We present a non-contact diffuse optical tomography (DOT) scanner with multi-view detection (over 360°) for localizing fluorescent markers in scattering and absorbing media, in particular small animals. It relies on time-domain detection after short pulse laser excitation. Ultrafast time-correlated single photon counting and photomultiplier tubes are used for time-domain measurements. For light collection, seven free-space optics non-contact dual wavelength detection channels comprising 14 detectors overall are placed around the subject, allowing the measurement of time point-spread functions at both excitation and fluorescence wavelengths. The scanner is endowed with a stereo camera pair for measuring the outer shape of the subject in 3D. Surface and DOT measurements are acquired simultaneously with the same laser beam. The hardware and software architecture of the scanner are discussed. Phantoms are used to validate the instrument. Results on the localization of fluorescent point-like inclusions immersed in a scattering and absorbing object are presented. The localization algorithm relies on distance ranging based on the measurement of early photons arrival times at different positions around the subject. This requires exquisite timing accuracy from the scanner. Further exploiting this capability, we show results on the effect of a scattering hetereogenity on the arrival time of early photons. These results demonstrate that our scanner provides all that is necessary for reconstructing images of small animals using full tomographic reconstruction algorithms, which will be the next step. Through its free-space optics design and the short pulse laser used, our scanner shows unprecedented timing resolution compared to other multi-view time-domain scanners.
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PeerJ Comput Sci
March 2024
Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
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January 2024
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View Article and Find Full Text PDFInt J Comput Vis
June 2023
Department of Electrical Engineering, National Tsing Hua University, Kuang-Fu Road, Hsinchu, 30048 Taiwan.
Unlabelled: Terahertz (THz) tomographic imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The diffraction-limited THz signals highly constrain the performances of existing restoration methods.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
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Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on parameter design to extract features, whereas deep learning algorithms represented by convolution neural network (CNN) can automatically extract features, which is more suitable for BCI applications.
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