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Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging.
NMR Biomed 2019; 32(3):e4055NB

Abstract

Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.

Authors+Show Affiliations

German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. Department of Computer Science, University of Bonn, Germany.Department of Computer Science, University of Bonn, Germany. Bonn-Aachen International Center for Information Technology, University of Bonn, Germany.German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile. Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.Linköping University, Linköping, Sweden.Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile. Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. Department of Physics and Astronomy, University of Bonn, Germany.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30637831

Citation

Tobisch, Alexandra, et al. "Comparison of Basis Functions and Q-space Sampling Schemes for Robust Compressed Sensing Reconstruction Accelerating Diffusion Spectrum Imaging." NMR in Biomedicine, vol. 32, no. 3, 2019, pp. e4055.
Tobisch A, Schultz T, Stirnberg R, et al. Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging. NMR Biomed. 2019;32(3):e4055.
Tobisch, A., Schultz, T., Stirnberg, R., Varela-Mattatall, G., Knutsson, H., Irarrázaval, P., & Stöcker, T. (2019). Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging. NMR in Biomedicine, 32(3), pp. e4055. doi:10.1002/nbm.4055.
Tobisch A, et al. Comparison of Basis Functions and Q-space Sampling Schemes for Robust Compressed Sensing Reconstruction Accelerating Diffusion Spectrum Imaging. NMR Biomed. 2019;32(3):e4055. PubMed PMID: 30637831.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging. AU - Tobisch,Alexandra, AU - Schultz,Thomas, AU - Stirnberg,Rüdiger, AU - Varela-Mattatall,Gabriel, AU - Knutsson,Hans, AU - Irarrázaval,Pablo, AU - Stöcker,Tony, Y1 - 2019/01/14/ PY - 2018/04/20/received PY - 2018/11/06/revised PY - 2018/11/13/accepted PY - 2019/1/15/pubmed PY - 2019/1/15/medline PY - 2019/1/15/entrez KW - basis functions KW - compressed sensing KW - diffusion MRI KW - diffusion spectrum imaging KW - microstructure KW - q-space undersampling KW - sparse acquisition SP - e4055 EP - e4055 JF - NMR in biomedicine JO - NMR Biomed VL - 32 IS - 3 N2 - Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies. SN - 1099-1492 UR - https://www.unboundmedicine.com/medline/citation/30637831/Comparison_of_basis_functions_and_q_space_sampling_schemes_for_robust_compressed_sensing_reconstruction_accelerating_diffusion_spectrum_imaging_ L2 - https://doi.org/10.1002/nbm.4055 DB - PRIME DP - Unbound Medicine ER -