CEST processing using multipool Lorentzian fitting (MPLF) with inverse Z-spectrum analysis

Author: Jianpan Huang

Email: [email protected]

Affiliation: Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China

The demo data is a simulation data created using 5-pool Bloch-McConnell equation with amide fraction varied as 0.0009009, 0.00090092, 0.00090093, 0.0009009*4. Other parameters remained the same. Therefore, we can see a gradient change in amide (3.5 ppm) map, but not in other CEST maps below.

You can change the data to your own CEST data, which must include CEST images (img), frequency offsets (offs) and ROI (roi).

After running the code, you will see the following fitting process and results (In this case, only the amide concentration was changed, rNOE and MT concentrations remained constant in the four tubes.):

If you use the code, please consider citing the references:

[1] Huang J, Lai J H C, Tse K H, et al. Deep neural network based CEST and AREX processing: Application in imaging a model of Alzheimer’s disease at 3 T. Magnetic Resonance in Medicine, 2022, 87(3): 1529-1545.