A multi-wavelength diffuse optical tomography (DOT) system has been developed to directly extract physiological information, such as total haemoglobin concentration, from tissue in human hand joints. Novel methods for 3D surface imaging and spectrally constrained image reconstruction are introduced and their potential application to imaging of rheumatoid arthritis is discussed. Results are presented from simulation studies as well as experiments using phantoms and data from imaging of three healthy volunteers. The image features are recovered partially for phantom data using transmission measurements only. Images that reveal joint regions and surrounding features within the hand are shown to co-register with co–acquired ultrasound images which are shown to be related to total haemoglobin concentration.

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The data analysis on tokamak plasmas is mainly based on various diagnostic systems, which are usually modularized and independent of each other. This leads to a large amount of data not fully and effectively exploited, so that it is not conducive to reveal the deep physical mechanism. In this paper, Bayesian probability inference (BPI) with machine learning methods has been applied to the electron cyclotron emission (ECE) and Thomson scattering (TS) diagnostic systems on HL-2A/2M, and the effects of integrated data analysis (IDA) on the electron temperature of HL-2A with Bayesian probability inference are demonstrated. A program is developed to infer the whole electron temperature profile with a confidence interval, and the program can be applied in the online analysis. The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA. The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool of HL-2A/2M in the near future.

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