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Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging.
Magn Reson Med 2015; 73(1):401-16MR

Abstract

PURPOSE

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.

CONCLUSION

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).

Authors+Show Affiliations

Department of Computer Science, Sherbrooke Connectivity Imaging Laboratory, Université de Sherbrooke, Sherbrooke, Quebec, Canada.Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, Sophia-Antipolis Cedex, France.MR Clinical Science, Philips Healthcare, Cleveland, Ohio, USA.Athena Project-Team, INRIA Sophia Antipolis-Méditerranée, Sophia-Antipolis Cedex, France.Department of Computer Science, Sherbrooke Connectivity Imaging Laboratory, Université de Sherbrooke, Sherbrooke, Quebec, Canada.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

24478106

Citation

Paquette, Michael, et al. "Comparison of Sampling Strategies and Sparsifying Transforms to Improve Compressed Sensing Diffusion Spectrum Imaging." Magnetic Resonance in Medicine, vol. 73, no. 1, 2015, pp. 401-16.
Paquette M, Merlet S, Gilbert G, et al. Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging. Magn Reson Med. 2015;73(1):401-16.
Paquette, M., Merlet, S., Gilbert, G., Deriche, R., & Descoteaux, M. (2015). Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging. Magnetic Resonance in Medicine, 73(1), pp. 401-16. doi:10.1002/mrm.25093.
Paquette M, et al. Comparison of Sampling Strategies and Sparsifying Transforms to Improve Compressed Sensing Diffusion Spectrum Imaging. Magn Reson Med. 2015;73(1):401-16. PubMed PMID: 24478106.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging. AU - Paquette,Michael, AU - Merlet,Sylvain, AU - Gilbert,Guillaume, AU - Deriche,Rachid, AU - Descoteaux,Maxime, Y1 - 2014/01/29/ PY - 2013/02/16/received PY - 2013/11/21/revised PY - 2013/12/02/accepted PY - 2014/1/31/entrez PY - 2014/1/31/pubmed PY - 2016/8/6/medline KW - compressive sensing KW - diffusion spectrum imaging KW - diffusion-weighted imaging KW - ensemble average propagator KW - kurtosis KW - orientation distribution function SP - 401 EP - 16 JF - Magnetic resonance in medicine JO - Magn Reson Med VL - 73 IS - 1 N2 - PURPOSE: 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. CONCLUSION: 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). SN - 1522-2594 UR - https://www.unboundmedicine.com/medline/citation/24478106/Comparison_of_sampling_strategies_and_sparsifying_transforms_to_improve_compressed_sensing_diffusion_spectrum_imaging_ L2 - https://doi.org/10.1002/mrm.25093 DB - PRIME DP - Unbound Medicine ER -