Patch Based Synthesis of Whole Head MR Images: Application To EPI Distortion Correction

  • Snehashis RoyEmail author
  • Yi-Yu Chou
  • Amod Jog
  • John A. Butman
  • Dzung L. Pham
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9968)


Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to “synthesize” the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize \(T_2-\)w whole head (including brain, skull, eyes etc.) images from \(T_1-\)w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to inhomogeneous \(B_0\) magnetic field are often corrected by non-linearly registering the corresponding \(b=0\) image with zero diffusion gradient to an undistorted \(T_2-\)w image. We show that our synthetic \(T_2-\)w images can be used as a template in absence of a real \(T_2-\)w image. Our patch based method requires multiple atlases with \(T_1\) and \(T_2\) to be registered to a given target \(T_1\). Then for every patch on the target, multiple similar looking matching patches are found on the atlas \(T_1\) images and corresponding patches on the atlas \(T_2\) images are combined to generate a synthetic \(T_2\) of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized \(T_2\) images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.


Image synthesis Patches Distortion correction EPI 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Snehashis Roy
    • 1
    Email author
  • Yi-Yu Chou
    • 1
  • Amod Jog
    • 2
  • John A. Butman
    • 3
  • Dzung L. Pham
    • 1
  1. 1.Center for Neuroscience and Regenerative Medicine, Henry Jackson FoundationBethesdaUSA
  2. 2.Department of Computer ScienceThe Johns Hopkins UniversityBaltimoreUSA
  3. 3.Diagnostic Radiology DepartmentNational Institute of HealthBethesdaUSA

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