Abstract #3907

Deep learning super-resolution MR spectroscopic imaging to map tumor metabolism in mutant IDH glioma patients

Xianqi Li¹, Bernhard Strasser¹, Kourosh Jafari-Khouzani², Daniel P Cahill³, Jorg Dietrich⁴, Tracy T Batchelor⁴, Martin Bendszus⁵, Ulf Neuberger⁶, Philipp Vollmuth⁶, and Ovidiu Andronesi⁷

¹Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ²IBM Watson Health, Boston, MA, United States, ³Neurosurgery, Massachusetts General Hospital, Boston, MA, United States, ⁴Massachusetts General Hospital, Boston, MA, United States, ⁵Heidelberg University Hospital, Boston, MA, United States, ⁶Heidelberg University Hospital, Heidelberg, Germany, ⁷Massachusetts General Hospital,, Charlestown, MA, United States

We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. A generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training was employed to upsample MR spectroscopic imaging data with a factor of four. The preliminary results on simulated and in vivo data indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.

This abstract and the presentation materials are available to members only; a login is required.

Join Here