Registration and Modeling from Spaced and Misaligned Image Volumes

We present an integrated registration, segmentation, and shape interpolation framework to model objects from 3D and 4D volumes made up of spaced and misaligned slices having arbitrary relative positions. The framework was validated on artificial data and tested on real MRI and CT scans. The complete framework performed significantly better than the sequential approach of registration followed by segmentation and shape interpo- lation

In silico modeling of oxygen-enhanced MRI of specific ventilation.

Specific ventilation imaging (SVI) proposes that using oxygen-enhanced 1H MRI to capture signal change as subjects alternatively breathe room air and 100% O2 provides an estimate of specific ventilation distribution in the lung. How well this technique measures SV and the effect of currently adopted approaches of the technique on resulting SV measurement is open for further exploration. We investigated (1) How well does imaging a single sagittal lung slice represent whole lung SV? (2) What is the influence of pulmonary venous blood on the measured MRI signal and resultant SVI measure? and (3) How does inclusion of misaligned images affect SVI measurement? In this study, we utilized two patient-based in silico models of ventilation, perfusion, and gas exchange to address these questions for normal healthy lungs. Simulation results from the two healthy young subjects show that imaging a single slice is generally representative of whole lung SV distribution, with a calculated SV gradient within 90% of that calculated for whole lung distributions. Contribution of O2 from the venous circulation results in overestimation of SV at a regional level where major pulmonary veins cross the imaging plane, resulting in a 10% increase in SV gradient for the imaging slice. A worst-case scenario simulation of image misalignment increased the SV gradient by 11.4% for the imaged slice

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR

Optimal Correction of The Slice Timing Problem and Subject Motion Artifacts in fMRI

Functional magnetic resonance imaging (fMRI) is an extremely popular investigative and clinical imaging tool that allows safe and noninvasive study of the functional living brain. Fundamentally, fMRI measures a physiological signal as it changes over time. The manner in which this spatio-temporal signal is acquired can create technical challenges during image reconstruction that must be corrected for if any meaningful information is to be extracted from the data. Two particular challenges that are fundamentally intertwined with each other are temporal misalignment and spatial misalignment. Temporal misalignment is due to the nature of fMRI acquisition protocols themselves: a 3D volume is created by sampling and stacking multiple 2D slices. However, these slices are not acquired simultaneously or sequentially, and therefore will always be temporally misaligned with each other. Spatial misalignment arises when subject motion is present during the scan, resulting in individual volumes being spatially misaligned with each other. Spatial and temporal misalignment are not independent from each other, and their interaction can cause additional artifacts and reconstruction challenges if not addressed properly. The purpose of this thesis is to critically examine the problem of both spatial and temporal misalignment from a signal processing perspective, while considering the physical nature and origin of the signal itself, and develop optimal correction routines for spatial and temporal misalignment and their associated artifacts. One of the most immediate problems associated with temporal misalignment is that the order in which the slices are acquired must be known in order for correction to be possible. Surprisingly, this information is rarely provided with old or shared data, meaning that this critical preprocessing step must be skipped, significantly lowering the value of the data. We use the spatio-temporal properties of the fMRI signal to develop a robust and accurate algorithm to infer the slice acquisition order retrospectively from any fMRI scan. The ability to extract the interleave parameter from any data set allows us to perform slice timing correction even if this information had been lost, or was not provided with the scan. In the next section of this work, we develop a new optimal method of slice timing correction (Filter-Shift) based on the fundamental properties of sampling theory in digital signal processing. By examining the properties of the signal of interest (The blood oxygen level depended signal: BOLD signal), we are able to design and implement an effective FIR filter to simultaneously remove noise and reconstruct the signal of interest at any shifted offset, without the need for sub-optimal interpolation. In the final section, we investigate the effects of different motion types on the MR signal based on the Bloch equation, in order to develop a theoretical foundation from which we can create an optimal correction method. We devise a novel method to remove these artifacts: Discrete reconstruction of irregular fMRI trajectory (DRIFT). Our method calculates the exact displacement of the k-space samples due to motion at each dwell time and retrospectively corrects each slice of the fMRI volume using an inverse nonuniform Fourier transform. We conclude that a hybrid approach with both prospective and retrospective components are essentially required for optimal removal of motion artifacts from the fMRI data. The combined work of this thesis provides two theoretically sound and extremely effective correction routines, that both remove artifacts and restore the underlying sampled signal. Motion correction and slice timing correction are typically the first two preprocessing steps to be applied to any fMRI data, and thus provide the foundation for any further analysis. While many other preprocessing steps can be omitted or included depending on the analysis, motion correction and slice timing correction are unequivocally beneficial and necessary for accurate and reliable results. This work provides a theoretical and quantitative framework that describes the optimal removal of artifacts associated with motion and slice timing

