Difference between revisions of "Projects:FieldmapFreeDistortionCorrection"

From NAMIC Wiki
Jump to: navigation, search
Line 3: Line 3:
 
Localization of functional information relies on accurate registration of EPI and structural MR, which can be difficult due to EPI distortion caused by B0 field inhomogeneity. Correcting distortion using acquired Fieldmaps has been shown to improve registration [1], but Fieldmaps may not be available or may not be applicable if significant motion is present in the EPI, resulting in sub-optimal registration.
 
Localization of functional information relies on accurate registration of EPI and structural MR, which can be difficult due to EPI distortion caused by B0 field inhomogeneity. Correcting distortion using acquired Fieldmaps has been shown to improve registration [1], but Fieldmaps may not be available or may not be applicable if significant motion is present in the EPI, resulting in sub-optimal registration.
  
 +
{|
 
|[[Image:Igt poster fig1.jpg|thumb|]]
 
|[[Image:Igt poster fig1.jpg|thumb|]]
 +
|}
  
 
= Problem 1: Segmentation =
 
= Problem 1: Segmentation =

Revision as of 20:00, 14 May 2009

Home < Projects:FieldmapFreeDistortionCorrection

Registration without Correction

Localization of functional information relies on accurate registration of EPI and structural MR, which can be difficult due to EPI distortion caused by B0 field inhomogeneity. Correcting distortion using acquired Fieldmaps has been shown to improve registration [1], but Fieldmaps may not be available or may not be applicable if significant motion is present in the EPI, resulting in sub-optimal registration.

Igt poster fig1.jpg

Problem 1: Segmentation

Magnetic field models exist to compute a Fieldmap from a Tissue/Air segmentation [2,3], but segmenting structural MR is difficult due to the similar intensities of bone and air. In the Fieldmap-Free method [4], T1 MRI was segmented using a trained classifier that computes the probability of tissue given MR intensity. CT data was used for training and validation only, allowing the trained classifier to be applied to data sets without CT.


Problem 2: Shim Estimation

Existing magnetic field models do not account for the shim fields that reduce B0 field inhomogeniety prior to acquisition. Without this information, accurate unwarping is not possible. In this Method, a Fieldmap (without shim) was computed from the Segmented MR using the field model in [2]. Missing Shim Fields were modeled by spherical harmonic basis functions. Registration was used to search over shim parameters until optimal agreement between the EPI and structural MR was obtained.