Constrained spherical deconvolution of nonspherically sampled diffusion <scp>MRI</scp> data

Morez, Jan; Sijbers, Jan; Vanhevel, Floris; Jeurissen, Ben

doi:10.1002/hbm.25241

Cited by 17 publications

(9 citation statements)

References 49 publications

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“…Therefore, by analyzing changes in parameters such as Fractional Anisotropy, it might be possible to additionally infer on the state of the examined nerves [28][29][30]. To obtain sufficient precision of the research, it will be important to use suitable methods to eliminate spatial systematic errors [31,32].…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Nerve Ultrasound Abnormalities in POEMS Syndrome—A Comparative Study

et al. 2021

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Background: High-resolution nerve ultrasound (HRUS) has been proven to be a valuable tool in the diagnosis of immune-mediated neuropathies, such as chronic inflammatory demyelinating polyradiculoneuropathy (CIDP). POEMS syndrome (polyneuropathy, organomegaly, endocrinopathy, M-protein, skin changes) is an important differential diagnosis of CIDP. Until now, there have been no studies that could identify specific HRUS abnormalities in POEMS syndrome patients. Thus, the aim of this study was to assess possible changes and compare findings with CIDP patients. Methods: We retrospectively analyzed HRUS findings in three POEMS syndrome and ten CIDP patients by evaluating cross-sectional nerve area (CSA), echogenicity and additionally calculating ultrasound pattern scores (UPSA, UPSB, UPSC and UPSS) and homogeneity scores (HS). Results: CIDP patients showed greater CSA enlargement and higher UPSS (median 14 vs. 11), UPSA (median 11.5 vs. 8) and HS (median 5 vs. 3) compared with POEMS syndrome patients. However, every POEMS syndrome patient illustrated enlarged nerves exceeding reference values, which were not restricted to entrapment sites. In CIDP and POEMS syndrome, heterogeneous enlargement patterns could be identified, such as inhomogeneous, homogeneous and regional nerve enlargement. HRUS in CIDP patients visualized both increased and decreased echointensity, while POEMS syndrome patients pictured hypoechoic nerves with hyperechoic intraneural connective tissue. Discussion: This is the first study to demonstrate HRUS abnormalities in POEMS syndrome outside of common entrapment sites. Although nerve enlargement was more prominent in CIDP, POEMS syndrome patients revealed distinct echogenicity patterns, which might aid in its differentiation from CIDP. Future studies should consider HRUS and its possible role in determining diagnosis, prognosis and treatment response in POEMS syndrome.

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Section: Discussionmentioning

confidence: 99%

High-Resolution Nerve Ultrasound Abnormalities in POEMS Syndrome—A Comparative Study

et al. 2021

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“…When fitting data acquired with strong diffusion weighting, it might be beneficial to take into account the presence of a minimum signal offset due to Rician noise in the measurements (Gudbjartsson and Patz 1995; Basu, Fletcher, and Whitaker 2006). One of the submissions considered in this work extended the DKI method with an offset term to account for such effect (DKI+Offset)(Morez et al 2020). This method was fit to SDE data by extending the classic DKI model with an additional degrees of freedom (22 + 1 = 23 free parameters), and to DDE and DODE data by extending a fourth order covariance tensor (28 + 1 = 29 free parameters) (C.-F. Westin et al 2016).…”

Section: Methodsmentioning

confidence: 99%

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Luca

Ianuş

Leemans

et al. 2021

Preprint

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Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

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“…Nonetheless, we acknowledge that the LMM approach could also be applied to multi‐shell data acquired with a single diffusion time, recognizing that the sensitivity to length scales across restricted and hindered compartments may be more limited. For the analysis of data acquired with nonspherical sampling, either Cartesian sampling or due to gradient nonlinearities, the single‐fiber response functions could be adopted to also describe their radial dependency, similar to (Morez et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Linear multi‐scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

Wichtmann

Fan

Eskandarian

et al. 2022

Human Brain Mapping

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Diffusion‐weighted magnetic resonance imaging (DW‐MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra‐high b ‐values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non‐Gaussian response functions, in an extended analysis framework called linear multi‐scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation‐specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi‐shell, multi‐diffusion time DW‐MRI data acquired with a state‐of‐the‐art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

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Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

Cited by 17 publications

References 49 publications

High-Resolution Nerve Ultrasound Abnormalities in POEMS Syndrome—A Comparative Study

High-Resolution Nerve Ultrasound Abnormalities in POEMS Syndrome—A Comparative Study

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

Linear multi‐scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

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