Abstract #4606

Deep-ERx2: Deep Learning Reconstruction for fast high-resolution non-Cartesian Compressed-Sensing MR Spectroscopic Imaging at 3T and 7T

Paul Weiser^1,2,3, Georg Langs³, Stanislav Motyka⁴, Bernhard Strasser⁴, Wolfgang Bogner⁴, Polina Golland⁵, Nalini Singh⁵, Jorg Dietrich⁶, Erik Uhlmann⁷, Tracy Batchelor⁸, Daniel Cahill⁹, Gulnur Ungan¹, Malte Hoffmann^1,2, Antoine Klauser¹⁰, and Ovidiu Andronesi^1,2

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA, Cambridge, MA, United States, ²Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA, Boston, MA, United States, ³Computational Imaging Research Lab - Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria, Vienna, Austria, ⁴High Field MR Center - Department of Biomedical Imaging and Image‐Guided Therapy, Medical University of Vienna, Austria, Vienna, Austria, ⁵Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA, Cambridge, MA, United States, ⁶Pappas Center for Neuro-Oncology, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA, Boston, MA, United States, ⁷Department of Neurology, Beth-Israel Deaconess Medical Center, Boston, MA, USA, Boston, MA, United States, ⁸Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA, Boston, MA, United States, ⁹Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA, Boston, MA, United States, ¹⁰Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland, Lausanne, Switzerland

Synopsis

Keywords: Image Reconstruction, Spectroscopy, MRSI, whole-Brain, Deep Learning

Motivation: High-Resolution magnetic resonance spectroscopic imaging (MRSI) is a powerful, non-invasive method for detailed imaging of brain metabolism. However, traditional methods for reconstructing accelerated high-resolution whole-brain MRSI are time-consuming, posing challenges for its routine clinical application.

Goal(s): Reconstruction of high-resolution whole-brain MRSI acquired at 3T and 7T in a timely fashion.

Approach: Deep-learning reconstruction using recurring interlaced convolutional layers with joint dual-space feature representation for non-Cartesian Compressed-Sensing MRSI acquired by 3D ECCENTRIC sampling.

Results: Deep learning ECCENTRIC reconstruction (Deep-ER) speeds up 600 times the reconstruction of high-resolution ECCENTRIC (k,t) data. Deep-learning reconstruction provides improved SNR metabolic maps across acceleration factors.

Impact: Deep-ER enables high-resolution (3.4 mm isotropic) metabolic imaging with clinically feasible acquisition (4-9 min) and reconstruction times (1 min) at 3T and 7T. These times are compatible with the clinical workflow.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.