Implementation of Compressive Sensing for Preclinical Cine-MRI

Abstract

This paper presents a practical implementation of Compressive Sensing (CS) for a preclinical MRI machine to acquire randomly undersampled k-space data in cardiac function imaging applications. First, random undersampling masks were generated based on Gaussian, Cauchy, wrapped Cauchy and von Mises probability distribution functions by the inverse transform method. The best masks for undersampling ratios of 0.3, 0.4 and 0.5 were chosen for animal experimentation, and were programmed into a Bruker Avance III BioSpec 7.0T MRI system through method programming in ParaVision. Three undersampled mouse heart datasets were obtained using a fast low angle shot (FLASH) sequence, along with a control undersampled phantom dataset. ECG and respiratory gating was used to obtain high quality images. After CS reconstructions were applied to all acquired data, resulting images were quantitatively analyzed using the performance metrics of reconstruction error and Structural Similarity Index (SSIM). The comparative analysis indicated that CS reconstructed images from MRI machine undersampled data were indeed comparable to CS reconstructed images from retrospective undersampled data, and that CS techniques are practical in a preclinical setting. The implementation achieved 2 to 4 times acceleration for image acquisition and satisfactory quality of image reconstruction. © 2014 SPIE.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Compressive sensing; Mouse heart; MRI; Random undersampling; Reconstruction; Reconstruction error; Structural similarity index

International Standard Book Number (ISBN)

978-081949827-4

International Standard Serial Number (ISSN)

1605-7422

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE), All rights reserved.

Publication Date

01 Jan 2014

Link to Full Text

Share

 
COinS