Optimization of the MR imaging pipeline using simulation

TY - CHAP

T1 - Optimization of the MR imaging pipeline using simulation

AU - Drobnjak, Ivana

AU - Graham, Mark

AU - Zhang, Hui

AU - Jenkinson, Mark

N1 - Publisher Copyright: © 2022 Elsevier Inc. All rights reserved.

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Careful optimization of the full imaging pipeline, from data acquisition to post-processing, is a crucial part of any magnetic resonance imaging study. However, testing and optimization of pipelines in the “real world” is expensive and challenging, and hence simulations play a key role in the process. In this chapter, we discuss simulation frameworks and the ways they can be applied to optimize imaging pipelines. We cover examples of the use of simulation to evaluate post-processing algorithms, study imaging artifacts, design and test new acquisition techniques, and to produce data for training machine-learning based post-processing tools.

AB - Careful optimization of the full imaging pipeline, from data acquisition to post-processing, is a crucial part of any magnetic resonance imaging study. However, testing and optimization of pipelines in the “real world” is expensive and challenging, and hence simulations play a key role in the process. In this chapter, we discuss simulation frameworks and the ways they can be applied to optimize imaging pipelines. We cover examples of the use of simulation to evaluate post-processing algorithms, study imaging artifacts, design and test new acquisition techniques, and to produce data for training machine-learning based post-processing tools.

KW - Motion correction

KW - MRI

KW - Optimization

KW - POSSUM

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=85137557739&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-824349-7.00016-5

DO - 10.1016/B978-0-12-824349-7.00016-5

M3 - Chapter

AN - SCOPUS:85137557739

SN - 9780128243503

SP - 165

EP - 193

BT - Biomedical Image Synthesis and Simulation

PB - Elsevier

ER -