1,033 research outputs found
Doctor of Philosophy
dissertationImage-based biomechanics, particularly numerical modeling using subject-specific data obtained via imaging, has proven useful for elucidating several biomechanical processes, such as prediction of deformation due to external loads, applicable to both normal function and pathophysiology of various organs. As the field evolves towards applications that stretch the limits of imaging hardware and acquisition time, the information traditionally expected as input for numerical routines often becomes incomplete or ambiguous, and requires specific acquisition and processing strategies to ensure physical accuracy and compatibility with predictive mathematical modeling. These strategies, often derivatives or specializations of traditional mechanics, effectively extend the nominal capability of medical imaging hardware providing subject-specific information coupled with the option of using the results for predictive numerical simulations. This research deals with the development of tools for extracting mechanical measurements from a finite set of imaging data and finite element analysis in the context of constructing structural atlases of the heart, understanding the biomechanics of the venous vasculature, and right ventricular failure. The tools include: (1) application of Hyperelastic Warping image registration to displacement-encoded MRI for reconstructing absolute displacement fields, (2) combination of imaging and a material parameter identification approach to measure morphology, deformation, and mechanical properties of vascular tissue, and (3) extrapolation of diffusion tensor MRI acquired at a single time point for the prediction the structural changes across the cardiac cycle with mechanical simulations. Selected tools were then applied to evaluate structural changes in a reversible animal model for right ventricular failure due to pressure overload
Livrable D2.2 of the PERSEE project : Analyse/Synthese de Texture
Livrable D2.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D2.2 du projet. Son titre : Analyse/Synthese de Textur
Talking Head(?) Anime from a Single Image 4: Improved Model and Its Distillation
We study the problem of creating a character model that can be controlled in
real time from a single image of an anime character. A solution to this problem
would greatly reduce the cost of creating avatars, computer games, and other
interactive applications.
Talking Head Anime 3 (THA3) is an open source project that attempts to
directly address the problem. It takes as input (1) an image of an anime
character's upper body and (2) a 45-dimensional pose vector and outputs a new
image of the same character taking the specified pose. The range of possible
movements is expressive enough for personal avatars and certain types of game
characters. However, the system is too slow to generate animations in real time
on common PCs, and its image quality can be improved.
In this paper, we improve THA3 in two ways. First, we propose new
architectures for constituent networks that rotate the character's head and
body based on U-Nets with attention that are widely used in modern generative
models. The new architectures consistently yield better image quality than the
THA3 baseline. Nevertheless, they also make the whole system much slower: it
takes up to 150 milliseconds to generate a frame. Second, we propose a
technique to distill the system into a small network (less than 2 MB) that can
generate 512x512 animation frames in real time (under 30 FPS) using consumer
gaming GPUs while keeping the image quality close to that of the full system.
This improvement makes the whole system practical for real-time applications
DiffDreamer: Consistent Single-view Perpetual View Generation with Conditional Diffusion Models
Perpetual view generation -- the task of generating long-range novel views by
flying into a given image -- has been a novel yet promising task. We introduce
DiffDreamer, an unsupervised framework capable of synthesizing novel views
depicting a long camera trajectory while training solely on internet-collected
images of nature scenes. We demonstrate that image-conditioned diffusion models
can effectively perform long-range scene extrapolation while preserving both
local and global consistency significantly better than prior GAN-based methods.
Project page: https://primecai.github.io/diffdreamer
Thermophysical Phenomena in Metal Additive Manufacturing by Selective Laser Melting: Fundamentals, Modeling, Simulation and Experimentation
Among the many additive manufacturing (AM) processes for metallic materials,
selective laser melting (SLM) is arguably the most versatile in terms of its
potential to realize complex geometries along with tailored microstructure.
However, the complexity of the SLM process, and the need for predictive
relation of powder and process parameters to the part properties, demands
further development of computational and experimental methods. This review
addresses the fundamental physical phenomena of SLM, with a special emphasis on
the associated thermal behavior. Simulation and experimental methods are
discussed according to three primary categories. First, macroscopic approaches
aim to answer questions at the component level and consider for example the
determination of residual stresses or dimensional distortion effects prevalent
in SLM. Second, mesoscopic approaches focus on the detection of defects such as
excessive surface roughness, residual porosity or inclusions that occur at the
mesoscopic length scale of individual powder particles. Third, microscopic
approaches investigate the metallurgical microstructure evolution resulting
from the high temperature gradients and extreme heating and cooling rates
induced by the SLM process. Consideration of physical phenomena on all of these
three length scales is mandatory to establish the understanding needed to
realize high part quality in many applications, and to fully exploit the
potential of SLM and related metal AM processes
Motion Compensation for Free-Breathing Abdominal Diffusion-Weighted Imaging (MoCo DWI)
Diffusion-weighted imaging (DWI) is a common technique in medical diagnostics. One challenge of thoracic and abdominal DWI is respiratory motion which can result in motion artifacts. To eliminate these artifacts, a new kind of retrospective, respiratory motion compensation for DWI was developed and tested. This new technique — MoCo DWI — is the first in DWI which provides fully-deformable motion compensation.
To enable this, despite the low image quality of DWI, two free-breathing sequences were used: (1) a gradient echo sequence (GRE) with a configuration for optimal respiratory motion estimation and (2) a DWI in a configuration of clinical interest. The DWI acquisition was gated into 10 motion phases. Each motion phase was then co-aligned with the motion estimation.
The implementation was tested with eleven volunteers. The results showed that MoCo DWI can reduce motion blurring in single b-value images, especially at the liver-lung interface. The improvement of ADC-maps was even more prominent. Individual slices showed motion induced artifacts which could be reduced or even eliminated by MoCo DWI. This was also reflected by expected more homogeneous ADC values in the liver in all data sets.
These results promise to reduce measurements with limited diagnostic value while keeping or increasing patient comfort
Visual analytics methods for shape analysis of biomedical images exemplified on rodent skull morphology
In morphometrics and its application fields like medicine and biology experts are interested in causal relations of variation in organismic shape to phylogenetic, ecological, geographical, epidemiological or disease factors - or put more succinctly by Fred L. Bookstein, morphometrics is "the study of covariances of biological form". In order to reveal causes for shape variability, targeted statistical analysis correlating shape features against external and internal factors is necessary but due to the complexity of the problem often not feasible in an automated way. Therefore, a visual analytics approach is proposed in this thesis that couples interactive visualizations with automated statistical analyses in order to stimulate generation and qualitative assessment of hypotheses on relevant shape features and their potentially affecting factors. To this end long established morphometric techniques are combined with recent shape modeling approaches from geometry processing and medical imaging, leading to novel visual analytics methods for shape analysis. When used in concert these methods facilitate targeted analysis of characteristic shape differences between groups, co-variation between different structures on the same anatomy and correlation of shape to extrinsic attributes. Here a special focus is put on accurate modeling and interactive rendering of image deformations at high spatial resolution, because that allows for faithful representation and communication of diminutive shape features, large shape differences and volumetric structures. The utility of the presented methods is demonstrated in case studies conducted together with a collaborating morphometrics expert. As exemplary model structure serves the rodent skull and its mandible that are assessed via computed tomography scans
Connectivity-enhanced diffusion analysis reveals white matter density disruptions in first episode and chronic schizophrenia.
Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with "fixel-based" analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = -0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia
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