9,391 research outputs found
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
AAMDM: Accelerated Auto-regressive Motion Diffusion Model
Interactive motion synthesis is essential in creating immersive experiences
in entertainment applications, such as video games and virtual reality.
However, generating animations that are both high-quality and contextually
responsive remains a challenge. Traditional techniques in the game industry can
produce high-fidelity animations but suffer from high computational costs and
poor scalability. Trained neural network models alleviate the memory and speed
issues, yet fall short on generating diverse motions. Diffusion models offer
diverse motion synthesis with low memory usage, but require expensive reverse
diffusion processes. This paper introduces the Accelerated Auto-regressive
Motion Diffusion Model (AAMDM), a novel motion synthesis framework designed to
achieve quality, diversity, and efficiency all together. AAMDM integrates
Denoising Diffusion GANs as a fast Generation Module, and an Auto-regressive
Diffusion Model as a Polishing Module. Furthermore, AAMDM operates in a
lower-dimensional embedded space rather than the full-dimensional pose space,
which reduces the training complexity as well as further improves the
performance. We show that AAMDM outperforms existing methods in motion quality,
diversity, and runtime efficiency, through comprehensive quantitative analyses
and visual comparisons. We also demonstrate the effectiveness of each
algorithmic component through ablation studies
A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function
Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term
Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI with Simultaneous Motion Estimation and Super-Resolution
Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique
for tumor motion management in image-guided radiation therapy (IGRT). However,
current 4D-MRI suffers from low spatial resolution and strong motion artifacts
owing to the long acquisition time and patients' respiratory variations; these
limitations, if not managed properly, can adversely affect treatment planning
and delivery in IGRT. Herein, we developed a novel deep learning framework
called the coarse-super-resolution-fine network (CoSF-Net) to achieve
simultaneous motion estimation and super-resolution in a unified model. We
designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with
consideration of limited and imperfectly matched training datasets. We
conducted extensive experiments on multiple real patient datasets to verify the
feasibility and robustness of the developed network. Compared with existing
networks and three state-of-the-art conventional algorithms, CoSF-Net not only
accurately estimated the deformable vector fields between the respiratory
phases of 4D-MRI but also simultaneously improved the spatial resolution of
4D-MRI with enhanced anatomic features, yielding 4D-MR images with high
spatiotemporal resolution
Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models
The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution
regional reanalysis dataset for the European domain. In recent years it has
shown significant utility across various climate-related tasks, ranging from
forecasting and climate change research to renewable energy prediction,
resource management, air quality risk assessment, and the forecasting of rare
events, among others. Unfortunately, the availability of CERRA is lagging two
years behind the current date, due to constraints in acquiring the requisite
external data and the intensive computational demands inherent in its
generation. As a solution, this paper introduces a novel method using diffusion
models to approximate CERRA downscaling in a data-driven manner, without
additional informations. By leveraging the lower resolution ERA5 dataset, which
provides boundary conditions for CERRA, we approach this as a super-resolution
task. Focusing on wind speed around Italy, our model, trained on existing CERRA
data, shows promising results, closely mirroring original CERRA data.
Validation with in-situ observations further confirms the model's accuracy in
approximating ground measurements
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
Tracing back the source of contamination
From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer
Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images
Cross-modality synthesis (CMS), super-resolution (SR), and their combination
(CMSR) have been extensively studied for magnetic resonance imaging (MRI).
Their primary goals are to enhance the imaging quality by synthesizing the
desired modality and reducing the slice thickness. Despite the promising
synthetic results, these techniques are often tailored to specific tasks,
thereby limiting their adaptability to complex clinical scenarios. Therefore,
it is crucial to build a unified network that can handle various image
synthesis tasks with arbitrary requirements of modality and resolution
settings, so that the resources for training and deploying the models can be
greatly reduced. However, none of the previous works is capable of performing
CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction
methods often treat alias frequencies improperly, resulting in suboptimal
detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free
framework (Uni-COAL) to accomplish the aforementioned tasks with a single
network. The co-modulation design of the image-conditioned and stochastic
attribute representations ensures the consistency between CMS and SR, while
simultaneously accommodating arbitrary combinations of input/output modalities
and thickness. The generator of Uni-COAL is also designed to be alias-free
based on the Shannon-Nyquist signal processing framework, ensuring effective
suppression of alias frequencies. Additionally, we leverage the semantic prior
of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic
preservation of anatomical structures during synthesis. Experiments on three
datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and
CMSR tasks for MR images, which highlights its generalizability to wide-range
applications
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