Journal: NeuroImage

Article Title: Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives

doi: 10.1016/j.neuroimage.2021.117845

Figure Lengend Snippet: Comparison of tested de-facing techniques on an input 3D FLAIR scan. Top: sagittal MRI slice (brain is omitted for participant privacy) Bottom: corresponding face reconstruction. Note that mri_deface retained the eyes and part of the nose. Our reconstruction process removes floating disconnected voxels, so the remaining nose is not visible on the corresponding render. Pydeface retained the top of the eyes. Among the three standard methods, only fsl_deface removed the ears, and entirely removed the eyes. In our proposed mri_reface , all face regions and ear regions were replaced with an average face and ears. This volunteer consented to allow publication of their photographs and corresponding MRI-based reconstructions for illustration purposes.

Article Snippet: Sagittal 3D FLAIR head MRI were acquired using Siemens Prisma scanners using standard protocols matching those from ADNI3: resolution 1.0×1.0×1.2mm, repetition time = 4800ms, echo time = 441ms, and inversion time = 1650ms.

Techniques: Comparison

Journal: BioMedical Engineering OnLine

Article Title: A 3D MRI denoising algorithm based on Bayesian theory

doi: 10.1186/s12938-017-0319-x

Figure Lengend Snippet: An example of 26-pixel neighborhood \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {N}_k$$\end{document} N k ( red cubes ) for a selected location ( blue cube ) in a 3D stack of acquired images

Article Snippet: The simulated case study exploits Matlab ® 3D MRI head phantom, which is composed of 27 slices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$128 \times 128$$\end{document} 128 × 128 voxels.

Techniques:

Journal: BioMedical Engineering OnLine

Article Title: A 3D MRI denoising algorithm based on Bayesian theory

doi: 10.1186/s12938-017-0319-x

Figure Lengend Snippet: The processing chain of the proposed methodology. For each slice s , the 3D neighborhood is extracted by analyzing the upper ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s+1$$\end{document} s + 1 ) and lower ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$s-1$$\end{document} s - 1 ) slices. The hyperparameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} θ are evaluated and the estimation is performed. The procedure is iterated until convergence and repeated for all slices composing the 3D stack

Article Snippet: The simulated case study exploits Matlab ® 3D MRI head phantom, which is composed of 27 slices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$128 \times 128$$\end{document} 128 × 128 voxels.

Techniques:

Journal: BioMedical Engineering OnLine

Article Title: A 3D MRI denoising algorithm based on Bayesian theory

doi: 10.1186/s12938-017-0319-x

Figure Lengend Snippet: Specifications of filters used for comparison

Article Snippet: The simulated case study exploits Matlab ® 3D MRI head phantom, which is composed of 27 slices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$128 \times 128$$\end{document} 128 × 128 voxels.

Techniques: Diffusion-based Assay

Journal: BioMedical Engineering OnLine

Article Title: A 3D MRI denoising algorithm based on Bayesian theory

doi: 10.1186/s12938-017-0319-x

Figure Lengend Snippet: 3 T real dataset: imaging protocol details

Article Snippet: The simulated case study exploits Matlab ® 3D MRI head phantom, which is composed of 27 slices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$128 \times 128$$\end{document} 128 × 128 voxels.

Techniques: Imaging, Sequencing

Journal: BioMedical Engineering OnLine

Article Title: A 3D MRI denoising algorithm based on Bayesian theory

doi: 10.1186/s12938-017-0319-x

Figure Lengend Snippet: 1.5 T real axial dataset: imaging protocol details

Article Snippet: The simulated case study exploits Matlab ® 3D MRI head phantom, which is composed of 27 slices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$128 \times 128$$\end{document} 128 × 128 voxels.

Techniques: Imaging, Sequencing

Journal: BioMedical Engineering OnLine

Article Title: A 3D MRI denoising algorithm based on Bayesian theory

doi: 10.1186/s12938-017-0319-x

Figure Lengend Snippet: 1.5 T real sagittal dataset: imaging protocol details

Article Snippet: The simulated case study exploits Matlab ® 3D MRI head phantom, which is composed of 27 slices of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$128 \times 128$$\end{document} 128 × 128 voxels.

Techniques: Imaging, Sequencing

Journal: Scientific Reports

Article Title: Portable Wideband Microwave Imaging System for Intracranial Hemorrhage Detection Using Improved Back-projection Algorithm with Model of Effective Head Permittivity

doi: 10.1038/srep20459

Figure Lengend Snippet: ( a ) Perspective view of the 3D head model with an inset showing the target location, and ( b ) top view of the simulation environment. The arrows point toward the direction of antenna rotation around the 3D head model. ( c ) The raw data received by the antenna in different positions. ( d–f ) Various signals from closest antennas 10, 11 and 12 in the processing pipeline: ( d ) the reflection coefficient data, , ( e ) the time domain converted data, and ( f ) the refined scattered signals, after the adjacent average subtractions. Here the red, green and blue curves respectively represents the real, imaginary and absolute values of different signals. The suspected positions of the target is circled in signal. ( g–l ) The reconstructed images of the 3D human head model after simulating the head imaging system in numerical environment including all the frequency dispersive electrical properties of the human head with a haemorrhagic target inside. The images are reconstructed using the existing algorithm with fixed effective permittivity of ( g ) = 30, ( h ) = 35, ( i ) = 40, ( j ) = 45, ( k ) = 50 and ( l ) proposed model of effective permittivity. The red rectangle shows the actual position of the head injury.

