Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging.
OBJECTIVE
Diffusion Spectrum Imaging enables to reconstruct the ensemble average propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive sensing offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of three sampling strategies and six sparsifying transforms to show their impact when applied to accelerate compressive sensing-diffusion spectrum imaging.


METHODS
We propose a novel sampling scheme that assures uniform angular and random radial q-space samples. We also compare and implement six discrete sparse representations of the EAP and thoroughly evaluate them on synthetic and real data using metrics from the full EAP, kurtosis, and orientation distribution function.


RESULTS
The discrete wavelet transform with Cohen-Daubechies-Feauveau 9/7 wavelets and uniform angular sampling in combination with random radial sampling showed to be better than other tested techniques to accurately reconstruct the EAP and its features.


CONCLUSIONS
It is important to jointly optimize the sampling scheme and the sparsifying transform to obtain accelerated compressive sensing-diffusion spectrum imaging. Experiments on synthetic and real human brain data show that one can robustly recover both radial and angular EAP features while undersampling the acquisition to 64 measurements (undersampling factor of 4).
Department of Computer Science, Sherbrooke Connectivity Imaging Laboratory, Université de Sherbrooke, Sherbrooke, Quebec, Canada.
Michael
M
Paquette
Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, Sophia-Antipolis Cedex, France.
Sylvain
S
Merlet
MR Clinical Science, Philips Healthcare, Cleveland, Ohio, USA.
Guillaume
G
Gilbert
Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, Sophia-Antipolis Cedex, France.
Rachid
R
Deriche
Department of Computer Science, Sherbrooke Connectivity Imaging Laboratory, Université de Sherbrooke, Sherbrooke, Quebec, Canada.
Maxime
M
Descoteaux
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D044962:Data Compression / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis
compressive sensing; diffusion spectrum imaging; diffusion-weighted imaging; ensemble average propagator; kurtosis; orientation distribution function