4,495 research outputs found
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute
stroke. Conventional perfusion analysis performs a deconvolution of the
measurements and thresholds the perfusion parameters to determine the tissue
status. We pursue a data-driven and deconvolution-free approach, where a deep
neural network learns to predict the final infarct volume directly from the
native CTP images and metadata such as the time parameters and treatment. This
would allow clinicians to simulate various treatments and gain insight into
predicted tissue status over time. We demonstrate on a multicenter dataset that
our approach is able to predict the final infarct and effectively uses the
metadata. An ablation study shows that using the native CTP measurements
instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the
salvageable penumbra and irreversibly damaged core lesions. As such, it helps
clinicians to decide on the optimal reperfusion treatment. In perfusion CT
imaging, deconvolution methods are used to obtain clinically interpretable
perfusion parameters that allow identifying brain tissue abnormalities.
Deconvolution methods require the selection of two reference vascular functions
as inputs to the model: the arterial input function (AIF) and the venous output
function, with the AIF as the most critical model input. When manually
performed, the vascular function selection is time demanding, suffers from poor
reproducibility and is subject to the professionals' experience. This leads to
potentially unreliable quantification of the penumbra and core lesions and,
hence, might harm the treatment decision process. In this work we automatize
the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable
deep learning approach for estimating the vascular functions. Unlike previous
methods using clustering or segmentation techniques to select vascular voxels,
AIFNet is directly optimized at the vascular function estimation, which allows
to better recognise the time-curve profiles. Validation on the public ISLES18
stroke database shows that AIFNet reaches inter-rater performance for the
vascular function estimation and, subsequently, for the parameter maps and core
lesion quantification obtained through deconvolution. We conclude that AIFNet
has potential for clinical transfer and could be incorporated in perfusion
deconvolution software.Comment: Preprint submitted to Elsevie
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Intelligent Imaging of Perfusion Using Arterial Spin Labelling
Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. However, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an "intelligent imaging" paradigm: one in which the image acquisition, reconstruction and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve parameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage
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