Article Snippet: The previously developed 3D human head phantom is imported from MATLAB and the respective tissue types are assigned with their actual measured values .

Techniques: Imaging

Journal: Journal of Applied Clinical Medical Physics

Article Title: System‐dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction

doi: 10.1002/acm2.13826

Figure Lengend Snippet: (a) The sagittal view of the MRID 3D phantom with the onboard distortion correction turned (i) Off (DstOff) and (ii) On (DstOn) and (b) the axial view of the Fluke phantom with (i) DstOff and (ii) DstOn. In the Fluke phantom, the circular dots indicate the distortion analysis regions for the phantom and the blue and orange arrows indicate the 175 and 100 mm radius analysis regions, respectively.

Article Snippet: Recently, Lewis et al. used the lightweight magnetic resonance imaging distortion in 3D (MRID 3D ) phantom, and phantom‐dependent geometric distortion analysis software produced by Modus Medical Devices Inc. (Modus QA) to study the imaging distortion and isocenter shifts for multiple gantry angles and to generate distortion vector fields (DVFs) for the correction of the system‐dependent image distortion.

Techniques:

Journal: Journal of Applied Clinical Medical Physics

Article Title: System‐dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction

doi: 10.1002/acm2.13826

Figure Lengend Snippet: MRID 3D phantom images mean and the maximum (Max) geometric distortion error for DstOff original and resampled images at all gantry angles

Article Snippet: Recently, Lewis et al. used the lightweight magnetic resonance imaging distortion in 3D (MRID 3D ) phantom, and phantom‐dependent geometric distortion analysis software produced by Modus Medical Devices Inc. (Modus QA) to study the imaging distortion and isocenter shifts for multiple gantry angles and to generate distortion vector fields (DVFs) for the correction of the system‐dependent image distortion.

Techniques:

Journal: Journal of Applied Clinical Medical Physics

Article Title: System‐dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction

doi: 10.1002/acm2.13826

Figure Lengend Snippet: MRID 3D phantom images mean and the maximum geometric distortion error for DstOn original and resampled images at all gantry angles

Article Snippet: Recently, Lewis et al. used the lightweight magnetic resonance imaging distortion in 3D (MRID 3D ) phantom, and phantom‐dependent geometric distortion analysis software produced by Modus Medical Devices Inc. (Modus QA) to study the imaging distortion and isocenter shifts for multiple gantry angles and to generate distortion vector fields (DVFs) for the correction of the system‐dependent image distortion.

Techniques:

Journal: Journal of Applied Clinical Medical Physics

Article Title: System‐dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction

doi: 10.1002/acm2.13826

Figure Lengend Snippet: Mean distortion using MRID 3D phantom. The mean distortion values of the original and resampled images on the right–left (RL), anterior–posterior (AP), and superior–inferior (SI) directions across all gantry angles. Plots show the values of (a) distortion correction off (DstOff) with distortion correction off resampled (DstOff (R)) and (b) distortion correction on (DstOn) with distortion correction on resampled (DstOn(R)).

Article Snippet: Recently, Lewis et al. used the lightweight magnetic resonance imaging distortion in 3D (MRID 3D ) phantom, and phantom‐dependent geometric distortion analysis software produced by Modus Medical Devices Inc. (Modus QA) to study the imaging distortion and isocenter shifts for multiple gantry angles and to generate distortion vector fields (DVFs) for the correction of the system‐dependent image distortion.

Techniques:

Journal: Journal of Applied Clinical Medical Physics

Article Title: System‐dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction

doi: 10.1002/acm2.13826

Figure Lengend Snippet: Isocenter shift using MRID 3D phantom. The MRI isocenter distance to agreement (DTA) original and resampled for the (a) right–left (RL), (b) anterior–posterior (AP), and (c) superior–inferior (SI) directions across all gantry angles. Each plot includes the value for the distortion correction turned on (DstOn) and off (DstOff) for original images and DstOn(R), DstOff(R) for resampled images.

Article Snippet: Recently, Lewis et al. used the lightweight magnetic resonance imaging distortion in 3D (MRID 3D ) phantom, and phantom‐dependent geometric distortion analysis software produced by Modus Medical Devices Inc. (Modus QA) to study the imaging distortion and isocenter shifts for multiple gantry angles and to generate distortion vector fields (DVFs) for the correction of the system‐dependent image distortion.

Techniques:

Journal: Journal of Applied Clinical Medical Physics

Article Title: System‐dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction

doi: 10.1002/acm2.13826

Figure Lengend Snippet: The axial view of the human chest MR image acquired at gantry 300° showing the original image and resampled image using the extended DVF generated from the MRID 3D phantom. The images are shown with the onboard Siemens distortion correction function turned on (DstOn) and turned off (DstOff). The dotted blue square box indicates the overlayed original and resampled images. In the overlay images, the primary image (DstOn) upper color is shown in white, and the secondary image (DstOff) upper color is shown in light brown.

Article Snippet: Recently, Lewis et al. used the lightweight magnetic resonance imaging distortion in 3D (MRID 3D ) phantom, and phantom‐dependent geometric distortion analysis software produced by Modus Medical Devices Inc. (Modus QA) to study the imaging distortion and isocenter shifts for multiple gantry angles and to generate distortion vector fields (DVFs) for the correction of the system‐dependent image distortion.

Techniques: Generated