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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Analysis of generalized rosette trajectory for compressed sensing MRI.

Ya Li¹, Ran Yang², Cishen Zhang³

¹School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510009, China.

Medical Physics

|September 3, 2015

Summary

Generalized rosette trajectories accelerate magnetic resonance imaging (MRI) scans by improving sampling incoherence. This novel approach enhances compressed sensing (CS) reconstruction performance, offering a ~10% higher signal-to-noise ratio in MRI.

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Area of Science:

Medical Imaging
Signal Processing
Biomedical Engineering

Background:

Compressed Sensing (CS) accelerates Magnetic Resonance Imaging (MRI) by undersampling k-space data.
Reconstructing images from undersampled data requires advanced trajectory designs.

Purpose of the Study:

To generalize rosette trajectories for undersampled k-space data acquisition in MRI.
To analyze the feasibility and advantages of these generalized rosette trajectories for CS MRI.

Main Methods:

Analysis of scan speed using transversal time derived from trajectory curvature.
Evaluation of sampling incoherence via point spread function analysis.
Comparison of rosette, spiral, and radial sampling patterns through simulations.

Main Results:

Generalized rosette trajectories demonstrate superior feasibility for undersampled MRI data acquisition compared to spiral trajectories.
Rosette trajectories exhibit higher sampling incoherence, leading to improved CS reconstruction.
A ~10% higher peak signal-to-noise ratio was achieved with rosette trajectories versus radial and spiral at R=10.

Conclusions:

Generalized rosette trajectories are a promising candidate for CS-based MRI reconstruction.
These trajectories offer enhanced performance for accelerated MRI scans.