Quantification of relaxation times in MR Fingerprinting using deep learning

By Zhenghan Fang, Yong Chen, Weili Lin, Dinggang Shen. 2017

12 citations recorded by the source corpus.

Synopsis MRF is a new quantitative MR imaging technique, which can provide rapid and simultaneous measurement of multiple tissue properties. Compared to the fast speed for data acquisition, the post-processing to extract tissue properties with MRF is relatively slow and often requires a large memory for the storage of both image dataset and MRF dictionary. In this study, a convolutional neural network was developed, which can provide rapid estimation of multiple tissue properties in 0.1 sec. The T1 and T2 values obtained in white matter and gray matter are also in a good agreement with the results from pattern matching.

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1. Arvid Lundervold
2. Florian Tramèr

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “[167] used a deep learning method to extract tissue properties from highly undersampled 2D MRF-FISP data in brain imaging, where 2300 time points were acquired from each measurement and each time point consisted of data from one spiral readout only.”