3D Surface Reconstruction of Plant Seeds by Volume Carving: Performance and Accuracies

We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 μm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camera pose variations have to be compensated. We do this by robustly estimating the tool center point from each acquired image. 3D reconstruction can then be performed by a simple shape-from-silhouette approach. In experiments we investigate runtimes, theoretically achievable accuracy, experimentally achieved accuracy, and show as a proof of principle that the proposed method is well sufficient for 3D seed phenotyping purposes

Integrated Segmentation and Interpolation of Sparse Data

International audienceWe address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework. The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjectively on artificial data and MRI and CT scans, and is compared against the traditional sequential approach which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach, but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach

Functional data analytics for wearable device and neuroscience data

This thesis uses methods from functional data analysis (FDA) to solve problems from three scientific areas of study. While the areas of application are quite distinct, the common thread of functional data analysis ties them together. The first chapter describes interactive open-source software for explaining and disseminating results of functional data analyses. Chapters two and three use curve alignment, or registration, to solve common problems in accelerometry and neuroimaging, respectively. The final chapter introduces a novel regression method for modeling functional outcomes that are trajectories over time. The first chapter of this thesis details a software package for interactively visualizing functional data analyses. The software is designed to work for a wide range of datasets and several types of analyses. This chapter describes that software and provides an overview ofFDA in different contexts. The second chapter introduces a framework for curve alignment, or registration, of exponential family functional data. The approach distinguishes itself from previous registration methods in its ability to handle dense binary observations with computational efficiency. Motivation comes from the Baltimore Longitudinal Study on Aging, in which accelerometer data provides valuable insights into the timing of sedentary behavior. The third chapter takes lessons learned about curve registration from the second chapter and use them to develop methods in an entirely new context: large multisite brain imaging studies. Scanner effects in multisite imaging studies are non-biological variability due to technical differences across sites and scanner hardware. This method identifies and removes scanner effects by registering cumulative distribution functions of image intensities values. In the final chapter the focus shifts from curve registration to regression. Described within this chapter is an entirely new nonlinear regression framework that draws from both functional data analysis and systems of ordinary equations. This model is motivated by the neurobiology of skilled movement, and was developed to capture the relationship between neural activity and arm movement in mice

Cardiac motion estimation from medical images: a regularisation framework applied on pairwise image registration displacement fields

Accurate cardiac motion estimation from medical images such as ultrasound is important for clinical evaluation. We present a novel regularisation layer for cardiac motion estimation that will be applied after image registration and demonstrate its effectiveness. The regularisation utilises a spatio-temporal model of motion, b-splines of Fourier, to fit to displacement fields from pairwise image registration. In the process, it enforces spatial and temporal smoothness and consistency, cyclic nature of cardiac motion, and better adherence to the stroke volume of the heart. Flexibility is further given for inclusion of any set of registration displacement fields. The approach gave high accuracy. When applied to human adult Ultrasound data from a Cardiac Motion Analysis Challenge (CMAC), the proposed method is found to have 10% lower tracking error over CMAC participants. Satisfactory cardiac motion estimation is also demonstrated on other data sets, including human fetal echocardiography, chick embryonic heart ultrasound images, and zebrafish embryonic microscope images, with the average Dice coefficient between estimation motion and manual segmentation at 0.82–0.87. The approach of performing regularisation as an add-on layer after the completion of image registration is thus a viable option for cardiac motion estimation that can still have good accuracy. Since motion estimation algorithms are complex, dividing up regularisation and registration can simplify the process and provide flexibility. Further, owing to a large variety of existing registration algorithms, such an approach that is usable on any algorithm may be useful

Acquisition and Mining of the Whole Mouse Brain Microstructure

Charting out the complete brain microstructure of a mammalian species is a grand challenge. Recent advances in serial sectioning microscopy such as the Knife- Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical sectioning technique, have the potential to finally address this challenge. Nevertheless, there still are several obstacles remaining to be overcome. First, many of these serial sectioning microscopy methods are still experimental and are not fully automated. Second, even when the full raw data have been obtained, morphological reconstruction, visualization/editing, statistics gathering, connectivity inference, and network analysis remain tough problems due to the unprecedented amounts of data. I designed a general data acquisition and analysis framework to overcome these challenges with a focus on data from the C57BL/6 mouse brain. Since there has been no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. To address the problems, I constructed a general software framework for automated data acquisition and computational analysis of the KESM data, and conducted two scientific case studies to discuss how the mouse brain microstructure from the KESM can be utilized. I expect the data, tools, and studies resulting from this dissertation research to greatly contribute to computational neuroanatomy and computational neuroscience