Gannet is a software package designed for the batch analysis of edited magnetic resonance spectroscopy (MRS) data. 

Gannet runs in Matlab, and is distributed as code rather than executables, empowering users to make local changes.  

Gannet is designed to run without user intervention, to remove operator variance from the quantification of edited MRS data.


The Team

 Richard Edden

Richard Edden

Richard Edden  

Associate Professor of Radiology, The Johns Hopkins University School of Medicine

 

 

 Ashley Harris

Ashley Harris

Ashley Harris

Assistant Professor of Radiology, University of Calgary

 

 

 

 John Evans

John Evans

John Evans

MRI Research Fellow, Cardiff University Brain Research Imaging Centre

 

 

 Nicolaas Puts

Nicolaas Puts

Nicolaas Puts

Postdoctoral Fellow, The Johns Hopkins University School of Medicine

 

Georg Oeltzschner

Postdoctoral Fellow, The Johns Hopkins University School of Medicine

 

 

Mark Mikkelsen

Postdoctoral Fellow, The Johns Hopkins University School of Medicine

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Muhammad G. Saleh

Postdoctoral Fellow, The Johns Hopkins University School of Medicine

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Kimberly Chan

PhD Student, The Johns Hopkins University, School of Biomedical Engineering


The Process

Gannet consists of a number of steps:

  • GannetLoad processes time-domain MRS data;
  • GannetCoRegister generates a mask of the MRS voxel in T1-image space;
  • GannetFit models the edited spectrum;
  • GannetSegment derives gray matter, white matter and CSF voxel fractions;
  • GannetQuantify calculates tissue-corrected GABA levels.