article · Figshare · 2016
Filter parameter optimization.
Dominique Van de Sompel (303197), Laura S. Sasportas (2587426), Jesse V. Jokerst (801981), Sanjiv S. Gambhir (1234206)
Cited by 0Open access
Abstract
<p><b>Figure A, Image reconstructions with Wiener deconvolution for different values of <i>σ</i></b>. Note that the brain slice appears shrunken without signal deconvolution. This is because the deconvolution also corrects for a slight time delay between the true arrival of the pressure waves at the detector surface and the actual pressure signal recorded by the scanner. This delay was present in all scans equally, including the scan in which the scanner impulse response function <i>p</i><sub><i>d</i>0</sub>(<i>t</i>) was measured. Without the deconvolution’s correction for this time delay, the backprojected signals are slightly shifted and misaligned in space. This misalignment in turn results in the shrunken appearance of the reconstructed brain slice. The spatial location of the brain slice within the reconstructed volume is identical for all reconstructions shown. The image intensities of the reconstructions are normalized (black: 0, white: 1), and the dimensions of the MIP images are 20 × 20 mm. <b>Figure B, Image reconstructions with Tikhonov deconvolution for different values of <i>β</i></b>. Note that the brain slice appears shrunken without signal deconvolution. This is because the deconvolution also corrects for a slight time delay between the true arrival of the pressure waves at the detector surface and the actual pressure signal recorded by the scanner. This delay was present in all scans equally, including the scan in which the scanner impulse response function <i>p</i><sub><i>d</i>0</sub>(<i>t</i>) was measured. Without the deconvolution’s correction for this time delay, the backprojected signals are slightly shifted and misaligned in space. This misalignment in turn results in the shrunken appearance of the reconstructed brain slice. The spatial location of the brain slice within the reconstructed volume is identical for all reconstructions shown. The image intensities of the reconstructions are normalized (black: 0, white: 1), and the dimensions of the MIP images are 20 × 20 mm.</p> <p>(PDF)</p>
Cite this paper
(303197), D. V. D. S., (2587426), L. S. S., (801981), J. V. J., & (1234206), S. S. G. (2016). Filter parameter optimization. Figshare. https://doi.org/10.1371/journal.pone.0152597.s001
(303197), Dominique Van de Sompel, et al. “Filter parameter optimization.” Figshare, 2016, https://doi.org/10.1371/journal.pone.0152597.s001.
(303197), Dominique Van de Sompel, Laura S. Sasportas (2587426), Jesse V. Jokerst (801981), and Sanjiv S. Gambhir (1234206). 2016. “Filter parameter optimization.” Figshare. https://doi.org/10.1371/journal.pone.0152597.s001.
(303197), D.V.D.S. et al. (2016) ‘Filter parameter optimization’, Figshare. Available at: https://doi.org/10.1371/journal.pone.0152597.s001.
@article{3031972016filter,
title = {{Filter parameter optimization.}},
author = {(303197), Dominique Van de Sompel and (2587426), Laura S. Sasportas and (801981), Jesse V. Jokerst and (1234206), Sanjiv S. Gambhir},
journal = {Figshare},
year = {2016},
doi = {10.1371/journal.pone.0152597.s001}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.