Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging
<|reference_start|>Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging: Introduction: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian compressed-sense acquisition enables fast high-resolution metabolic imaging but has lengthy reconstruction times that limits throughput and needs expert user interaction. Here, we present a robust and efficient Deep Learning reconstruction to obtain high-quality metabolic maps.   Methods: Fast high-resolution whole-brain metabolic imaging was performed at 3.4 mm$^3$ isotropic resolution with acquisition times between 4:11-9:21 min:s using ECCENTRIC pulse sequence on a 7T MRI scanner. Data were acquired in a high-resolution phantom and 27 human participants, including 22 healthy volunteers and 5 glioma patients. A deep neural network using recurring interlaced convolutional layers with joint dual-space feature representation was developed for deep learning ECCENTRIC reconstruction (Deep-ER). 21 subjects were used for training and 6 subjects for testing. Deep-ER performance was compared to conventional iterative Total Generalized Variation reconstruction using image and spectral quality metrics.   Results: Deep-ER demonstrated 600-fold faster reconstruction than conventional methods, providing improved spatial-spectral quality and metabolite quantification with 12%-45% (P<0.05) higher signal-to-noise and 8%-50% (P<0.05) smaller Cramer-Rao lower bounds. Metabolic images clearly visualize glioma tumor heterogeneity and boundary.   Conclusion: 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.<|reference_end|>
@article{weiser2024deep-er:,
    title={Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution
  neurometabolic imaging},
    author={Paul Weiser, Georg Langs, Wolfgang Bogner, Stanislav Motyka, Bernhard
  Strasser, Polina Golland, Nalini Singh, Jorg Dietrich, Erik Uhlmann, Tracy
  Batchelor, Daniel Cahill, Malte Hoffmann, Antoine Klauser, Ovidiu C.
  Andronesi},
    journal={arXiv preprint arXiv:2409.18303},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18303},
    primaryClass={eess.IV cs.LG}
}