pMRI Calibration via Cartesian Continuous Sampling

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Solution Overview

Problem

Conventional Parallel Magnetic Resonance Imaging (pMRI) techniques require additional auto-calibration signal (ACS) lines to estimate coil sensitivities, which increases scan time due to the need for extra k-space data acquisition, reducing the benefits of under-sampling.

Innovation Solution

The method employs Cartesian continuous sampling with an extended acquisition window and overlapping phase-encoding gradients, allowing for calibration without additional ACS lines by continuously sampling during gradient changes, thereby reducing the need for extra data acquisition and shortening the imaging session.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If additional ACS lines are acquired for calibration, then coil sensitivity estimation accuracy is improved, but scan time increases

Engineering Contradiction:
Improvecoil sensitivity estimation accuracyVSAvoidscan time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The patent combines the calibration data acquisition with the actual imaging data acquisition by using overlapping phase-encoding gradients. The ACS lines are acquired during the same scan session as the imaging data, merging two previously separate processes into one unified acquisition sequence, thereby eliminating dedicated calibration time while maintaining sensitivity estimation accuracy

Inventive Principle:
Principle #5Merging (Combining)

Solution Approach 2:

The patent implements continuous sampling during gradient changes and transitions between k-space lines. By continuously acquiring data during what would traditionally be idle gradient transition periods, the system maintains useful data acquisition throughout the entire scan session, eliminating wasted time while ensuring sufficient calibration data is collected

Inventive Principle:
Principle #20Continuity of useful action

2Productivity

If under-sampling is applied to reduce scan time, then productivity is improved, but calibration data quality deteriorates

Engineering Contradiction:
Improvescan speedVSAvoidcalibration data quality
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the k-space acquisition into multiple interleaved sequences with different phase-encoding steps. By distributing the under-sampled acquisitions across multiple segments and combining them through the overlapping gradient technique, sufficient calibration data is accumulated from multiple sources, maintaining data quality despite reduced sampling density in any single sequence

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent changes the timing and phasing parameters of the gradient waveforms to create overlapping acquisition windows. By adjusting gradient durations and timing offsets, the system transforms the acquisition parameters to enable continuous sampling during transitions, ensuring adequate calibration data is captured even with under-sampling of the main imaging data

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS7671589B2Calibrating pMRI with cartesian continuous sampling
Publication Date: 2010.03.02 CASE WESTERN RESERVE UNIV
  • US7671589B2 patent drawing
  • US7671589B2 patent drawing
  • US7671589B2 patent drawing

AI summary

Example systems, methods, and apparatus control a pMRI apparatus to produce a pulse sequence having an extended acquisition window, and overlapping phase-encoding gradients and read gradients. One example method controls a pMRI apparatus to produce a trajectory having Cartesian and radial segments that sample in a manner that satisfies the Nyquist criterion in at least one region of a volume to be imaged. The pMRI apparatus is controlled to apply radio frequency energy to the volume according to the pulse sequence and following the trajectory and to acquire MR signal from the volume in response to the application of the RF energy. The MR signal includes a first component associated with the Cartesian segment of the trajectory and a second component associated with the radial segment of the trajectory. The example method includes calibrating a reconstruction process using Nyquist-satisfying data from the second component.