Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

Diffusion microscopic MRI of the mouse embryo: Protocol and practical implementation in the splotch mouse model

Advanced methodologies for visualizing novel tissue contrast are essential for phenotyping the ever-increasing number of mutant mouse embryos being generated. Although diffusion microscopic MRI (μMRI) has been used to phenotype embryos, widespread routine use is limited by extended scanning times, and there is no established experimental procedure ensuring optimal data acquisition

The Influence of Bowel Preparation on ADC Measurements: Comparison between Conventional DWI and DWIBS Sequences

Background and objectives: The aim of the study was to assess whether there were di ff erences between apparent di ff usion coe ffi cient (ADC) values of di ff usion-weighted imaging (DWI) and di ff usion-weighted imaging with background body signal suppression (DWIBS) sequences in non-prepared and prepared bowels before and after preparation with an enteric hyperosmolar agent, to assess whether ADC measurements have the potential to avoid bowel preparation and whether ADC-DWIBS has advantages over ADC-DWI. Materials and Methods: 106 adult patients without evidence of inflammatory bowel disease (IBD) underwent magnetic resonance (MR) enterography before and after bowel preparation. ADC-DWI and ADC-DWIBS values were measured in the intestinal and colonic walls demonstrating high signal intensity (SI) at DWI tracking images of b = 800 s /mm2 before and after preparation. Results: There were significant di ff erence (p < 0.0001) in both ADC-DWI and ADC-DWIBS results between non-prepared and prepared jejunum for DWI being 1.09 x 10 3 mm2 /s and 1.76 x 10 3 mm2 /s, respectively, and for DWIBS being 0.91 x 10 3 mm2 /s and 1.75 x 10 3 mm2 /s, respectively. Both ADC-DWI and DWIBS also showed significant di ff erence between non-prepared and prepared colon (p < 0.0001), with DWI values 1.41 x 10 3 mm2 /s and 2.13 x 10 3 mm2 /s, and DWIBS-1.01 x 10 3mm2 /s and 2.04 x 10 3mm2 /s, respectively. Nosignificant di ff erence between ADC-DWI and ADC-DWIBS was found in prepared jejunum (p = 0.84) and prepared colon (p = 0.58), whereas a significant di ff erence was found in non-prepared jejunum and non-prepared colon (p = 0.0001 in both samples). Conclusions: ADC between DWI and DWIBS does not di ff er in prepared bowel walls but demonstrates a di ff erence in non-prepared bowel. ADC in non-prepared bowel is lower than in prepared bowel and possible overlap with the ADC range of IBD is possible in non-prepared bowel. ADC-DWIBS has no advantage over ADC-DWI in regard to IBD assessment.publishersversionPeer reviewe

RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI

The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time

Comparison between diffusion-weighted sequences with selective and non-selective fat suppression in the evaluation of Crohn's disease activity : are they equally useful?

Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: We compared the efficiency of two MRI diffusion weighted imaging (DWI) techniques: DWI with SPIR (DWI SPIR) and DWI with STIR (DWI STIR), to estimate their eligibility for quantitative assessment of Crohn's disease activity in children and adults. Methods: In inflamed terminal ileum segments ( n = 32 in adults, n = 46 in children), Magnetic Resonance Index of Activity (MaRIA) was calculated, ADC values of both DWI techniques were measured, and the corresponding Clermont scores calculated. ADC values of both DWI techniques were compared between both and within each patient group, assessing their mutual correlation. Correlations between MaRIA and the corresponding ADC values, and Clermont scores based on both DWI techniques were estimated. Results: No correlation between ADC of DWI SPIR and DWI STIR was observed (rho = 0.27, p = 0.13 in adults, rho = 0.20, p = 0.17 in children). The correlation between MaRIA and Clermont scores was strong in both techniques-in SPIR, rho = 0.93; p < 0.0005 in adults, rho = 0.98, p < 0.0005 in children, and, in STIR, rho = 0.89; p < 0.0005 in adults, rho = 0.95, p < 0.0005 in children. The correlation between ADC and MaRIA was moderate negative for DWI STIR (rho = 0.93, p < 0.0005 in adults, rho = 0.95, p < 0.0005 in children), but, in DWI STIR, no correlation between ADC and MaRIA score was observed in adults (rho = -0.001, p = 0.99), whereas children presented low negative correlation (rho = -0.374, p = 0.01). Conclusions: DWI STIR is not suitable for quantitative assessment of Crohn's disease activity both in children and adult patients.publishersversionPeer reviewe

