SNR Calculation in Parallel MRI Reconstruction
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Solution Overview
Problem
Current parallel imaging technologies in MRI face challenges in accurately calculating the signal-to-noise ratio (SNR) due to the lack of consideration for the contribution of reference lines in image reconstruction, leading to potential artifacts and reduced imaging speed.
Innovation Solution
A method for calculating SNR in parallel acquisition image reconstruction that takes into account the contribution of reference lines by determining a reconstruction expression and weighted coefficients, allowing for accurate evaluation of SNR loss through the ratio of SNR in parallel-acquired images to standard-acquired images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel acquisition imaging is used to increase imaging speed, then imaging speed is improved, but signal-to-noise ratio (SNR) calculation accuracy deteriorates due to not considering reference line contributions
Solution Approach 1:
The patent applies preliminary action by acquiring reference lines (fully sampled k-space data) before performing parallel reconstruction. These reference lines are stored and later used to calculate accurate SNR values for each pixel in the reconstructed image, ensuring that the contribution of reference data to noise reduction is properly accounted for in the SNR evaluation.
Solution Approach 2:
The patent uses reference lines as an intermediary element that bridges the gap between accelerated parallel imaging and accurate SNR measurement. The reference lines serve as a mediator that provides the necessary information to evaluate SNR without requiring a separate fully-sampled acquisition, thus maintaining both speed improvement and measurement accuracy.
2Productivity
If K-space filling rate is reduced below Nyquist theorem limit to increase imaging speed, then imaging speed is improved, but image quality deteriorates due to appearance of artifacts
Solution Approach 1:
The patent applies partial action by acquiring only a portion of the k-space data (reference lines) at full sampling rate while under-sampling the remaining data for parallel reconstruction. This partial full-sampling approach provides enough information to calculate accurate SNR and maintain image quality without requiring complete Nyquist-compliant sampling, thus achieving speed improvement while minimizing artifacts.
3Productivity
If multiple receiving channels and multi-arrayed coils are used in parallel imaging, then imaging speed is improved, but system complexity increases due to coil sensitivity calibration and data processing requirements
Solution Approach 1:
The patent applies self-service by using the acquired reference lines to automatically calculate coil sensitivity maps and SNR values without requiring manual calibration or complex external processing. The system uses its own acquired data (reference lines) to perform the calibration and evaluation tasks, reducing the need for additional complex equipment or procedures.
Data Source
AI summary
The invention discloses a method for calculating the signal-to-noise ratio (SNR) in parallel acquisition image reconstruction, comprising: determining a reconstruction expression for a linear operation of the image reconstruction; determining a weighted coefficient according to the reconstruction expression; calculating the SNR according to the weighted coefficient and the raw data. The SNR not only is relevant to the geometric shape and position of the coils, but also is influenced by the reconstruction method and the sampling mode. The SNR is calculated based on contribution of the raw data at positions in the reading direction from all the phase-coding lines in all acquisition channels. It reflects more precisely the loss of the SNR in the parallel acquisition image reconstruction, especially the changes in the SNR caused by the number of the reference lines combined during the reconstruction.