1,729 research outputs found
Non-local MRI upsampling.
International audienceIn Magnetic Resonance Imaging, image resolution is limited by several factors such as hardware or time constraints. In many cases, the acquired images have to be upsampled to match a specific resolution. In such cases, image interpolation techniques have been traditionally applied. However, traditional interpolation techniques are not able to recover high frequency information of the underlying high resolution data. In this paper, a new upsampling method is proposed to recover some of this high frequency information by using a data-adaptive patch-based reconstruction in combination with a subsampling coherence constraint. The proposed method has been evaluated on synthetic and real clinical cases and compared with traditional interpolation methods. The proposed method is shown to outperform classical interpolation methods compared in terms of quantitative measures and visual observation
Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space
Diffusion MRI requires sufficient coverage of the diffusion wavevector space,
also known as the q-space, to adequately capture the pattern of water diffusion
in various directions and scales. As a result, the acquisition time can be
prohibitive for individuals who are unable to stay still in the scanner for an
extensive period of time, such as infants. To address this problem, in this
paper we harness non-local self-similar information in the x-q space of
diffusion MRI data for q-space upsampling. Specifically, we first perform
neighborhood matching to establish the relationships of signals in x-q space.
The signal relationships are then used to regularize an ill-posed inverse
problem related to the estimation of high angular resolution diffusion MRI data
from its low-resolution counterpart. Our framework allows information from
curved white matter structures to be used for effective regularization of the
otherwise ill-posed problem. Extensive evaluations using synthetic and infant
diffusion MRI data demonstrate the effectiveness of our method. Compared with
the widely adopted interpolation methods using spherical radial basis functions
and spherical harmonics, our method is able to produce high angular resolution
diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the full stack of 2D slices and has the ability to leverage inter-slice
spatial dependences through internal memory units. RFCN combines anatomical
detection and segmentation into a single architecture that is trained
end-to-end thus significantly reducing computational time, simplifying the
segmentation pipeline, and potentially enabling real-time applications. We
report on an investigation of RFCN using two datasets, including the publicly
available MICCAI 2009 Challenge dataset. Comparisons have been carried out
between fully convolutional networks and deep restricted Boltzmann machines,
including a recurrent version that leverages inter-slice spatial correlation.
Our studies suggest that RFCN produces state-of-the-art results and can
substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201
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
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
This paper analyzes the use of 3D Convolutional Neural Networks for brain
tumor segmentation in MR images. We address the problem using three different
architectures that combine fine and coarse features to obtain the final
segmentation. We compare three different networks that use multi-resolution
features in terms of both design and performance and we show that they improve
their single-resolution counterparts
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