Comparison of basis functions and q‐space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging

Alexandra Tobisch, Thomas Schultz, Rüdiger Stirnberg, Gabriel Varela-Mattatall, Hans Knutsson, Pablo Irarrázaval, and Tony Stöcker
In: NMR in Biomedicine (2019)
 

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.

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Bibtex

@ARTICLE{Tobisch:NBM2019,
    author = {Tobisch, Alexandra and Schultz, Thomas and Stirnberg, R{\"u}diger and Varela-Mattatall, Gabriel and
              Knutsson, Hans and Irarr{\'a}zaval, Pablo and St{\"o}cker, Tony},
     title = {Comparison of basis functions and q‐space sampling schemes for robust compressed sensing
              reconstruction accelerating diffusion spectrum imaging},
   journal = {NMR in Biomedicine},
      year = {2019},
      note = {Early View},
  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.},
       doi = {10.1002/nbm.4055}
}