Abstract #3783

tDKI-Net: a joint q-t space learning network for diffusion-time-dependent kurtosis imaging and Karger’s model fitting

Tianshu Zheng¹, Ruicheng Ba¹, Xiaoli Wang², Xizhen Wang³, Chuyang Ye⁴, and Dan Wu¹

¹Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China, Hangzhou, China, ²School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China, Weifang, China, ³Medical imaging center, Affiliated hospital of Weifang Medical University, Weifang, Shandong, China, Weifang, China, ⁴School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China, Beijing, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniquesTime-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for non-invasive characterization of tissue microstructure. The TDDMRI models require both densely sampled q-space (b-value and diffusion direction) and t-space (diffusion time) data for microstructural fitting, leading to very time-consuming acquisition protocols. In this work, we presented a tDKI-Net to estimate diffusion kurtosis at multiple diffusion times, which was fed into the Karger model to obtain K₀ and transmembrane exchange time, using downsampled q-space and t-space data. We tested the proposed network in the normal rat brains, as well as those in a rat model of Middle Cerebral Artery Occlusion.

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