Massively Multidimensional Diffusion-Relaxation Correlation MRI

Narvaez, Omar; Svenningsson, Leo; Yon, Maxime; Sierra, Alejandra; Topgaard, Daniel

doi:10.3389/fphy.2021.793966

Cited by 13 publications

(29 citation statements)

References 104 publications

(179 reference statements)

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“…Like the diffusion tensor, QTI is a signal representation [36]. There are other interesting avenues of analysis like Diffusion Tensor Distribution imaging [37] that can extract direct DTD features or even extend it to multidimensional MRI analysis to capture relaxometry effects [38,39]. Approaches with biophysical models using b-tensor encoding [40,41] can be used to extract microstructural properties that cannot be obtained without strong modeling assumptions using single diffusion encoding acquisitions.…”

Section: Discussionmentioning

confidence: 99%

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Rios-Carrillo

Ramírez-Manzanares

Luna-Munguía

et al. 2023

Preprint

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Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different forms of white matter damage in rodent optic nerves. 2) We obtained ex-vivo DW-MRI with b-tensor encoding schemes and calculated quantitative metrics using Q-space Trajectory Imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.

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Section: Discussionmentioning

confidence: 99%

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Rios-Carrillo

Ramírez-Manzanares

Luna-Munguía

et al. 2023

Preprint

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“…We expect this to also be the case for tumor-related hyperintensities in T2w images but are not aware of any detailed analysis. Note that diffusion-relaxometry techniques could be of interest here to add microstructure specificity ( De Almeida Martins and Topgaard, 2018 ; Slator et al, 2021 ; Narvaez et al, 2022 ). This was, however, outside the aim of the present work, which was to study the contrast mechanism provided by STE-DWI without modeling or optimizing the imaging protocol.…”

Section: Discussionmentioning

confidence: 99%

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding

et al. 2022

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BackgroundTumor-related hyperintensities in high b-value diffusion-weighted imaging (DWI) are radiologically important in the workup of gliomas. However, the white matter may also appear as hyperintense, which may conflate interpretation.PurposeTo investigate whether DWI with spherical b-tensor encoding (STE) can be used to suppress white matter and enhance the conspicuity of glioma hyperintensities unrelated to white matter.Materials and MethodsTwenty-five patients with a glioma tumor and at least one pathology-related hyperintensity on DWI underwent conventional MRI at 3 T. The DWI was performed both with linear and spherical tensor encoding (LTE-DWI and STE-DWI). The LTE-DWI here refers to the DWI obtained with conventional diffusion encoding and averaged across diffusion-encoding directions. Retrospectively, the differences in contrast between LTE-DWI and STE-DWI, obtained at a b-value of 2,000 s/mm2, were evaluated by comparing hyperintensities and contralateral normal-appearing white matter (NAWM) both visually and quantitatively in terms of the signal intensity ratio (SIR) and contrast-to-noise ratio efficiency (CNReff).ResultsThe spherical tensor encoding DWI was more effective than LTE-DWI at suppressing signals from white matter and improved conspicuity of pathology-related hyperintensities. The median SIR improved in all cases and on average by 28%. The median (interquartile range) SIR was 1.9 (1.6 – 2.1) for STE and 1.4 (1.3 – 1.7) for LTE, with a significant difference of 0.4 (0.3 –0.5) (p < 10–4, paired U-test). In 40% of the patients, the SIR was above 2 for STE-DWI, but with LTE-DWI, the SIR was below 2 for all patients. The CNReff of STE-DWI was significantly higher than of LTE-DWI: 2.5 (2 – 3.5) vs. 2.3 (1.7 – 3.1), with a significant difference of 0.4 (−0.1 –0.6) (p < 10–3, paired U-test). The STE improved CNReff in 70% of the cases. We illustrate the benefits of STE-DWI in three patients, where STE-DWI may facilitate an improved radiological description of tumor-related hyperintensity, including one case that could have been missed out if only LTE-DWI was inspected.ConclusionThe contrast mechanism of high b-value STE-DWI results in a stronger suppression of white matter than conventional LTE-DWI, and may, therefore, be more sensitive and specific for assessment of glioma tumors and DWI-hyperintensities.

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“…By numerical optimizations to maximize the b-value for given gradient strength Sjölund et al, 2015), mitigating image artifacts from eddy currents (Yang and McNab, 2019) and concomitant gradients (Szczepankiewicz et al, 2019), and further minimizing side-lobes in the encoding spectra (Hennel et al, 2020), we anticipate that the waveforms may be adapted for human in vivo studies. The merging of oscillating gradients (Aggarwal, 2020) and tensor-valued encoding (Reymbaut, 2020) into a common acquisition protocol encourages further development of a joint analysis framework, for instance by augmenting current nonparametric diffusion tensor distributions (Topgaard, 2019a) with a Lorentzian frequency dimension (Narvaez et al, 2021;Narvaez et al, 2022) or building on the concept of confinement tensors (Yolcu et al, 2016;Boito et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Multidimensional encoding of restricted and anisotropic diffusion by double rotation of the q-vector

Hong

Svenningsson

Topgaard

2022

Preprint

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Abstract. Diffusion NMR and MRI methods building on the classic pulsed gradient spin echo sequence are sensitive to many aspects of translational motion, including time/frequency-dependence (“restriction”), anisotropy, and flow, which leads to ambiguities when interpreting experimental data from complex heterogeneous materials such as living biological tissues. Higher specificity to restriction or anisotropy can be obtained with, respectively, oscillating gradient or tensor-valued encoding which nevertheless both have some sensitivity to the property not being of direct interest. Here we propose a simple scheme derived from the “double rotation” technique in solid-state NMR to generate a family of modulated gradient waveforms allowing for comprehensive exploration of the two-dimensional frequency-anisotropy space and convenient investigation of both restricted and anisotropic diffusion with a single multidimensional acquisition protocol. The method is demonstrated by measuring multicomponent isotropic Gaussian diffusion in simple liquids, anisotropic Gaussian diffusion in a polydomain lyotropic liquid crystal, and restricted diffusion in a yeast cell sediment.

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Massively Multidimensional Diffusion-Relaxation Correlation MRI

Cited by 13 publications

References 104 publications

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Separating Glioma Hyperintensities From White Matter by Diffusion-Weighted Imaging With Spherical Tensor Encoding

Multidimensional encoding of restricted and anisotropic diffusion by double rotation of the q-vector

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