| 1. |
- de Almeida Martins, João P., et al.
(författare)
-
Computing and visualising intra-voxel orientation-specific relaxation–diffusion features in the human brain
- 2021
-
Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 42:2, s. 310-328
-
Tidskriftsartikel (refereegranskat)abstract
- Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation–diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo-times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation–diffusion distributions where contributions from different sub-voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre-specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation-specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre-tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways.
|
|
| 2. |
- Martin, Jan, et al.
(författare)
-
Nonparametric D-R1-R2 distribution MRI of the living human brain
- 2021
-
Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119. ; 245
-
Tidskriftsartikel (refereegranskat)abstract
- Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R1 and D-R2 correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R2 or R1 in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R1-R2 correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R1-R2 correlation MRI across 633 individual measurements within 25 min—a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R1-R2 distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R1-, and R2-weightings based on the rich information contained in the D-R1-R2 distributions.
|
|
| 3. |
- Naranjo, Isaac Daimiel, et al.
(författare)
-
Multidimensional diffusion magnetic resonance imaging for characterization of tissue microstructure in breast cancer patients : A prospective pilot study
- 2021
-
Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 13:7
-
Tidskriftsartikel (refereegranskat)abstract
- Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.
|
|
| 4. |
- Reymbaut, Alexis, et al.
(författare)
-
Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms
- 2020
-
Ingår i: NMR in Biomedicine. - : Wiley. - 0952-3480 .- 1099-1492. ; 33:12
-
Tidskriftsartikel (refereegranskat)abstract
- In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor-valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one-dimensional Gamma fit, a generalized two-term cumulant approach, and two-dimensional and four-dimensional Monte-Carlo-based inversion techniques, using a clinically feasible tensor-valued acquisition scheme. In particular, we compare their performance at different signal-to-noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two-dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite-SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite-SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two-dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four-dimensional Monte Carlo inversion presents no infinite-SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.
|
|
| 5. |
- Reymbaut, Alexis, et al.
(författare)
-
Diffusion Anisotropy and Tensor-valued Encoding
- 2020
-
Ingår i: Advanced Diffusion Encoding Methods in MRI. - Cambridge : Royal Society of Chemistry. - 2044-253X .- 2044-2548. - 9781788017268 - 9781788019910 ; 2020-January:24, s. 68-102
-
Bokkapitel (refereegranskat)abstract
- The study of diffusion anisotropy via diffusion NMR was pioneered 40 years ago in systems pertaining to the field of porous media. Since then, its combination with MRI has attracted overwhelming interest within the neuroscience community as a way to perform non-invasive in vivo assessment of tissue microstructure. However, most conventional diffusion MRI techniques measured diffusion anisotropy metrics that are not free from the confounding effects of intra-voxel orientational ordering. In this chapter, we introduce and link two major concepts that enable the extraction of metrics, teasing apart diffusion anisotropy and orientational order: diffusion tensor distributions and tensor-valued diffusion encoding. In particular, we explain how the statistical descriptors of diffusion tensor distributions quantify relevant physical characteristics of the voxel content, such as the mean diffusivity, the variance in isotropic diffusivities, the mean anisotropy, and the orientational order. We also discuss how tensor-valued diffusion encoding enables isolating and combining different pieces of diffusion information, ultimately allowing for robust estimations of the aforementioned statistical descriptors. We then review signal inversion methods drawing from tensor-valued encoding and compare them in silico. We finally conclude by suggesting future research directions.
|
|
| 6. |
- Reymbaut, Alexis, et al.
(författare)
-
Magic DIAMOND : Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding
- 2021
-
Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 70
-
Tidskriftsartikel (refereegranskat)abstract
- Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this “Magic DIAMOND” model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
|
|
| 7. |
- Reymbaut, Alexis, et al.
(författare)
-
Toward nonparametric diffusion-T1 characterization of crossing fibers in the human brain
- 2021
-
Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 0740-3194 .- 1522-2594. ; 85:5, s. 2815-2827
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: To estimate (Formula presented.) for each distinct fiber population within voxels containing multiple brain tissue types. Methods: A diffusion- (Formula presented.) correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions (Formula presented.) of diffusion tensors and longitudinal relaxation rates (Formula presented.). Orientation distribution functions (ODFs) of the highly anisotropic components of (Formula presented.) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles. Results: Parameter maps corresponding to (Formula presented.) ’s statistical descriptors were obtained, exhibiting the expected (Formula presented.) contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in (Formula presented.) between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup. Conclusions: Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- (Formula presented.) features, thereby showing potential for characterizing developmental or pathological changes in (Formula presented.) within a given fiber bundle, and for investigating interbundle (Formula presented.) differences.
|
|
