Simultaneous acquisition of Qualitative and Quantitative MRI (Q2MRI) using deep learning

Stanford researchers at the Xing Lab have developed a novel method using deep neural networks called "Q²MRI" to simultaneously acquire qualitative MR image and quantitative MRI parametric maps without changing the clinical imaging protocol or elongating MRI scan time. Currently, quantitative MRIs are obtained from a series of qualitative MRI, which takes prohibitively long scan times. The new method automatically derives quantitative MRI from a single qualitative MRI with an established prediction model. In addition, the proposed approach suppresses measurement errors caused by RF inhomogeneity and eliminates the possibility of inter-scan motion. This invention will enable broader use of quantitative MRIs which provides more information about tissue characterization and tissue response assessment than qualitative MRIs.

Figure 1

Figure 1 description - Generating quantitative T₁ map and proton density map from qualitative T₁ weighted MRI (a) using conventional model fitting and (b) using a deep convolutional neural network.

Figure 2

Figure 2 description - Training and testing of a neural network for the derivation of quantitative MRI from a single qualitative MRI. Notice that the ground truth image is obtained using conventional quantitative MRI.

Stage of Research: