A novel deep learning approach for b-value optimisation in intravoxel incoherent motion magnetic resonance imaging on simulated data

Intravoxel incoherent motion magnetic resonance imaging (IVIM MRI) is a non-invasive technique which measures the perfusion and diffusion effects present in a tissue. One of the major challenges in IVIM imaging is the prolonged scan time since multiple b-value images are required for estimating IVIM parameters. The proposed work introduces a novel approach for reducing the number of b-values required for the generation of IVIM signals from unknown b-values using long short-term memory (LSTM) network. Experimental results show that LSTM network has the capability of accurately estimating IVIM parameters even with 4 b-values, with the estimated values are in agreement with the literature. If an IVIM machine takes 37.5 seconds for the acquisition of a b-value signal, then it will take only 2.5 minutes for acquiring IVIM signals for 4 b-values. This will reduce the patient discomfort and increases the clinical acceptance of IVIM imaging.