Motion-Compensated Image Reconstruction for Magnetic Resonance (MR) Imaging and for Simultaneous Positron Emission Tomography/MR Imaging

In this work, novel algorithms for 4D (3D + respiratory) and 5D (3D + respiratory + cardiac) motion-compensated (MoCo) magnetic resonance (MR) and positron emission tomography (PET) image reconstruction were developed. The focus of all methods was set on short MR acquisition times. Therefore, respiratory and cardiac patient motion were estimated on the basis of strongly undersampled radial MR data employing joint motion estimation and MR image reconstruction. In case of simultaneous PET/MR acquisitions, motion information derived from MR was incorporated into the MoCo PET reconstruction. 4D respiratory MoCo MR image reconstructions with acquisition times of 40 s achieved an image quality comparable to standard motion handling approaches, which require one order of magnitude longer MR acquisition times. Respiratory MoCo PET images using 1 min of the MR acquisition time for motion estimation revealed improved PET image quality and quantification accuracy when compared to standard reconstruction methods. Additional compensation of cardiac motion resulted in increased image sharpness of MR and PET images in the heart region and enabled time-resolved 5D imaging allowing for reconstruction of any arbitrary combination of respiratory and cardiac motion phases. The proposed methods for MoCo image reconstruction may be integrated into clinical routine, reducing MR acquisition times for improved patient comfort and increasing the diagnostic value of MR and simultaneous PET/MR examinations of the thorax and abdomen

New computational methods toward atomic resolution in single particle cryo-electron microscopy

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 22-06-2016Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. In turn, Electron microscopy (EM) is an essential tool to study the structure and function of biological macromolecules at a medium-high resolution. In this context, Single-Particle Analysis (SPA), as an EM modality, is able to yield Three-Dimensional (3-D) structural information for large biological complexes at near atomic resolution by combining many thousands of projection images. However, these views su er from low Signal-to-Noise Ratios (SNRs), since an extremely low total electron dose is used during exposure to reduce radiation damage and preserve the functional structure of macromolecules. In recent years, the emergence of Direct Detection Devices (DDDs) has opened up the possibility of obtaining images with higher SNRs. These detectors provide a set of frames instead of just one micrograph, which makes it possible to study the behavior of frozen hydrated specimens as a function of electron dose and rate. In this way, it has become apparent that biological specimens embedded in a solid matrix of amorphous ice move during imaging, resulting in Beam-Induced Motion (BIM). Therefore, alignment of frames should be added to the classical standard data processing work ow of single-particle reconstruction, which includes: particle selection, particle alignment, particle classi cation, 3-D reconstruction, and model re nement. In this thesis, we propose new algorithms and improvements for three important steps of this work ow: movie alignment, particles selection, and 3-D reconstruction. For movie alignment, a methodology based on a robust to noise optical ow approach is proposed that can e ciently correct for local movements and provide quantitative analysis of the BIM pattern. We then introduce a method for automatic particle selection in micrographs that uses some new image features to train two classi ers to learn from the user the kind of particles he is interested in. Finally, for 3-D reconstruction, we introduce a gridding-based direct Fourier method that uses a weighting technique to compute a uniform sampled Fourier transform. The algorithms are fully implemented in the open-source Xmipp package (http://xmipp.cnb.csic.es

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

Incorporating accurate statistical modeling in PET: reconstruction for whole-body imaging

Tese de doutoramento em Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2007The thesis is devoted to image reconstruction in 3D whole-body PET imaging. OSEM ( Ordered Subsets Expectation maximization ) is a statistical algorithm that assumes Poisson data. However, corrections for physical effects (attenuation, scattered and random coincidences) and detector efficiency remove the Poisson characteristics of these data. The Fourier Rebinning (FORE), that combines 3D imaging with fast 2D reconstructions, requires corrected data. Thus, if it will be used or whenever data are corrected prior to OSEM, the need to restore the Poisson-like characteristics is present. Restoring Poisson-like data, i.e., making the variance equal to the mean, was achieved through the use of weighted OSEM algorithms. One of them is the NECOSEM, relying on the NEC weighting transformation. The distinctive feature of this algorithm is the NEC multiplicative factor, defined as the ratio between the mean and the variance. With real clinical data this is critical, since there is only one value collected for each bin the data value itself. For simulated data, if we keep track of the values for these two statistical moments, the exact values for the NEC weights can be calculated. We have compared the performance of five different weighted algorithms (FORE+AWOSEM, FORE+NECOSEM, ANWOSEM3D, SPOSEM3D and NECOSEM3D) on the basis of tumor detectablity. The comparison was done for simulated and clinical data. In the former case an analytical simulator was used. This is the ideal situation, since all the weighting factors can be exactly determined. For comparing the performance of the algorithms, we used the Non-Prewhitening Matched Filter (NPWMF) numerical observer. With some knowledge obtained from the simulation study we proceeded to the reconstruction of clinical data. In that case, it was necessary to devise a strategy for estimating the NEC weighting factors. The comparison between reconstructed images was done by a physician largely familiar with whole-body PET imaging

