19,633 research outputs found
Image quality transfer and applications in diffusion MRI
This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content. Here, we demonstrate IQT using a simple patch-regression implementation and the uniquely rich diffusion MRI data set from the human connectome project (HCP). Results highlight potential benefits of IQT in both brain connectivity mapping and microstructure imaging. In brain connectivity mapping, IQT reveals, from standard data sets, thin connection pathways that tractography normally requires specialised data to reconstruct. In microstructure imaging, IQT shows potential in estimating, from standard âsingle-shellâ data (one non-zero b-value), maps of microstructural parameters that normally require specialised multi-shell data. Further experiments show strong generalisability, highlighting IQT's benefits even when the training set does not directly represent the application domain. The concept extends naturally to many other imaging modalities and reconstruction problems
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI
Many medical imaging techniques utilize fitting approaches for quantitative
parameter estimation and analysis. Common examples are pharmacokinetic modeling
in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and
Z-spectra analysis in chemical exchange saturation transfer MRI. Most available
software tools are limited to a special purpose and do not allow for own
developments and extensions. Furthermore, they are mostly designed as
stand-alone solutions using external frameworks and thus cannot be easily
incorporated natively in the analysis workflow. We present a framework for
medical image fitting tasks that is included in MITK, following a rigorous
open-source, well-integrated and operating system independent policy. Software
engineering-wise, the local models, the fitting infrastructure and the results
representation are abstracted and thus can be easily adapted to any model
fitting task on image data, independent of image modality or model. Several
ready-to-use libraries for model fitting and use-cases, including fit
evaluation and visualization, were implemented. Their embedding into MITK
allows for easy data loading, pre- and post-processing and thus a natural
inclusion of model fitting into an overarching workflow. As an example, we
present a comprehensive set of plug-ins for the analysis of DCE MRI data, which
we validated on existing and novel digital phantoms, yielding competitive
deviations between fit and ground truth. Providing a very flexible environment,
our software mainly addresses developers of medical imaging software that
includes model fitting algorithms and tools. Additionally, the framework is of
high interest to users in the domain of perfusion MRI, as it offers
feature-rich, freely available, validated tools to perform pharmacokinetic
analysis on DCE MRI data, with both interactive and automatized batch
processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi
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
Magnetic resonance imaging: a tool for pork pie development
The traditional British pork pie consists of roughly chopped pork cooked in a hot water pastry crust. Due to shrinkage of the meat during cooking, the gap formed around the meat is usually sealed using a gelatin based jelly to exclude air and thus help to preserve the pie. The properties of the jelly are such that it will ingress into the pastry crust causing undesirable softening. The jelly is traditionally produced by simmering pig trotters with seasoning for several hours. In this work we demonstrate the potential of magnetic resonance imaging (MRI) as a tool for investigating the conditions required for producing jellies with different properties and present two examples of this use. Firstly we demonstrate that MRI can determine the ability of water to diffuse through the jelly which is critical in minimizing the amount of moisture moving from the jelly to the crust. Secondly, the impact of jelly temperature on the penetration length into the crust is investigated. These examples highlight the power of MRI as a tool for food assessment
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