Super resolución (SR) en imágenes de resonancia magnética DWI de cerebro usando estimación bayesiana

En la presente tesis, se propone un método bayesiano de Súper resolución (SR) que obtiene imágenes de alta resolución (HR) DWI a partir de imágenes degradadas de baja resolución (LR), tratando de recuperar un máximo de la información en alta frecuencia. Bajo la formuación bayesiana, la imagen desconocida de alta resolución (HR), el proceso de adquisición y los parámetros del modelo son modelados como procesos estocásticos. El término de verosimilitud es modelado usando una distribución gausiana para estimar el error entre la representación y las observaciones. El término a priori se modela como una distribución gausiana multivariada en el que los pesos del vecindario corresponden a variables intermedias que se introducen con dos propósitos: modelar las relaciones locales con una distribución Laplaciana y utilizar la información más relevante de su vecindario. En consecuencia, la matriz de covarianza de los pesos de este prior se aproxima por variables latentes que se calculan de las relaciones locales modeladas con una Laplaciana. Los resultados experimentales muestran que el método supera la línea base por 2.56 dB usando como métrica el PSNR para una colección de 35 casos.Abstract: In this thesis, a Bayesian super resolution (SR) method obtains high resolution (HR) brain Diffusion-Weighted Magnetic Resonance Imaging (DMRI) images from degraded low resolution (LR) images. Under a Bayesian formulation, the unknown HR image, the acquisition process and the unknown parameters are modeled as stochastic processes. The likelihood model is modeled using a Gaussian distribution to estimate the error between the representation and the observations. The prior is introduced as a Multivariate Gaussian Distribution, for which the inverse of the covariance matrix is approximated by Laplacian-like functions that model the local relationships, capturing thereby non-homogeneous relationships between neighbor intensities. Experimental results show the method outperforms the base line by 2.56 dB when using PSNR as a metric of quality in a set of 35 cases.Maestrí

Fast high spatio-angular resolution estimation of the neuronal fiber orientations in the brain with diffusion MRI

The human brain is a very complex organ. The white matter tissue composing the brain is made of threadlike structures, called axons, which are responsible for the transmission of the impulses between different areas of the brain. Axons are extremely fine and cannot be visualized by any in vivo imaging technique, leaving a lot to explore about which brain regions are connected and how information is carried through these structures. Diffusion Magnetic Resonance Imaging (dMRI) is a unique technique that allows to investigate the inner structures of the brain in vivo and in a non-invasive way. High spatio-angular resolution dMRI techniques have been shown to provide accurate fiber reconstructions even in the presence of complex fiber configurations. However, high resolution techniques are characterized by long acquisition times, which hamper their application into the clinical practice. In this manuscript we present a novel method to recover the fiber orientation distribution (FOD) of the bundles of axons at high spatio-angular resolution via practical kq-space under-sampling that enables both acceleration and super-resolution. The quality of the recovered fibers is preserved by making use of advanced anatomical priors for the FOD reconstruction. Prior knowledge of the spatial distribution of the white matter, the gray matter and the cerebrospinal fluid is taken into account for the recovery of the FOD coefficients. In addition, the simultaneous voxelwise sparsity and spatial smoothness of fiber orientations is accounted for by means of a structured sparsity prior. A convex minimization problem is formulated and solved via an accelerated stochastic Forward-Backward algorithm. Simulations show that the proposed method outperforms state-of-the-art kq-space approaches in terms of reconstruction quality. Real data analysis suggests that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes, potentially enabling the application of high spatio-angular resolution dMRI into the clinical practice