Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging

Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.This dissertation describes quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), which is a powerful tool in diagnostics, mainly in oncology. After a time series of T1-weighted images recording contrast-agent distribution in the body has been acquired, data processing phase follows. It is presented step by step in this dissertation. The theoretical background in physiological and MRI-acquisition modeling is described together with the estimation process leading to parametric maps describing perfusion and microcirculation properties of the investigated tissue on a voxel-by-voxel basis. The dissertation is divided into this theoretical analysis and a set of publications representing particular contributions of the author to DCE-MRI.

Analysis of DCE-MRI Data using a Nonnegative Elastic Net

We present a nonnegative Elastic Net approach for the analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. A multi-compartment approach is considered, which is translated into a (restricted) least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of some compartment. Using the Elastic Net estimator, we chose clusters of basis functions, and hence, rate constants of compartments. As further challenge, the estimator has to be restricted to positive regression parameters, which correspond to transfer rates of the compartments. The proposed estimation method is applied to an in-vivo data set

Optimizing Magnetic Resonance Imaging for Image-Guided Radiotherapy

Magnetic resonance imaging (MRI) is playing an increasingly important role in image-guided radiotherapy. MRI provides excellent soft tissue contrast, and is flexible in characterizing various tissue properties including relaxation, diffusion and perfusion. This thesis aims at developing new image analysis and reconstruction algorithms to optimize MRI in support of treatment planning, target delineation and treatment response assessment for radiotherapy. First, unlike Computed Tomography (CT) images, MRI cannot provide electron density information necessary for radiation dose calculation. To address this, we developed a synthetic CT generation algorithm that generates pseudo CT images from MRI, based on tissue classification results on MRI for female pelvic patients. To improve tissue classification accuracy, we learnt a pelvic bone shape model from a training dataset, and integrated the shape model into an intensity-based fuzzy c-menas classification scheme. The shape-regularized tissue classification algorithm is capable of differentiating tissues that have significant overlap in MRI intensity distributions. Treatment planning dose calculations using synthetic CT image volumes generated from the tissue classification results show acceptably small variations as compared to CT volumes. As MRI artifacts, such as B1 filed inhomogeneity (bias field) may negatively impact the tissue classification accuracy, we also developed an algorithm that integrates the correction of bias field into the tissue classification scheme. We modified the fuzzy c-means classification by modeling the image intensity as the true intensity corrupted by the multiplicative bias field. A regularization term further ensures the smoothness of the bias field. We solved the optimization problem using a linearized alternating direction method of multipliers (ADMM) method, which is more computational efficient over existing methods. The second part of this thesis looks at a special MR imaging technique, diffusion-weighted MRI (DWI). By acquiring a series of DWI images with a wide range of b-values, high order diffusion analysis can be performed using the DWI image series and new biomarkers for tumor grading, delineation and treatment response evaluation may be extracted. However, DWI suffers from low signal-to-noise ratio at high b-values, and the multi-b-value acquisition makes the total scan time impractical for clinical use. In this thesis, we proposed an accelerated DWI scheme, that sparsely samples k-space and reconstructs images using a model-based algorithm. Specifically, we built a 3D block-Hankel tensor from k-space samples, and modeled both local and global correlations of the high dimensional k-space data as a low-rank property of the tensor. We also added a phase constraint to account for large phase variations across different b-values, and to allow reconstruction from partial Fourier acquisition, which further accelerates the image acquisition. We proposed an ADMM algorithm to solve the constrained image reconstruction problem. Image reconstructions using both simulated and patient data show improved signal-to-noise ratio. As compared to clinically used parallel imaging scheme which achieves a 4-fold acceleration, our method achieves an 8-fold acceleration. Reconstructed images show reduced reconstruction errors as proved on simulated data and similar diffusion parameter mapping results on patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143919/1/llliu_1.pd

T1 relaxometry of crossing fibres in the human brain

A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI

Spatially regularized estimation for the analysis of DCE-MRI data

Competing compartment models of different complexities have been used for the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data. We present a spatial Elastic Net approach that allows to estimate the number of compartments for each voxel such that the model complexity is not fixed a priori. A multi-compartment approach is considered, which is translated into a restricted least square model selection problem. This is done by using a set of basis functions for a given set of candidate rate constants. The form of the basis functions is derived from a kinetic model and thus describes the contribution of a specific compartment. Using a spatial Elastic Net estimator, we chose a sparse set of basis functions per voxel, and hence, rate constants of compartments. The spatial penalty takes into account the voxel structure of an image and performs better than a penalty treating voxels independently. The proposed estimation method is evaluated for simulated images and applied to an in-vivo data set

