Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction
By offering a rapid warm start to the iterative reconstruction algorithm, this method substantially reduces processing time while preserving reconstruction quality. Its successful implementation paves the way for advanced spatio-temporal MRI techniques, addressing the challenge of extensive reconstruction times and ensuring efficient, high-quality imaging in a streamlined manner.
Authors
- Sophie Schauman (ORCID: https://orcid.org/0000-0002-3744-2553)
- Elizabeth Tong (ORCID: https://orcid.org/0000-0002-9550-8178)
- Itthi Chatnuntawech (ORCID: https://orcid.org/0000-0002-4049-5648)
- Congyu Liao (ORCID: https://orcid.org/0000-0003-2270-276X)
- Siddharth Iyer (ORCID: https://orcid.org/0000-0002-6432-0850)
- Christopher M. Sandino (ORCID: https://orcid.org/0000-0002-8360-0153)
- Natthanan Ruengchaijatuporn (ORCID: https://orcid.org/0000-0001-7219-4148)
- Mahmut Yurt (ORCID: https://orcid.org/0000-0003-3280-4217)
- Xiaozhi Cao (ORCID: https://orcid.org/0000-0001-5095-648X)
- Kawin Setsompop (ORCID: https://orcid.org/0000-0003-0455-7634)
Institutions
- National Science and Technology Development Agency (TH)
- Chulalongkorn University (TH)
- Karolinska Institutet (SE)
- National Nanotechnology Center (TH)
- Massachusetts Institute of Technology (US)
- Stanford University (US)
Publication Details
- Journal
- Magnetic Resonance Materials in Physics Biology and Medicine
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1007/s10334-024-01222-2
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Karolinska Institutet
- National Institutes of Health
- GE Healthcare