Q-space trajectory imaging with positivity constraints (QTI+)

Herberthson, Magnus; Boito, Deneb; Haije, T.C.J. Dela; Feragen, Aasa; Westin, Carl‐Fredrik; Özarslan, Evren

doi:10.1016/j.neuroimage.2021.118198

Cited by 12 publications

(22 citation statements)

References 73 publications

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“…Indeed, guaranteeing the physicality of the estimates of the tensor parameters reduces the size of the search space, thus improving the noise resilience of the estimator. Imposing semi‐positive definiteness on the diffusion and covariance tensors has recently been proposed by Herberthson et al ( 2021 ), and they demonstrated that these constraints can substantially improve the quality of scalar DTD parameter maps, even on very limited data.…”

Section: Discussionmentioning

confidence: 99%

“…An iteratively weighted linear least squares (IWLLS) estimator with its weights based on the squared inverse of the predicted signal is expected to provide more accurate and precise parameter estimates compared to WLLS and even nonlinear least squares (NLS; Veraart et al, 2013 ). Moreover, imposing certain constraints that follow from the physics of diffusion has the potential to dramatically improve the precision of DTD parameters (Basser & Pajevic, 2007 ; Herberthson et al, 2021 ; Tabesh et al, 2011 ; Veraart et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

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Optimal experimental design and estimation for q‐space trajectory imaging

Morez

Szczepankiewicz

Dekker

et al. 2022

Human Brain Mapping

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Tensor‐valued diffusion encoding facilitates data analysis by q‐space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision‐optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal experimental design and estimation for q‐space trajectory imaging

Morez

Szczepankiewicz

Dekker

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…We obtained QTI metrics using the implementation in QTI+ [18]. The original QTI implementation is biased to very complex microstructure [6], while QTI+ provides a more stable solution to the DTD fitting optimization problem and achieves smoother and more precise maps than the standard QTI implementation.…”

Section: Methodsmentioning

confidence: 99%

“…Given the long spin-echo based acquisition, the obtained images do not present many artifacts. The only preprocessing step needed was denoising, as the high b-value shells (4 ms/µm 2 ) are noisy, which we achieved through Marčenko-Pastur PCA [14,15] We obtained QTI metrics using the implementation in QTI+ [18]. The original QTI implementation is biased to very complex microstructure [6], while QTI+ provides a more stable solution to the DTD fitting optimization problem and achieves smoother and more precise maps than the standard QTI implementation.…”

Section: Image Data Preprocessingmentioning

confidence: 99%

“…We fit the QTI method to the obtained b-tensor encoding images and extract eight microstructural metrics. Four of them capture the macroscopic behavior of the DTD ensemble and are akin to those from diffusion tensor imaging (DTI) [17] We obtained QTI metrics using the implementation in QTI+ [18]. The original QTI implementation is biased to very complex microstructure [6], while QTI+ provides a more stable solution to the DTD fitting optimization problem and achieves smoother and more precise maps than the standard QTI implementation.…”

Section: Analysis Of B-tensor Encoded Dw-mrimentioning

confidence: 99%

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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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Cardiac q‐space trajectory imaging by motion‐compensated tensor‐valued diffusion encoding in human heart in vivo

Teh

Shelley

Boyle

et al. 2023

Magnetic Resonance in Med

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Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo. Methods:Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (C c ).A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time.Results: QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm 2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and C c = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect. Conclusion:We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and C c . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization.

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Q-space trajectory imaging with positivity constraints (QTI+)

Cited by 12 publications

References 73 publications

Optimal experimental design and estimation for q‐space trajectory imaging

Optimal experimental design and estimation for q‐space trajectory imaging

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

Cardiac q‐space trajectory imaging by motion‐compensated tensor‐valued diffusion encoding in human heart in vivo

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