Metropolis Monte Carlo for tomographic reconstruction with prior smoothness information

Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography

“…Very similar approach has been recently proposed by Barbuzza and Clausse (2011) for tomographic reconstructions in the field of material science radiography. They use voxels of their volume as samples and perform one random walk guided by MH.…”

“…In the field of radiography, a new technique for tomographic reconstruction was proposed in a work of Barbuzza and Clausse (2011), where Monte Carlo Minimization (MCM) technique together with Metropolis-Hastings (MH) (Metropolis et al, 1953;Hastings, 1970) sampling strategy was used for reconstruction. This idea was further extended in the work of Gregson et al (2012) for visible light 3D imaging of fluid dynamics.…”

Characterisation framework for epileptic signals

“…5. For each interval j, obtain y k as the approximation of y k using (5). Note that several algorithms of all the intervals are joined using (5).…”

“…One part of artificial vision has been worked to obtain important information from the medical images as are the works of Fallahpour et al [1], Li et al [2], Wu et al [3], Karimi et al [4] and Barbuzza and Clausse [5].…”

Progressive stochastic reconstruction technique for cryo electron tomography

“…Gordon and Herman [1971] first introduced Monte Carlo in Electron Tomography but their approach has never found a widespread use. Recently, methods based on Metropolis-Hastings (MH) were proposed [Barbuzza and Clausse 2011;Gregson et al 2012] for other application domains, but require a-priori information and/or regularizers to obtain results with a sufficient quality. We extend these MH algorithms to match the specifics of cryoET, thereby improving the contrast and signal-to-noise ratio without the need for any regularizers or a-priori information about the target specimen.…”