Functional pulmonary MRI with ultra-fast steady-state free precession

To date, computed tomography and nuclear medicine techniques are still the reference standard for lung imaging, but radiation exposure is a major concern; especially in case of longitudinal examinations and in children. Therefore, radiation-free imaging is an urgent necessity. Pulmonary magnetic resonance imaging (MRI) is radiation-free, but poses challenges since the low proton density and the presence of strong mesoscopic susceptibility variations considerably reduce the detectable MR signal. As a result, the lung typically appears as a “black hole” with conventional MRI techniques. Recently, ultra-fast balanced steady-state free precession (ufSSFP) methods were proposed for ameliorated lung morphological imaging. In this thesis, ufSSFP is employed to develop and improve several pulmonary functional imaging methods, which can be used in clinical settings using standard MR scanners and equipment. At every breath, the lung expands and contracts, and at every heartbeat, the blood is pumped through the arteries to reach the lung parenchyma. This creates signal modulations associated with pulmonary blood perfusion and ventilation that are detectable by MRI. The second chapter of this thesis focuses on the optimization of time-resolved two-dimensional (2D) ufSSFP for perfusion-weighted and ventilation-weighted imaging of the lung. Subsequently, in the third chapter, three-dimensional (3D) multi-volumetric ufSSFP breath-hold imaging is used to develop a lung model and retrieve the measure α, a novel ventilation-weighted quantitative parameter. Oxygen-enhanced MRI exploits the paramagnetic properties of oxygen dissolved in the blood, acting as a weak T1-shortening contrast agent. When breathing pure oxygen, it reaches only ventilated alveoli of the parenchyma and dissolves only in functional and perfused regions. How ufSSFP imaging in combination with a lung model can be used to calculate robust 3D oxygen enhancement maps is described in the fourth chapter. In addition, in the fifth chapter, 2D inversion recovery ufSSFP imaging is employed to map the T1 and T2 relaxation times of the lung, the change of the relaxation times after hyperoxic conditions, as well as the physiological oxygen wash-in and wash-out time (related to the time needed to shorten T1 after oxygen breathing). The objective of the last chapter of this thesis is the application of 3D ufSSFP imaging before and after intravenous gadolinium-based contrast agent administration for the investigation of signal enhancement ratio (SER) mapping: a rapid technique to visualize perfusion-related diseases of the lung parenchyma. The techniques presented in this thesis using optimized ufSSFP pulse sequences demonstrated potential to reveal new insights on pulmonary function as well as quantification, and might become part of the future standard for the evaluation and follow-up of several lung pathologies

Robust Subspace Estimation via Low-Rank and Sparse Decomposition and Applications in Computer Vision

PhDRecent advances in robust subspace estimation have made dimensionality reduction and noise and outlier suppression an area of interest for research, along with continuous improvements in computer vision applications. Due to the nature of image and video signals that need a high dimensional representation, often storage, processing, transmission, and analysis of such signals is a difficult task. It is therefore desirable to obtain a low-dimensional representation for such signals, and at the same time correct for corruptions, errors, and outliers, so that the signals could be readily used for later processing. Major recent advances in low-rank modelling in this context were initiated by the work of Cand`es et al. [17] where the authors provided a solution for the long-standing problem of decomposing a matrix into low-rank and sparse components in a Robust Principal Component Analysis (RPCA) framework. However, for computer vision applications RPCA is often too complex, and/or may not yield desirable results. The low-rank component obtained by the RPCA has usually an unnecessarily high rank, while in certain tasks lower dimensional representations are required. The RPCA has the ability to robustly estimate noise and outliers and separate them from the low-rank component, by a sparse part. But, it has no mechanism of providing an insight into the structure of the sparse solution, nor a way to further decompose the sparse part into a random noise and a structured sparse component that would be advantageous in many computer vision tasks. As videos signals are usually captured by a camera that is moving, obtaining a low-rank component by RPCA becomes impossible. In this thesis, novel Approximated RPCA algorithms are presented, targeting different shortcomings of the RPCA. The Approximated RPCA was analysed to identify the most time consuming RPCA solutions, and replace them with simpler yet tractable alternative solutions. The proposed method is able to obtain the exact desired rank for the low-rank component while estimating a global transformation to describe camera-induced motion. Furthermore, it is able to decompose the sparse part into a foreground sparse component, and a random noise part that contains no useful information for computer vision processing. The foreground sparse component is obtained by several novel structured sparsity-inducing norms, that better encapsulate the needed pixel structure in visual signals. Moreover, algorithms for reducing complexity of low-rank estimation have been proposed that achieve significant complexity reduction without sacrificing the visual representation of video and image information. The proposed algorithms are applied to several fundamental computer vision tasks, namely, high efficiency video coding, batch image alignment, inpainting, and recovery, video stabilisation, background modelling and foreground segmentation, robust subspace clustering and motion estimation, face recognition, and ultra high definition image and video super-resolution. The algorithms proposed in this thesis including batch image alignment and recovery, background modelling and foreground segmentation, robust subspace clustering and motion segmentation, and ultra high definition image and video super-resolution achieve either state-of-the-art or comparable results to existing methods

