Signal LMMSE Estimation from Multiple Samples in MRI and DT-MRI

Santiago Aja-Fernandez, Carlos Alberola-Lopez, C.-F. Westin
Tenth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '07)
Volume 4792, Pages 368-375
November, 2007

Download full paper

Abstract

A method to estimate the magnitude MR data from several noisy samples is presented. It is based on the Linear Minimum Mean Squared Error (LMMSE) estimator for the Rician noise model when several scanning repetitions are available. This method gives a closed-form analytical solution that takes into account the probability distribution of the data as well as the existing level of noise, showing a better performance than methods such as the average or the median.

Fusion of MR images from an EPI volume. Original (left) and filtered (right). (a)

Reference

Aja-Fernandez S, Alberola-Lopez C, Westin CF. Signal lmmse estimation from multiple samples in mri and dt-mri. In Tenth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '07), volume 4792 of Lecture Notes in Computer Science. Brisbane, Australia, 2007;368-375.

Bibtex entry

@InProceedings{aja-fernandezMICCAI07,
  author         = "Santiago Aja-Fernandez and Carlos Alberola-Lopez and       
                   Carl-Fredrik Westin",                                       
  title          = "Signal LMMSE Estimation from Multiple Samples in MRI and   
                   DT-MRI",                                                    
  booktitle      = "Tenth International Conference on Medical Image Computing  
                   and Computer-Assisted  Intervention (MICCAI '07)",          
  month          = "November",                                                 
  year           = "2007",                                                     
  volume         = {4792},                                                     
  series         = "Lecture Notes in Computer Science",                        
  pages          = "368--375",                                                 
  address        = "Brisbane, Australia"}

Grants

NIH R01-MH074794