Novel MRI Technologies for Structural and Functional Imaging of Tissues with Ultra-short T₂ Values

Conventional MRI has several limitations such as long scan durations, motion artifacts, very loud acoustic noise, signal loss due to short relaxation times, and RF induced heating of electrically conducting objects. The goals of this work are to evaluate and improve the state-of-the-art methods for MRI of tissue with short T₂, to prove the feasibility of in vivo Concurrent Excitation and Acquisition, and to introduce simultaneous electroglottography measurement during functional lung MRI

GRASP-Pro: imProving GRASP DCE‐MRI through self-calibrating subspace-modeling and contrast phase automation

Purpose: To propose a highly accelerated, high-resolution dynamic contrast-enhanced MRI (DCE-MRI) technique called GRASP-Pro (golden-angle radial sparse parallel imaging with imProved performance) through a joint sparsity and self-calibrating subspace constraint with automated selection of contrast phases. Methods: GRASP-Pro reconstruction enforces a combination of an explicit low-rank subspace-constraint and a temporal sparsity constraint. The temporal basis used to construct the subspace is learned from an intermediate reconstruction step using the low-resolution portion of radial k-space, which eliminates the need for generating the basis using auxiliary data or a physical signal model. A convolutional neural network was trained to generate the contrast enhancement curve in the artery, from which clinically relevant contrast phases are automatically selected for evaluation. The performance of GRASP-Pro was demonstrated for high spatiotemporal resolution DCE-MRI of the prostate and was compared against standard GRASP in terms of overall image quality, image sharpness, and residual streaks and/or noise level. Results: Compared to GRASP, GRASP-Pro reconstructed dynamic images with enhanced sharpness, less residual streaks and/or noise, and finer delineation of the prostate without prolonging reconstruction time. The image quality improvement reached statistical significance (P < 0.05) in all the assessment categories. The neural network successfully generated contrast enhancement curves in the artery, and corresponding peak enhancement indexes correlated well with that from the manual selection. Conclusion: GRASP-Pro is a promising method for rapid and continuous DCE-MRI. It enables superior reconstruction performance over standard GRASP and allows reliable generation of artery enhancement curve to guide the selection of desired contrast phases for improving the efficiency of GRASP MRI workflow

Rosette Spectroscopic Imaging

Chemical shift imaging (CSI) has been the mainstay of spectroscopic imaging because of its simple implementation, reliability and ease of image reconstruction. This technique has been widely used for observing the changes in the metabolic signature of tissues during evolving pathological and/or physiological conditions. CSI owes its ease of implementation and analysis to the Fourier encoding approach upon which is based. In this approach, the spectral-spatial information is encoded in a rectilinear fashion that favors the acquisition of very high-resolution information along the spectral axis and relatively low resolution along the spatial directions. For applications where higher spatial resolution is desired over a narrower spectral bandwidth, trajectory designs that repeatedly cross the center of k-space through the use of time-dependent gradients offer a convenient means to achieve significant speedups in data acquisition. This stems from the fact that the readout period could be used to acquire multiple spatial frequency values which, in turn, leads to a reduction in the total number of RF excitations required to provide proper encoding of the spatial and spectral information. Among the trajectory designs that could be well suited for such a spectroscopic imaging approach the Rosette data acquisition approach is particularly attractive because of its relatively simple implementation and modest gradient requirements. The time-varying nature of the gradients in this trajectory design, while flexible, leads to smooth variations in sample density and larger signal bandwidths than those associated with the CSI gold standard. Despite these potential drawbacks, because no time is spent collecting information in the corners of k-space, we demonstrate that rosette spectroscopic imaging (RSI) can lead to an efficiency gain over CSI in a wide range of spectral bandwidths and spatial resolutions. An analytic relationship for the number of excitations to be used in an RSI experiment is derived and a method to obtain a more accurate self-derived B0 map that uses the information of the prevalent resonance in each voxel and linear regression is offered. Moreover, we show that any imaging technique that periodically samples the center and edges of k-space could be used for spectroscopic imaging