Model-Based Super-Resolution of Diffusion MRI

Tobisch, Alexandra; Neher, Peter; Rowe, Matthew; Maier‐Hein, Klaus H.; Zhang, Hui

doi:10.1007/978-3-319-02475-2_3

Cited by 8 publications

(12 citation statements)

References 16 publications

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“…Instead of performing the reconstruction of each DW image independently [8, 6], we account for the correlations between DW images by incorporating the knowledge of a local tissue model. Instead of an ad-hoc coupling [12], we formalize the simultaneous HR reconstruction and tissue model estimation with a joint probabilistic model from which interactions between the two processes naturally emerge. Importantly, and unlike [8, 6, 12], our framework enables reconstruction from snapshots with different subsets of diffusion gradients.…”

Section: Discussionmentioning

confidence: 99%

“…Instead of an ad-hoc coupling [12], we formalize the simultaneous HR reconstruction and tissue model estimation with a joint probabilistic model from which interactions between the two processes naturally emerge. Importantly, and unlike [8, 6, 12], our framework enables reconstruction from snapshots with different subsets of diffusion gradients. This enables reconstruction from acquisitions in which snapshots are missing, for example due to corruption by intra-scan motion.…”

Section: Discussionmentioning

confidence: 99%

“…Tobisch et al [12] built upon the work in [8, 6] and proposed to introduce an ad-hoc coupling between HR reconstruction and tissue model estimation to capture the coupling between DW images. They considered the ball-and-stick tissue model at each voxel, thereby assuming 1) the presence of a single fascicle in each voxel; 2) the absence of radial diffusivity; and 3) a prefixed axial diffusivity value constant for the entire brain.…”

Section: Introductionmentioning

confidence: 99%

“…This is critical because, when HR reconstruction and tissue model estimation are coupled, the ability of the tissue model to accurately predict the DW signal for a diffusion gradient conditions the ultimate HR reconstruction accuracy. In [12], only results with synthetic simulations were reported, but no evidence of the technical efficacy of the technique was reported with in vivo data. More importantly, and similarly to [8, 6], this technique required the successful acquisition of all the snapshots for a diffusion gradient to reconstruct the corresponding HR gradient image.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of an ad-hoc coupling [12], our approach is formalized as a joint probabilistic model with missing observations, from which interactions between missing snapshots, HR reconstruction and a generic tissue model naturally emerge. We describe the tissue microstructure at a voxel with a diffusion compartment imaging (DCI) tissue model that reflects the presence of tissue compartments in each voxel, providing a model-based description of the signal attenuation for any diffusion gradient orientation and strength.…”

Section: Introductionmentioning

confidence: 99%

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Accelerated High Spatial Resolution Diffusion-Weighted Imaging

Scherrer¹,

Afacan²,

Taquet³

et al. 2015

Lecture Notes in Computer Science

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Acquisition of a series of anisotropically oversampled acquisitions (so-called anisotropic “snapshots”) and reconstruction in the image space has recently been proposed to increase the spatial resolution in diffusion weighted imaging (DWI), providing a theoretical 8× acceleration at equal signal-to-noise ratio (SNR) compared to conventional dense k-space sampling. However, in most works, each DW image is reconstructed separately and the fact that the DW images constitute different views of the same anatomy is ignored. In addition, current approaches are limited by their inability to reconstruct a high resolution (HR) acquisition from snapshots with different subsets of diffusion gradients: an isotropic HR gradient image cannot be reconstructed if one of its anisotropic snapshots is missing, for example due to intra-scan motion, even if other snapshots for this gradient were successfully acquired. In this work, we propose a novel multi-snapshot DWI reconstruction technique that simultaneously achieves HR reconstruction and local tissue model estimation while enabling reconstruction from snapshots containing different subsets of diffusion gradients, providing increased robustness to patient motion and potential for acceleration. Our approach is formalized as a joint probabilistic model with missing observations, from which interactions between missing snapshots, HR reconstruction and a generic tissue model naturally emerge. We evaluate our approach with synthetic simulations, simulated multi-snapshot scenario and in vivo multi-snapshot imaging. We show that 1) our combined approach ultimately provides both better HR reconstruction and better tissue model estimation and 2) the error in the case of missing snapshots can be quantified. Our novel multi-snapshot technique will enable improved high spatial characterization of the brain connectivity and microstructure in vivo.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accelerated High Spatial Resolution Diffusion-Weighted Imaging

Scherrer¹,

Afacan²,

Taquet³

et al. 2015

Lecture Notes in Computer Science

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Super‐resolution reconstruction of diffusion parameters from diffusion‐weighted images with different slice orientations

Steenkiste

Jeurissen

Veraart

et al. 2015

Magnetic Resonance in Med

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Purpose: Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the tradeoff between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. Method: An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusionweighted images, optimally samples the q-and k-space, and performs motion correction with b-matrix rotation.Results: Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-meansquare error.

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Diffusion MRI fiber tractography of the brain

et al. 2017

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The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.

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Model-Based Super-Resolution of Diffusion MRI

Cited by 8 publications

References 16 publications

Accelerated High Spatial Resolution Diffusion-Weighted Imaging

Accelerated High Spatial Resolution Diffusion-Weighted Imaging

Super‐resolution reconstruction of diffusion parameters from diffusion‐weighted images with different slice orientations

Diffusion MRI fiber tractography of the brain

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