13,014 research outputs found
PartDiff: Image Super-resolution with Partial Diffusion Models
Denoising diffusion probabilistic models (DDPMs) have achieved impressive
performance on various image generation tasks, including image
super-resolution. By learning to reverse the process of gradually diffusing the
data distribution into Gaussian noise, DDPMs generate new data by iteratively
denoising from random noise. Despite their impressive performance,
diffusion-based generative models suffer from high computational costs due to
the large number of denoising steps.In this paper, we first observed that the
intermediate latent states gradually converge and become indistinguishable when
diffusing a pair of low- and high-resolution images. This observation inspired
us to propose the Partial Diffusion Model (PartDiff), which diffuses the image
to an intermediate latent state instead of pure random noise, where the
intermediate latent state is approximated by the latent of diffusing the
low-resolution image. During generation, Partial Diffusion Models start
denoising from the intermediate distribution and perform only a part of the
denoising steps. Additionally, to mitigate the error caused by the
approximation, we introduce "latent alignment", which aligns the latent between
low- and high-resolution images during training. Experiments on both magnetic
resonance imaging (MRI) and natural images show that, compared to plain
diffusion-based super-resolution methods, Partial Diffusion Models
significantly reduce the number of denoising steps without sacrificing the
quality of generation
Fuzzy Fibers: Uncertainty in dMRI Tractography
Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
In this work, we investigate the value of uncertainty modeling in 3D
super-resolution with convolutional neural networks (CNNs). Deep learning has
shown success in a plethora of medical image transformation problems, such as
super-resolution (SR) and image synthesis. However, the highly ill-posed nature
of such problems results in inevitable ambiguity in the learning of networks.
We propose to account for intrinsic uncertainty through a per-patch
heteroscedastic noise model and for parameter uncertainty through approximate
Bayesian inference in the form of variational dropout. We show that the
combined benefits of both lead to the state-of-the-art performance SR of
diffusion MR brain images in terms of errors compared to ground truth. We
further show that the reduced error scores produce tangible benefits in
downstream tractography. In addition, the probabilistic nature of the methods
naturally confers a mechanism to quantify uncertainty over the super-resolved
output. We demonstrate through experiments on both healthy and pathological
brains the potential utility of such an uncertainty measure in the risk
assessment of the super-resolved images for subsequent clinical use.Comment: Accepted paper at MICCAI 201
Structural network efficiency is associated with cognitive impairment in small-vessel disease.
To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment.METHODS: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested.RESULTS: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed.CONCLUSIONS: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies
- …