Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

Mani, Merry; Jacob, Mathews; Guidon, Arnaud; Magnotta, Vincent A.; Zhong, Jianhui

doi:10.1002/mrm.25119

Cited by 47 publications

(79 citation statements)

References 57 publications

(91 reference statements)

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“…As presented in simulations, our spatial prior on the FOD outperforms as well the empirical TV regularisation of q-435 space images proposed by Mani et al (2014), being able to recover the fibre orientation distribution using fewer samples. Note that spatial regularisation of the q-space images is actually complementary to our formulation and could be added as an additional prior to our method.…”

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confidence: 71%

“…For instance, propose a variational formulation to jointly estimate and regularise DTI to account for the effect of Rician noise in low SNR regimes, while Mani et al (2014);Michailovich et al (2011) use the standard state-of-the-art minimisation of the total variation (TV) 55 semi-norm (Rudin et al, 1992) of the diffusion images.…”

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confidence: 99%

“…While the spatial regularisation schemes proposed by ; Mani et al (2014); Michailovich et al (2011) enforce sparsity of the images at each q-space point, our spatial regularisation relates to the fundamental coherence between fibre directions -the FOD -in neighbour voxels, thus adding anatomically driven constraints. Our code is available at https://github.com/basp-group/co-dmri and it is distributed open-source.…”

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confidence: 99%

“…We pay special attention to the comparison with the rest of methods that promote any kind of spatial regularisation. L2L0 NW with 15 samples (SR ∠ = 85 and mean(θ) = 6.4 • ) outperforms L2-L1-TV, the method imposing TV 355 regularisation (Daducci et al (2014a); see also Mani et al (2014)), in terms of success rate (SR ∠ = 75) and present similar average angular error (mean(θ) = 6 • ), stressing the fact that the latter uses a sampling scheme with the double number of points (30 samples). Overall, we point out that all participant methods imposing spatial regularisation (L2-L1-TV, L2-L1-TGV) use a significant amount of measurements (from 360 30 to 64 points) to recover the fibre configuration.…”

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confidence: 99%

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Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI

et al. 2015

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We propose a novel formulation to solve the problem of intra-voxel reconstruction of the fibre orientation distribution function (FOD) in each voxel of the white matter of the brain from diffusion MRI data. The majority of the state-of-the-art methods in the field perform the reconstruction on a voxel-by-voxel level, promoting sparsity of the orientation distribution. Recent methods have proposed a global denoising of the diffusion data using spatial information prior to reconstruction, while others promote spatial regularisation through an additional empirical prior on the diffusion image at each qspace point. Our approach reconciles voxelwise sparsity and spatial regularisation and defines a spatially structured FOD sparsity prior, where the structure originates from the spatial coherence of the fibre orientation between neighbour voxels. The method is shown, through both simulated and real data, to enable accurate FOD reconstruction from a much lower number of q-space samples than the state of the art, typically 15 samples, even for quite adverse noise conditions.

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confidence: 71%

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Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI

et al. 2015

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“…More recently, Mani et al [11] proposed compressed sensing in ( k, q )-space by jointly under-sampling k and q spaces. This was achieved by under-sampling the k -space randomly for each direction q .…”

Section: Introductionmentioning

confidence: 99%

Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in $$({\mathbf {k}},{\mathbf {q}})$$ -Space

Sun

Sakhaee

Entezari

et al. 2015

Lecture Notes in Computer Science

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Compressed Sensing (CS) for the acceleration of MR scans has been widely investigated in the past decade. Lately, considerable progress has been made in achieving similar speed ups in acquiring multi-shell high angular resolution diffusion imaging (MS-HARDI) scans. Existing approaches in this context were primarily concerned with sparse reconstruction of the diffusion MR signal S(q) in the q-space. More recently, methods have been developed to apply the compressed sensing framework to the 6-dimensional joint (k, q)-space, thereby exploiting the redundancy in this 6D space. To guarantee accurate reconstruction from partial MS-HARDI data, the key ingredients of compressed sensing that need to be brought together are: (1) the function to be reconstructed needs to have a sparse representation, and (2) the data for reconstruction ought to be acquired in the dual domain (i.e., incoherent sensing) and (3) the reconstruction process involves a (convex) optimization. In this paper, we present a novel approach that uses partial Fourier sensing in the 6D space of (k, q) for the reconstruction of P(x, r). The distinct feature of our approach is a sparsity model that leverages surfacelets in conjunction with total variation for the joint sparse representation of P(x, r). Thus, our method stands to benefit from the practical guarantees for accurate reconstruction from partial (k, q)-space data. Further, we demonstrate significant savings in acquisition time over diffusion spectral imaging (DSI) which is commonly used as the benchmark for comparisons in reported literature. To demonstrate the benefits of this approach, we present several synthetic and real data examples.

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Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography

Schilling

Tax

Rheault

et al. 2021

Human Brain Mapping

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Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple

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Acceleration of high angular and spatial resolution diffusion imaging using compressed sensing with multichannel spiral data

Abstract: The proposed scheme can significantly accelerate the acquisition of high spatial and angular resolution diffusion imaging by accurately reconstructing crossing fiber architectures from under-sampled data.

Cited by 47 publications

References 57 publications

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI

Structured sparsity for spatially coherent fibre orientation estimation in diffusion MRI

Leveraging EAP-Sparsity for Compressed Sensing of MS-HARDI in $$({\mathbf {k}},{\mathbf {q}})$$ -Space

Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography

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