Super-resolution in brain Diffusion Weighted Imaging (DWI)

Abstract. Diffusion Weighted (DW) imaging has proven to be useful at analysing brain architecture as well as at establishing brain tract organization and neuronal connectivity. However, an actual clinical use of DW images is currently limited by a series of acquisition artifacts, among them the partial volume effect (PVE) that may completely alter the spatial resolution and therefore the visualization of microanatomical details. In this work, a new superresolution method will be presented, taking advantage of the redundant structural patterns that shape the brain. The proposed method couples low-high resolution information and explores different directional spaces that might exploit the spectral content of the DW images. A comparison of this proposal with a classical image interpolation method demostrates an improvement of about 3 dB when using the typical PSNR.Las imágenes de Difusión Ponderada (DWI por sus siglas en inglés) han probado ser de gran utilidad en proceso de análisis de la arquitectura del cerebro y en investigaciones acerca de la organización de tractos y la conectividad neuronal. Sin embargo, el uso clínico de la imágenes DW está limitado actualmente por algunos artefactos propios de la adquisición, tales como el efecto de volumen parcial (PVE), que afecta la resolución espacial y por ende la visualización de detalles microanatómicos. En este trabajo de tesis se presenta un nuevo método de super-resolución que aprovecha lo redundante de los patrones estructurales que dan forma al cerebro el cerebro. El método propuesto acopla información de alta /baja resolución y explora diferentes espacios de representación para las características direccionales y el contenido espectral de las DWI. Una comparación con métodos clásicos de interpolación demuestra una mejora de cerca de 3dB usando como métrica el PSNR.Maestrí

Super-resolution in brain Diffusion Weighted Imaging (DWI)

Abstract. Diffusion Weighted (DW) imaging has proven to be useful at analysing brain architecture as well as at establishing brain tract organization and neuronal connectivity. However, an actual clinical use of DW images is currently limited by a series of acquisition artifacts, among them the partial volume effect (PVE) that may completely alter the spatial resolution and therefore the visualization of microanatomical details. In this work, a new superresolution method will be presented, taking advantage of the redundant structural patterns that shape the brain. The proposed method couples low-high resolution information and explores different directional spaces that might exploit the spectral content of the DW images. A comparison of this proposal with a classical image interpolation method demostrates an improvement of about 3 dB when using the typical PSNR.Las imágenes de Difusión Ponderada (DWI por sus siglas en inglés) han probado ser de gran utilidad en proceso de análisis de la arquitectura del cerebro y en investigaciones acerca de la organización de tractos y la conectividad neuronal. Sin embargo, el uso clínico de la imágenes DW está limitado actualmente por algunos artefactos propios de la adquisición, tales como el efecto de volumen parcial (PVE), que afecta la resolución espacial y por ende la visualización de detalles microanatómicos. En este trabajo de tesis se presenta un nuevo método de super-resolución que aprovecha lo redundante de los patrones estructurales que dan forma al cerebro el cerebro. El método propuesto acopla información de alta /baja resolución y explora diferentes espacios de representación para las características direccionales y el contenido espectral de las DWI. Una comparación con métodos clásicos de interpolación demuestra una mejora de cerca de 3dB usando como métrica el PSNR.Maestrí

Collaborative patch-based super-resolution for diffusion-weighted images

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquis itions. A comparison with classical interpo- lation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in termsofimprovementsonimagereconstruction,fractiona lanisotropy(FA)estimation,generalizedFAandangular reconstruction for tensor and high angular resolut ion diffusion imaging (HARDI) models. Besides, fi rst results of reconstructed ultra high resolution DW images are presented at 0.6 × 0.6 × 0.6 mm 3 and0.4×0.4×0.4mm 3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fi ber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.We thank the reviewers for their useful comments that helped improve the paper. We also want to thank the Pr Louis Collins for proofreading this paper and his fruitful comments. Finally, we want to thank Martine Bordessoules for her help during image acquisition of DWI used to build the phantom. This work has been supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. This work has been also partially supported by the French National Agency for Research (Project MultImAD; ANR-09-MNPS-015-01) and by the Spanish grant TIN2011-26727 from the Ministerio de Ciencia e Innovacion. This work benefited from the use of FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), FiberNavigator (code.google.com/p/fibernavigator/), MRtrix software (http://www. brain.org.au/software/mrtrix/) and ITKsnap (www.itk.org).Coupé, P.; Manjón Herrera, JV.; Chamberland, M.; Descoteaux, M.; Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage. 83:245-261. https://doi.org/10.1016/j.neuroimage.2013.06.030S2452618

