Abstract: RinQ Fingerprinting
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Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network.
- 1.Hoppe E, Thamm F, Körzdörfer G, et al. RinQ fingerprinting: recurrence-informed quantile networks for magnetic resonance fingerprinting. In: Proc MICCAI. Springer; 2019. p. 92–100.Google Scholar