Magnetic Resonance Fingerprinting

(T. Christen, N. Pannetier, W. Ni)

A powerful alternative to traditional analytical modeling has been recently proposed [1], in which numerical simulations of the MRI signal are used in conjunction with the concept of fingerprinting. In the first demonstration of the method, a fast MR sequence (inversion-recovery balanced free precession sequence (IR-bSSFP)) known for its sensitivity to relaxation times (T1 and T2) was used to acquire data in vivo. The acquisition was repeated with a random choice of parameters such as repetition time, flip angle, and inversion time, resulting in a different signal evolution in every voxel. The signal was too complex to be analyzed with classic MR equations, yet this ‘fingerprint’ was compared to a dictionary of curves obtained using numerical simulations of the same experiment. Using this approach, simultaneous measurements of T1, T2, frequency, and proton density were obtained with good accuracy and robust behavior in the presence of noise and other acquisitions errors. 

In this project, we hypothesize that a similar approach of using numerical simulations and MRI fingerprinting together in an analysis of FID and SE signal evolutions can be used to retrieve quantitative information about the microvascular network. As a demonstration, we sampled the MR signal evolution with a Gradient Echo Sampling of the FID and SE (GESFIDE) sequence and defined the vascular fingerprint as the ratio of GESFIDE signal acquired pre and post contrast agent injection. We then simulated the same experiment with a numerical tool that takes a virtual voxel containing blood vessels as input, computes the microscopic magnetic fields and water diffusion effects, and eventually predicts the MR signal evolution. The parameter inputs of the simulations (CBV, vessel radius, and blood oxygenation) were varied to obtain a dictionary of possible MR signal evolutions. The fingerprint and dictionary were finally compared using a least square minimization method. Our approach has been tested in normal human subjects and the results compared to conventional MR vascular imaging approaches. It has also been tested in different models of gliomas in rat brains and compared to histological markers.

We are currently working on new ways to improve the creation of virtual voxels by including realistic microvasculare structures and myelin fibers. We are increasing the dimensions of the dictionaries by including new parameters such as water diffusion, phase maps, etc...New types of optimized fingerprints are also under investigations.


References: [1] Ma et al., Nature 2013. [2] Christen et al., NeuroImage, 2014.

Greg Zaharchuk, MD., PhD.

Associate Professor of Radiology
Office: Lucas Center, PS-04
Phone: (650) 735-6172
Email: gregz@stanford.edu

Michael E. Moseley, PhD.

Professor, Radiology
Office: Lucas Center, Rm PS-062
Phone: (650) 723-8697
email: moseley@stanford.edu