Design and Construction of a Realistic DWI Phantom for Filtering Performance Assessment

A methodology to build a realistic phantom for the assessment of filtering performance in Diffusion Weighted Images (DWI) is presented. From a real DWI data-set, a regularization process is carried out taking into account the diffusion model. This process drives to a model which accurately preserves the structural characteristics of actual DWI volumes, being in addition regular enough to be considered as a noise-free data-set and therefore to be used as a ground-truth. We compare our phantom with a kind of simplified phantoms commonly used in the literature (those based on homogeneous cross sections), concluding that the latter may introduce important biases in common quality measures used in the filtering performance assessment, and even drive to erroneous conclusions in the comparison of different filtering techniques.

[1]  J. Schnabel,et al.  Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging , 2000, Journal of magnetic resonance imaging : JMRI.

[2]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. , 1996, Journal of magnetic resonance.

[3]  Santiago Aja-Fernández,et al.  Joint LMMSE Estimation of DWI Data for DTI Processing , 2008, MICCAI.

[4]  Jean-Philippe Thiran,et al.  Sequential anisotropic multichannel Wiener filtering with Rician bias correction applied to 3D regularization of DWI data , 2009, Medical Image Anal..

[5]  Pierrick Coupé,et al.  Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI , 2007, MICCAI.

[6]  Pierrick Coupé,et al.  Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI , 2008, MICCAI.

[7]  N. Ayache,et al.  Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics , 2007 .

[8]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[9]  J. Sijbers,et al.  Maximum likelihood estimation of signal amplitude and noise variance from MR data , 2004, Magnetic resonance in medicine.

[10]  Nicholas Ayache,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I , 2007, MICCAI.

[11]  Yunmei Chen,et al.  DT-MRI denoising and neuronal fiber tracking , 2004, Medical Image Anal..

[12]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[13]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[14]  Aleksandra Pizurica,et al.  A versatile wavelet domain noise filtration technique for medical imaging , 2003, IEEE Transactions on Medical Imaging.

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  R. Deriche,et al.  Regularized, fast, and robust analytical Q‐ball imaging , 2007, Magnetic resonance in medicine.

[17]  Derek K. Jones,et al.  “Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data , 2004, Magnetic resonance in medicine.

[18]  P. Basser,et al.  Statistical artifacts in diffusion tensor MRI (DT‐MRI) caused by background noise , 2000, Magnetic resonance in medicine.

[19]  Carl-Fredrik Westin,et al.  Restoration of DWI Data Using a Rician LMMSE Estimator , 2008, IEEE Transactions on Medical Imaging.

[20]  M. Smith,et al.  An unbiased signal-to-noise ratio measure for magnetic resonance images. , 1993, Medical physics.

[21]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[22]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.