Journal: Frontiers in Neuroscience

Article Title: Group-level component analyses of EEG: validation and evaluation

doi: 10.3389/fnins.2015.00254

Figure Lengend Snippet: Basic concepts of group analyses using temporal concatenation and multilevel decomposition in combination with ICA or SOBI . For temporal concatenation, data aggregation yields a horizontally elongated matrix on which the demixing matrix W can be estimated, assuming the same mixing process for all subjects. This, however, is not the case with multilevel decomposition since single-subject as well as group-level decomposition prior to ICA/SOBI not only reduce the number of variables, but also allow for some variability of the latent structure across subjects. Note that usually only a subset of the c*n vertically concatenated components (c = number of channels/components, n = number of subjects) enter final decomposition via ICA or SOBI.

Article Snippet: Notable exceptions are the implementation of Infomax ICA, SOBI, as well as functions for data filtering and plotting of EEG topographies; for these tasks, routines of the MATLAB-based open source software package EEGLAB were used (Delorme and Makeig, ).

Techniques:

Journal: Journal of Biomedical Optics

Article Title: Multidomain computational modeling of photoacoustic imaging: verification, validation, and image quality prediction

doi: 10.1117/1.jbo.24.12.121910

Figure Lengend Snippet: Fig. 4 Reconstructed photoacoustic images of filament phantom (phantom 2) for (a) simulated and (b) experimental RF data and (c) computed axial and (d) lateral resolution from simulated and exper- imental data. The color bar is in dB.

Article Snippet: MC has been used to compare performances of different PAI device designs,20,34–41 to evaluate target lesion visualization and detectability,39,42 and to enable quantitative PAI.43,44 Common tools for modeling acoustic wave propagation in tissue include Field II,45 which has been used to simulate photoacoustic response and quantify spatial resolution of a proposed PAI system,46,47 and k-Wave,48,49 a popular open-source photoacoustic simulation toolbox for MATLAB used by several groups to study PAI systems.42,43 For PAI simulation, several groups have proposed multidomain finite element models based on commercial software (e.g., COMSOL)50,51 or open-source packages (e.g., ONELAB)52 to simulate photoacoustic processes by explicitly modeling heat transfer, solid mechanics, and acoustic wave propagation.

Techniques:

Journal: Journal of Biomedical Optics

Article Title: Multidomain computational modeling of photoacoustic imaging: verification, validation, and image quality prediction

doi: 10.1117/1.jbo.24.12.121910

Figure Lengend Snippet: Fig. 5 Upper row: Reconstructed photoacoustic images from penetration depth phantom (phantom 3) for (a) and (b) low-absorbing and (c) and (d) medium-absorbing background, using (a) and (c) experimental and (b)–(d) simulated data. Data are normalized to the intensity of the shallowest target intensity. The color bar is in dB. Lower row: line plot across second target (white line in a) for depth of 5 to 20 mm.

Article Snippet: MC has been used to compare performances of different PAI device designs,20,34–41 to evaluate target lesion visualization and detectability,39,42 and to enable quantitative PAI.43,44 Common tools for modeling acoustic wave propagation in tissue include Field II,45 which has been used to simulate photoacoustic response and quantify spatial resolution of a proposed PAI system,46,47 and k-Wave,48,49 a popular open-source photoacoustic simulation toolbox for MATLAB used by several groups to study PAI systems.42,43 For PAI simulation, several groups have proposed multidomain finite element models based on commercial software (e.g., COMSOL)50,51 or open-source packages (e.g., ONELAB)52 to simulate photoacoustic processes by explicitly modeling heat transfer, solid mechanics, and acoustic wave propagation.

Techniques:

Journal: Journal of Biomedical Optics

Article Title: Multidomain computational modeling of photoacoustic imaging: verification, validation, and image quality prediction

doi: 10.1117/1.jbo.24.12.121910

Figure Lengend Snippet: Fig. 7 Energy deposition maps and corresponding simulated photo- acoustic images for (a) and (b) 0.8- and 12.6-mm circular beams and (c) and (d) elliptical beams of size 0.25 mm × 2.5 mm and 4 mm × 40 mm. The small lower-right figure in each energy deposi- tion map is an en face view of beam fluence at the phantom surface, which were self-normalized for visualization purposes. All beam cases used a fixed uniform radiant exposure of 10 mJ∕cm2. Energy deposition colorbars in mJ∕cm3, photoacoustic image colorbars in dB.

Article Snippet: MC has been used to compare performances of different PAI device designs,20,34–41 to evaluate target lesion visualization and detectability,39,42 and to enable quantitative PAI.43,44 Common tools for modeling acoustic wave propagation in tissue include Field II,45 which has been used to simulate photoacoustic response and quantify spatial resolution of a proposed PAI system,46,47 and k-Wave,48,49 a popular open-source photoacoustic simulation toolbox for MATLAB used by several groups to study PAI systems.42,43 For PAI simulation, several groups have proposed multidomain finite element models based on commercial software (e.g., COMSOL)50,51 or open-source packages (e.g., ONELAB)52 to simulate photoacoustic processes by explicitly modeling heat transfer, solid mechanics, and acoustic wave propagation.

Techniques:

Journal: Journal of Biomedical Optics

Article Title: Multidomain computational modeling of photoacoustic imaging: verification, validation, and image quality prediction

doi: 10.1117/1.jbo.24.12.121910

Figure Lengend Snippet: Fig. 9 Reconstructed photoacoustic images of filament phantom (phantom 2) using ultrasound trans- ducer arrays with varying center frequency (columns) as well as fractional bandwidth of 50% (top row) and 100% (bottom row). Each image was normalized to its maximum target intensity.

Article Snippet: MC has been used to compare performances of different PAI device designs,20,34–41 to evaluate target lesion visualization and detectability,39,42 and to enable quantitative PAI.43,44 Common tools for modeling acoustic wave propagation in tissue include Field II,45 which has been used to simulate photoacoustic response and quantify spatial resolution of a proposed PAI system,46,47 and k-Wave,48,49 a popular open-source photoacoustic simulation toolbox for MATLAB used by several groups to study PAI systems.42,43 For PAI simulation, several groups have proposed multidomain finite element models based on commercial software (e.g., COMSOL)50,51 or open-source packages (e.g., ONELAB)52 to simulate photoacoustic processes by explicitly modeling heat transfer, solid mechanics, and acoustic wave propagation.

Techniques: