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Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
- Source :
- Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, May 2022, London, United Kingdom
- Publication Year :
- 2022
- Publisher :
- HAL CCSD, 2022.
-
Abstract
- International audience; We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR scanner hardware constraints as compared to using added penalties in the cost function. Further, we use the novel HybLearn scheme to jointly learn and compare our results through a retrospective study on fastMRI validation dataset.
- Subjects :
- Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
acquisition
Machine Learning (stat.ML)
Machine Learning (cs.LG)
joint optimization
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Statistics - Machine Learning
Optimization and Control (math.OC)
FOS: Electrical engineering, electronic engineering, information engineering
FOS: Mathematics
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
Electrical Engineering and Systems Science - Signal Processing
Mathematics - Optimization and Control
non-Cartesian trajectories
MRI
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, May 2022, London, United Kingdom
- Accession number :
- edsair.doi.dedup.....36da62439f5684e8b774b77b5eca42c8