Spatially Regularizing High Angular Resolution Diffusion Imaging

Many recent high angular resolution diffusion imaging (HARDI) reconstruction techniques have been introduced to infer ensemble average propagator (EAP),describing the three-dimensional (3D) average diffusion process of water molecules or the angular structure information contained in EAP, orientation distribution function (ODF). Most of these methods perform reconstruction independently at each voxel, which essentially ignoring the functional nature of the HARDI data at different voxels in space. The aim of my thesis is to develop methods which can spatially and adaptively infer the EAP, or ODF of water diffusion in regions with complex fiber configurations. In Chapter 3, we propose a penalized multi-scale adaptive regression model (PMARM) framework to spatially and adaptively infer the ODF of water diffusion in regions with complex fiber configurations. We first represent DW-MRI signals using Spherical Harmonic (SH) basis, then apply PMARM on advanced statistical methods to calculate the coefficients of SH representation, from which ODF representation is calculated using Funk-Radon transformation. PMARM reconstructs the ODF at each voxel by adaptively borrowing the spatial information from the neighboring voxels. We show in the real and simulated data sets that PMARM can substantially reduce the noise level, while improving the ODF reconstruction. In Chapter 4, we propose a robust multi-scale adaptive and sequential smoothing (MASS) method framework to robustly, spatially and adaptively infer the EAP of water diffusion in regions with complex fiber configurations. We first calculate spherical polar Fourier basis representation of the DW-MRI signals, and then apply MASS adaptively and sequentially updating SPF representation by borrowing the spatial information from the neighboring voxels. We show in the real and simulated data sets that MASS can reduce the angle detection errors on fiber crossing area and provides more accurate reconstructions than standard voxel-wise methods and robust MASS performs very well with the presence of outliers. In Chapter 5, we extend multi-scale adaptive method framework to dictionary learning methods, and show that by adding smoothing technique, we can significantly improve the accuracy of EAP reconstruction and reduce the angle detection errors on fiber crossing, even in very low signal-to-noise ratio situation.Doctor of Philosoph

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Super Resolution of HARDI images Using Compressed Sensing Techniques

Effective techniques of inferring the condition of neural tracts in the brain is invaluable for clinicians and researchers towards investigation of neurological disorders in patients. It was not until the advent of diffusion Magnetic Resonance Imaging (dMRI), a noninvasive imaging method used to detect the diffusion of water molecules, that scientists have been able to assess the characteristics of cerebral diffusion in vivo. Among different dMRI methods, High Angular Resolution Diffusion Imaging (HARDI) is well known for striking a balance between ability to distinguish crossing neural fibre tracts while requiring a modest number of diffusion measurements (which is directly related to acquisition time). HARDI data provides insight into the directional properties of water diffusion in cerebral matter as a function of spatial coordinates. Ideally, one would be interested in having this information available at fine spatial resolution while minimizing the probing along different spatial orientations (so as to minimize the acquisition time). Unfortunately, availability of such datasets in reasonable acquisition times are hindered by limitations in current hardware and scanner protocols. On the other hand, post processing techniques prove promising in increasing the effective spatial resolution, allowing more detailed depictions of cerebral matter, while keeping the number of diffusion measurements within a feasible range. In light of the preceding developments, the main purpose of this research is to look into super resolution of HARDI data, using the modern theory of compressed sensing. The method proposed in this thesis allows an accurate approximation of HARDI signals at a higher spatial resolution compared to data obtained with a typical scanner. At the same time, ideas for reducing the number of diffusion measurements in the angular domain to improve the acquisition time are explored. Accordingly, the novel method of applying two distinct compressed sensing approaches in both spatial and angular domain, and combining them into a single framework for performing super resolution forms the main contribution provided by this thesis

