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Non-local means (NLM) filtering of fMRI can reduce noise while preserving spatial structure. We have developed a variant called temporal-NLM (tNLM) which uses similarity in time-series between voxels as the basis for computing the weights in the filter. Using tNLM, dynamic fMRI data can be denoised while spatial boundaries between functionally distinct areas in the brain tend to be preserved. The...
Surface-based approaches have proven particularly relevant and reliable to study cortical functional magnetic resonance imaging (fMRI) data. However projecting fMRI volumes onto the cortical surface remains a challenging problem. Very few methods have been proposed to solve it and most of them rely on a simple interpolation. We propose here an original surface-based method based on a model representing...
We propose a stable and fast reconstruction technique for parallel-beam (PB) tomographic X-Ray imaging, relying on the discrete pseudo-polar (PP) Radon and PP Fourier transforms. Our main contribution is a resampling method, based on modern sampling theory, that transforms the PB measurements to a PP grid. The resampling process is both fast and accurate, and in addition, simultaneously denoises the...
In clinical practice, the magnetic resonance imaging (MRI) is a prevalent neuroimaging technique for Alzheimer's disease (AD) diagnosis. As a learning using privileged information (LUPI) algorithm, SVM+ has shown its effectiveness on the classification of brain disorders, with single-modal neuroimaging samples for testing but multimodal neuroimaging samples for training. In this work, we propose to...
In radiation therapy, tumor tracking allows to adjust the beam such that it follows the respiration-induced tumor motion. However, most clinical approaches rely on implanted fiducial markers to locate the tumor and, thus, only provide sparse information. Motion models have been investigated to estimate dense internal displacement fields from an external surrogate signal, such as range imaging. With...
Estimating human age from brain MR images is useful for early detection of Alzheimer's disease. In this paper we propose a fast and accurate method based on deep learning to predict subject's age. Compared with previous methods, our algorithm achieves comparable accuracy using fewer input images. With our GPU version program, the time needed to make a prediction is 20 ms. We evaluate our methods using...
Spatio-temporal atlas is a useful tool in imaging studies of neurodevelopment, which characterizes the growth of brain, and allows the monitoring of its development. The imaging of preterm and term born infants provides opportunities to develop a series of spatio-temporal atlases that track the changes during the particular period of neurodevelopment between. The aim of this paper is to develop a...
MRI parameter quantification has diverse applications, but likelihood-based methods typically require nonconvex optimization due to nonlinear signal models. To avoid expensive grid searches used in prior works, we propose to learn a nonlinear estimator from simulated training examples and (approximate) kernel ridge regression. As proof of concept, we apply kernel-based estimation to quantify six parameters...
Microtubules play an essential role in many cellular processes whose disrupted functioning is associated with devastating human diseases such as cancer. The discovery and testing of microtubule targeting drugs often involve time-lapse fluorescence microscopy imaging of microtubule plus-end binding proteins and require highly accurate estimation of their dynamic behavior. Although many methods exist...
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