The Habitable Exoplanet Observatory (HabEx) Mission Concept Study Final Report

The Habitable Exoplanet Observatory, or HabEx, has been designed to be the Great Observatory of the 2030s. For the first time in human history, technologies have matured sufficiently to enable an affordable space-based telescope mission capable of discovering and characterizing Earthlike planets orbiting nearby bright sunlike stars in order to search for signs of habitability and biosignatures. Such a mission can also be equipped with instrumentation that will enable broad and exciting general astrophysics and planetary science not possible from current or planned facilities. HabEx is a space telescope with unique imaging and multi-object spectroscopic capabilities at wavelengths ranging from ultraviolet (UV) to near-IR. These capabilities allow for a broad suite of compelling science that cuts across the entire NASA astrophysics portfolio. HabEx has three primary science goals: (1) Seek out nearby worlds and explore their habitability; (2) Map out nearby planetary systems and understand the diversity of the worlds they contain; (3) Enable new explorations of astrophysical systems from our own solar system to external galaxies by extending our reach in the UV through near-IR. This Great Observatory science will be selected through a competed GO program, and will account for about 50% of the HabEx primary mission. The preferred HabEx architecture is a 4m, monolithic, off-axis telescope that is diffraction-limited at 0.4 microns and is in an L2 orbit. HabEx employs two starlight suppression systems: a coronagraph and a starshade, each with their own dedicated instrument

The Habitable Exoplanet Observatory (HabEx) Mission Concept Study Final Report

The Habitable Exoplanet Observatory, or HabEx, has been designed to be the Great Observatory of the 2030s. For the first time in human history, technologies have matured sufficiently to enable an affordable space-based telescope mission capable of discovering and characterizing Earthlike planets orbiting nearby bright sunlike stars in order to search for signs of habitability and biosignatures. Such a mission can also be equipped with instrumentation that will enable broad and exciting general astrophysics and planetary science not possible from current or planned facilities. HabEx is a space telescope with unique imaging and multi-object spectroscopic capabilities at wavelengths ranging from ultraviolet (UV) to near-IR. These capabilities allow for a broad suite of compelling science that cuts across the entire NASA astrophysics portfolio. HabEx has three primary science goals: (1) Seek out nearby worlds and explore their habitability; (2) Map out nearby planetary systems and understand the diversity of the worlds they contain; (3) Enable new explorations of astrophysical systems from our own solar system to external galaxies by extending our reach in the UV through near-IR. This Great Observatory science will be selected through a competed GO program, and will account for about 50% of the HabEx primary mission. The preferred HabEx architecture is a 4m, monolithic, off-axis telescope that is diffraction-limited at 0.4 microns and is in an L2 orbit. HabEx employs two starlight suppression systems: a coronagraph and a starshade, each with their own dedicated instrument.Comment: Full report: 498 pages. Executive Summary: 14 pages. More information about HabEx can be found here: https://www.jpl.nasa.gov/habex