Sparse Reconstruction of Compressive Sensing Magnetic Resonance Imagery using a Cross Domain Stochastic Fully Connected Conditional Random Field Framework

Prostate cancer is a major health care concern in our society. Early detection of prostate cancer is crucial in the successful treatment of the disease. Many current methods used in detecting prostate cancer can either be inconsistent or invasive and discomforting to the patient. Magnetic resonance imaging (MRI) has demonstrated its ability as a non-invasive and non-ionizing medical imaging modality with a lengthy acquisition time that can be used for the early diagnosis of cancer. Speeding up the MRI acquisition process can greatly increase the number of early detections for prostate cancer diagnosis. Compressive sensing has exhibited the ability to reduce the imaging time for MRI by sampling a sparse yet sufficient set of measurements. Compressive sensing strategies are usually accompanied by strong reconstruction algorithms. This work presents a comprehensive framework for a cross-domain stochastically fully connected conditional random field (CD-SFCRF) reconstruction approach to facilitate compressive sensing MRI. This approach takes into account original k-space measurements made by the MRI machine with neighborhood and spatial consistencies of the image in the spatial domain. This approach facilitates the difference in domain between MRI measurements made in the k-space, and the reconstruction results in spatial domain. An adaptive extension of the CD-SFCRF approach that takes into account regions of interest in the image and changes the CD-SFCRF neighborhood connectivity based on importance is presented and tested as well. Finally, a compensated CD-SFCRF approach that takes into account MRI machine imaging apparatus properties to correct for degradations and aberrations from the image acquisition process is presented and tested. Clinical MRI data were collected from twenty patients with ground truth data examined and con firmed by an expert radiologist with multiple years of prostate cancer diagnosis experience. Compressive sensing simulations were performed and the reconstruction results show the CD-SFCRF and extension frameworks having noticeable improvements over state of the art methods. Tissue structure and image details are well preserved while sparse sampling artifacts were reduced and eliminated. Future work on this framework include extending the current work in multiple ways. Extensions including integration into computer aided diagnosis applications as well as improving on the compressive sensing strategy

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations

Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom

Statistical Learning Methods for Diffusion Magnetic Resonance Imaging

Diffusion Magnetic Resonance Imaging (dMRI) is a commonly used imaging technique to reveal white matter (WM) microstructure by probing the diffusion of water molecules. The diffusion of water molecules is constrained by the biological boundaries including nerves and tissues. Thus, quantifying the diffusion process is important to understand the WM microstructure. However, the development of efficient analytical methods for the reconstruction, lifespan structural connectome analysis, and surrogate variable analysis have fallenseriously behind the technological advances. This challenge motivates us to develop new statistical learning methods for dMRI. In the first project, we propose a two-stage sparse and adaptive smoothing model (TSASM) for two major image denoising tasks in neuroimaging data analysis, including image reconstruction from a series of noisy images within each subject and group analysis of images obtained from different subjects. Our TSASM consists of an initial smoothing stage of applying a penalized M-estimator and a refined smoothing stage of applying kernel-based smoothing methods. The key novelties of our TSASM are that it accounts for the sparse structure of imaging signals while preserving piecewise smooth regions with unknown edges. In the second project, we develop a scalable analytical method for mapping the lifespan human structural connectome. Specifically, we develop a novel lifespan population-based structural connectome (LPSC) framework that integrates fiber bundle and functional network information for hierarchically guiding the registration. Our LPSC is applicable to several neuroimaging studies of neuropsychiatric disorders as well as normal brain development. An improved understanding of human structural connectome has the potential to inspire new approaches to prevention, diagnosis, and treatment of many illnesses. In the third project, we propose an eigen-shrinkage projection (ESP) method to perform the surrogate variable analysis and solve the hidden confounder and harmonization problems in the neuroimaging studies. Our ESP can eliminate the signals from primary variable while preserving the eigenvalue-gap between hidden confounder and noises, which enables hidden confounders estimation from the projected data. We then investigate the statistical properties of the estimated hidden confounders and uncover the natural connection with ridge regression. Numerical experiments are used to illustrate the finite-sample performance.Doctor of Philosoph