Robust Magnetic Resonance Imaging of Short T2 Tissues

Tissues with short transverse relaxation times are defined as ‘short T2 tissues’, and short T2 tissues often appear dark on images generated by conventional magnetic resonance imaging techniques. Common short T2 tissues include tendons, meniscus, and cortical bone. Ultrashort Echo Time (UTE) pulse sequences can provide morphologic contrasts and quantitative maps for short T2 tissues by reducing time-of-echo to the system minimum (e.g., less than 100 us). Therefore, UTE sequences have become a powerful imaging tool for visualizing and quantifying short T2 tissues in many applications. In this work, we developed a new Flexible Ultra Short time Echo (FUSE) pulse sequence employing a total of thirteen acquisition features with adjustable parameters, including optimized radiofrequency pulses, trajectories, choice of two or three dimensions, and multiple long-T2 suppression techniques. Together with the FUSE sequence, an improved analytical density correction and an auto-deblurring algorithm were incorporated as part of a novel reconstruction pipeline for reducing imaging artifacts. Firstly, we evaluated the FUSE sequence using a phantom containing short T2 components. The results demonstrated that differing UTE acquisition methods, improving the density correction functions and improving the deblurring algorithm could reduce the various artifacts, improve the overall signal, and enhance short T2 contrast. Secondly, we applied the FUSE sequence in bovine stifle joints (similar to the human knee) for morphologic imaging and quantitative assessment. The results showed that it was feasible to use the FUSE sequence to create morphologic images that isolate signals from the various knee joint tissues and carry out comprehensive quantitative assessments, using the meniscus as a model, including the mappings of longitudinal relaxation (T1) times, quantitative magnetization transfer parameters, and effective transverse relaxation (T2*) times. Lastly, we utilized the FUSE sequence to image the human skull for evaluating its feasibility in synthetic computed tomography (CT) generation and radiation treatment planning. The results demonstrated that the radiation treatment plans created using the FUSE-based synthetic CT and traditional CT data were able to present comparable dose calculations with the dose difference of mean less than a percent. In summary, this thesis clearly demonstrated the need for the FUSE sequence and its potential for robustly imaging short T2 tissues in various applications

Noise-Corrected, Exponentially Weighted, Diffusion-Weighted MRI (niceDWI) Improves Image Signal Uniformity in Whole-Body Imaging of Metastatic Prostate Cancer.

Purpose: To characterize the voxel-wise uncertainties of Apparent Diffusion Coefficient (ADC) estimation from whole-body diffusion-weighted imaging (WBDWI). This enables the calculation of a new parametric map based on estimates of ADC and ADC uncertainty to improve WBDWI imaging standardization and interpretation: NoIse-Corrected Exponentially-weighted diffusion-weighted MRI (niceDWI). Methods: Three approaches to the joint modeling of voxel-wise ADC and ADC uncertainty (σADC) are evaluated: (i) direct weighted least squares (DWLS), (ii) iterative linear-weighted least-squares (IWLS), and (iii) smoothed IWLS (SIWLS). The statistical properties of these approaches in terms of ADC/σADC accuracy and precision is compared using Monte Carlo simulations. Our proposed post-processing methodology (niceDWI) is evaluated using an ice-water phantom, by comparing the contrast-to-noise ratio (CNR) with conventional exponentially-weighted DWI. We present the clinical feasibility of niceDWI in a pilot cohort of 16 patients with metastatic prostate cancer. Results: The statistical properties of ADC and σADC conformed closely to the theoretical predictions for DWLS, IWLS, and SIWLS fitting routines (a minor bias in parameter estimation is observed with DWLS). Ice-water phantom experiments demonstrated that a range of CNR could be generated using the niceDWI approach, and could improve CNR compared to conventional methods. We successfully implemented the niceDWI technique in our patient cohort, which visually improved the in-plane bias field compared with conventional WBDWI. Conclusions: Measurement of the statistical uncertainty in ADC estimation provides a practical way to standardize WBDWI across different scanners, by providing quantitative image signals that improve its reliability. Our proposed method can overcome inter-scanner and intra-scanner WBDWI signal variations that can confound image interpretation

CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. We address this shortcoming by arranging dictionary atoms in the form of cover tree structures and adopt the corresponding fast approximate nearest neighbour searches to accelerate matched-filtering. For datasets belonging to smooth low-dimensional manifolds cover trees offer search complexities logarithmic in terms of data population. With this motivation we propose an iterative reconstruction algorithm, named CoverBLIP, to address large-size MRF problems where the fingerprint dictionary i.e. discrete manifold of Bloch responses, encodes several intrinsic NMR parameters. We study different forms of convergence for this algorithm and we show that provided with a notion of embedding, the inexact and non-convex iterations of CoverBLIP linearly convergence toward a near-global solution with the same order of accuracy as using exact brute-force searches. Our further examinations on both synthetic and real-world datasets and using different sampling strategies, indicates between 2 to 3 orders of magnitude reduction in total search computations. Cover trees are robust against the curse-of-dimensionality and therefore CoverBLIP provides a notion of scalability -- a consistent gain in time-accuracy performance-- for searching high-dimensional atoms which may not be easily preprocessed (i.e. for dimensionality reduction) due to the increasing degrees of non-linearities appearing in the emerging multi-parametric MRF dictionaries

Spatial two tissue compartment model for DCE-MRI

In the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) compartment models allow to describe the uptake of contrast medium with biological meaningful kinetic parameters. As simple models often fail to adequately describe the observed uptake behavior, more complex compartment models have been proposed. However, the nonlinear regression problem arising from more complex compartment models often suffers from parameter redundancy. In this paper, we incorporate spatial smoothness on the kinetic parameters of a two tissue compartment model by imposing Gaussian Markov random field priors on them. We analyse to what extent this spatial regularisation helps to avoid parameter redundancy and to obtain stable parameter estimates. Choosing a full Bayesian approach, we obtain posteriors and point estimates running Markov Chain Monte Carlo simulations. The proposed approach is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study