Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging
Deep-ER provides efficient and robust reconstruction for sparse-sampled MRSI. The accelerated acquisition-reconstruction MRSI is compatible with high-throughput imaging workflow. It is expected that such improved performance will facilitate basic and clinical MRSI applications for neuroscience and precision medicine.
Authors
- Daniel P. Cahill (ORCID: https://orcid.org/0000-0003-2552-6546)
- Wolfgang Bogner (ORCID: https://orcid.org/0000-0002-0130-3463)
- Stanislav Motyka (ORCID: https://orcid.org/0000-0002-6314-316X)
- Paul Weiser (ORCID: https://orcid.org/0009-0004-2503-5696)
- Jörg Dietrich (ORCID: https://orcid.org/0000-0001-8291-0136)
- Bernhard Strasser (ORCID: https://orcid.org/0000-0001-9542-3855)
- Ovidiu C. Andronesi (ORCID: https://orcid.org/0000-0002-7412-0641)
- Erik J. Uhlmann (ORCID: https://orcid.org/0000-0003-1432-4754)
- Georg Langs
- Tracy T. Batchelor (ORCID: https://orcid.org/0000-0001-7568-6607)
- Antoine Klauser (ORCID: https://orcid.org/0000-0003-3019-9914)
- Polina Golland (ORCID: https://orcid.org/0000-0003-2516-731X)
- Malte Hoffmann (ORCID: https://orcid.org/0000-0002-5511-0739)
- Nalini Singh (ORCID: https://orcid.org/0000-0003-3584-2198)
Institutions
- Brigham and Women's Hospital (US)
- Beth Israel Deaconess Medical Center (US)
- Massachusetts General Hospital (US)
- Center for Neuro-Oncology (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Centre d'Imagerie BioMedicale (CH)
- Massachusetts Institute of Technology (US)
- Medical University of Vienna (AT)
Publication Details
- Journal
- NeuroImage
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1016/j.neuroimage.2025.121045
- Citations
- 1
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Austrian Science Fund
- National Institutes of Health