QSM Software Demo
Zhe Liu1, Pascal Spincemaille1, Alexey Dimov1, Ludovic de Rochefort2, and Yi Wang1

1Department of Radiology, Weill Cornell Medical College, New York, NY, United States, 2Univ. Paris-Sud, Orsay, France

Synopsis

We have developed robust QSM software for both clinicians and researchers. For clinicians interested in using QSM in their daily practices, we present an automated QSM workflow that can be implemented across major MRI manufacturers at both 1.5 and 3T. QSM is automatically reconstructed and available for viewing at the end of each patient MRI session. For researchers interested in further developing QSM algorithms, we present MATLAB tools and source codes for the core Bayesian QSM algorithm, along with implementation for nonlinear field estimation, field unwrapping and background field removal. A GUI tool is provided and demonstrated.

Introduction

We have developed robust QSM software for both clinicians and researchers. For clinicians interested in using QSM in their daily practices, we present an automated QSM workflow that can be implemented across major MRI manufacturers at both 1.5 and 3T. For researchers interested in further developing QSM algorithms, we present MATLAB tools and source codes for the core Bayesian algorithm MEDI (morphology enabled dipole inversion) (1-4), along with algorithms for phase unwrapping (SPURS (5)) and background field removal (PDF (6) or LBV (7)).

Automated QSM in clinics

We have established automated QSM to enable utilization in clinical practice. After a multi echo 3D gradient echo (GRE) sequence is completed, the DICOM images (both magnitude and phase, or real and imaginary) are transferred immediately to a dedicated desktop computer with no human intervention (GE) or with a click by a technician at the scanner console (Siemens and Philips, we are working on eliminating this click). Upon receiving data, the computer automatically performs QSM reconstruction. As the reconstruction finishes (in 8 min), the computer automatically transfers QSM images (DICOM format) back to the scanner console or PACS system. Meanwhile, all images are archived in a backup drive which is connected to the reconstruction server. The entire process takes about 20 min on average at Cornell, so QSM images are available by the end of a patient MRI session. This automated QSM streamline has been distributed and installed on multiple sites.

Data acquisition and saving

Phase image data need to be properly acquired and saved on a MR scanner for reliable QSM. We recommend the following brain protocol using 3D multi-echo GRE sequence with outcome of DICOM images for magnitude/phase or real/imaginary channels: flow compensation on, 1st TE ~ 3 msec, TE spacing = 3 msec, # of TEs = 6~12, TR = 50~60 msec, flip angle = 20, bandwidth 400 Hz/pixel, FOV = 24 cm, slice thickness = 1~2 mm, matrix size = 400x300x88~176, head surface coil array (8~32 channels) with acceleration factor = 2. Total scan time is about 6~12 minutes and dependent on the prescribed resolution and image volume.

We recommend clinicians to work with manufacturers and physicists in setting up automated QSM. There are potential issues in saving proper phase image data, which are related to the manufacturer-specific implementations of the GRE sequence. The magnetic field determination from phase data depends on readout gradient polarity in multiecho GRE. Any phase filter needs to be turned off, and some manufacturers may have echo-dependent phase modulation that needs to be turned off or removed during recon. Because phase data has traditionally been discarded, older software versions of MRI systems may have phase bugs, which may require a software upgrade or a substantial effort to re-engineer proper phase extraction from coil array.

QSM toolbox in MATLAB

We have developed the MEDI toolbox in MATLAB, which can be downloaded from (http://weill.cornell.edu/mri/pages/qsm.html). This toolbox contains implementation for all major steps in QSM: field estimation, phase/field unwrapping, background field removal, and dipole inversion (MEDI). There are options provided for some of these steps, such as PDF/LBV for background field removal. The entire QSM reconstruction could be performed using MATLAB scripts, as well as a graphic user interface (GUI).

GUI tool in QSM toolbox

Our MEDI tool takes DICOM (multi echo GRE complex images) as input. The user can do either an automatic QSM recons with default parameter setting or a manual recon by choosing specific algorithm options and tuning the recon parameters. The user can examine the outcome of each step on a built-in 3D viewer. For example, the user may want to examine whether the phase wrapping is successfully resolved, and if not, try another algorithm option for phase unwrapping. Furthermore, the MEDI tool enables “batch” recon for a user to apply the same recon setting on multiple cases.

Acknowledgements

This work has been supported in part by NIH: NS095562, NS090464, EB013443, NS076092

References

1. de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, Wu J, Wang Y. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magnetic Resonance in Medicine 2010;63(1):194-206.

2. Liu J, Liu T, de Rochefort L, Ledoux J, Khalidov I, Chen W, Tsiouris AJ, Wisnieff C, Spincemaille P, Prince MR, Wang Y. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map. Neuroimage 2012;59(3):2560-2568.

3. Liu T, Wisnieff C, Lou M, Chen W, Spincemaille P, Wang Y. Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping. Magnetic Resonance in Medicine 2013;69(2):467-476.

4. Wang Y, Liu T. Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 2015;73(1):82-101.

5. Dong J, Liu T, Chen F, Zhou D, Dimov A, Raj A, Cheng Q, Spincemaille P, Wang Y. Simultaneous Phase Unwrapping and Removal of Chemical Shift (SPURS) Using Graph Cuts: Application in Quantitative Susceptibility Mapping. Medical Imaging, IEEE Transactions on 2015;34(2):531-540.

6. Liu T, Khalidov I, de Rochefort L, Spincemaille P, Liu J, Tsiouris AJ, Wang Y. A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR in biomedicine 2011;24(9):1129-1136.

7. Zhou D, Liu T, Spincemaille P, Wang Y. Background field removal by solving the Laplacian boundary value problem. NMR in biomedicine 2014;27(3):312-319.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)