE) reconstruction of Magnetic Resonance Imaging (MRI) images. Multiple coil sensitivity maps per coil of a receiver coil array are used, e.g., obtained from an Eigenvalue-based Spatially Constrained Iterative Reconstruction Technique (ESPIRIT) autocalibration protocol.