Iterative tomographic reconstruction with TV prior for low-dose CBCT dental imaging.

Cone-beam computed tomography is becoming more and more popular in applications such as 3D dental imaging. Iterative methods compared to the standard Feldkamp algorithm have shown improvements in image quality of reconstruction of low-dose acquired data despite their long computing time. An interesting aspect of iterative methods is their ability to include prior information such as sparsity-constraint.

Imaging of polychromatic sources through Compton spectral reconstruction.

Study the performance of a spectral reconstruction method for Compton imaging of polychromatic sources and compare it to standard Compton reconstruction based on the selection of photopeak events.The proposed spectral and the standard photopeak reconstruction methods are used to reconstruct images from simulated sources emitting simultaneously photons of 140, 245, 364 and 511 keV. Data are simulated with perfect and realistic energy resolutions and including Doppler broadening.

Impact of the training loss in deep learning-based CT reconstruction of bone microarchitecture.

Purpose: Computed tomography (CT) is a technique of choice to image bone structure at different scales. Methods to enhance the quality of degraded reconstructions obtained from low-dose CT data have shown impressive results recently, especially in the realm of supervised deep learning. As the choice of the loss function affects the reconstruction quality, it is necessary to focus on the way neural networks evaluate the correspondence between predicted and target images during the training stage.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography.

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode.

Proton therapy monitoring by Compton imaging: influence of the large energy spectrum of the prompt-γ radiation.

In proton therapy, the prompt-γ (PG) radiation produced by the interactions between protons and matter is related to the range of the beam in the patient. Tomographic Compton imaging is currently studied to establish a PG image and verify the treatment. However the quality of the reconstructed images depends on a number of factors such as the volume attenuation, the spatial and energy resolutions of the detectors, incomplete absorptions of high energy photons and noise from other particles reaching the camera.

Probabilistic models and numerical calculation of system matrix and sensitivity in list-mode MLEM 3D reconstruction of Compton camera images.

This paper addresses the problem of evaluating the system matrix and the sensitivity for iterative reconstruction in Compton camera imaging. Proposed models and numerical calculation strategies are compared through the influence they have on the three-dimensional reconstructed images. The study attempts to address four questions.

Filtered backprojection reconstruction and redundancy in Compton camera imaging.

During the acquisition process with the Compton gamma-camera, integrals of the intensity distribution of the source on conical surfaces are measured. They represent the Compton projections of the intensity. The inversion of the Compton transform reposes on a particular Fourier-Slice theorem.

Fractional Gaussian noise, functional MRI and Alzheimer's disease.

Fractional Gaussian noise (fGn) provides a parsimonious model for stationary increments of a self-similar process parameterised by the Hurst exponent, H, and variance, sigma2. Fractional Gaussian noise with H < 0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with H > 0.

Wavelets and functional magnetic resonance imaging of the human brain.

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or "whitening" of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI.