  Quantitative MRI is highly desirable in terms of intrinsic tissue parameters
such as T1, T2 and proton density. This approach promises to minimize
diagnostic variability and differentiate normal and pathological tissues by
comparing tissue parameters to the normal ranges. Also, absolute quantification
can help segment MRI tissue images with better accuracy compared to traditional
qualitative segmentation methods. Currently, there are several methods proposed
to quantify tissue parameters; however, all of them require excessive scan time
and thus are difficult to be applied in clinical applications. In this paper,
we propose a novel machine learning approach for MRI quantification, which can
dramatically decrease the scan time and improve image quality.
