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Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy. Phys Med Biol 2017 04 21;62(8):2910-2921 PMID: 27997382

Pubmed ID

27997382

DOI

10.1088/1361-6560/aa54f2

Abstract

The purpose of this work is to investigate the effects of undersampling and reconstruction algorithm on the total processing time and image quality of respiratory phase-resolved 4D MRI data. Specifically, the goal is to obtain quality 4D-MRI data with a combined acquisition and reconstruction time of five minutes or less, which we reasoned would be satisfactory for pre-treatment 4D-MRI in online MRI-gRT. A 3D stack-of-stars, self-navigated, 4D-MRI acquisition was used to scan three healthy volunteers at three image resolutions and two scan durations. The NUFFT, CG-SENSE, SPIRiT, and XD-GRASP reconstruction algorithms were used to reconstruct each dataset on a high performance reconstruction computer. The overall image quality, reconstruction time, artifact prevalence, and motion estimates were compared. The CG-SENSE and XD-GRASP reconstructions provided superior image quality over the other algorithms. The combination of a 3D SoS sequence and parallelized reconstruction algorithms using computing hardware more advanced than those typically seen on product MRI scanners, can result in acquisition and reconstruction of high quality respiratory correlated 4D-MRI images in less than five minutes.

Author List

Mickevicius NJ, Paulson ES

Author

Eric Paulson PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin




Scopus

2-s2.0-85016933762   17 Citations

MESH terms used to index this publication - Major topics in bold

Algorithms
Artifacts
Healthy Volunteers
Humans
Imaging, Three-Dimensional
Magnetic Resonance Imaging
Motion
Online Systems
Radiographic Image Interpretation, Computer-Assisted
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Image-Guided
Respiratory-Gated Imaging Techniques
jenkins-FCD Prod-332 f92a19b0ec5e8e1eff783fac390ec127e367c2b5