Optical imaging methods capable of providing real-time information related to functional and structural conditions of living tissues are becoming increasingly popular.1–6 Some medical fields in particular, such as surgery, are driving this need for real-time interpretable information content to aid decision-making in a time-constrained environment.7–12 Several solutions have been investigated to fulfill this need, each with their pros and cons, ranging from raster scanning of microscopic information to wide-field acquisitions of diffused information.13,14 For instance, fluorescence imaging to help surgeons visualizing structures of interest, such as lymph nodes, ureters, or micrometastasis, stands as an example where providing tissue-related information in real time plays a key role in clinical adoption.15–18

Over the last decade, extensive progress has been made in diffuse optics toward developing quantitative, wide field, rapid, and inexpensive methods to visualize the physiological properties of living tissues. Among the numerous approaches proposed in this direction, the most recent ones rely on the spatial modulation of the light and processing in the frequency domain.^{1 – 3} Spatial frequency-domain imaging (SFDI) techniques allow the measurement of tissues absorption and reduced scattering optical properties over an entire image at once, which are used to retrieve physiological tissues parameters.^{4 – 10} However, major limitations of such imaging system include the necessity to project several patterns of light and to illuminate sequentially at several wavelengths to extract physiological parameters, impeding the use of such technology for real-time imaging.

The development of real-time, wide-field and quantitative diffuse optical imaging methods is becoming increasingly popular for biological and medical applications. Recent developments introduced a novel approach for real-time multispectral acquisition in the spatial frequency domain using spatio-temporal modulation of light. Using this method, optical properties maps (absorption and reduced scattering) could be obtained for two wavelengths (665 nm and 860 nm). These maps, in turn, are used to deduce oxygen saturation levels in tissues. However, while the acquisition was performed in real-time, processing was performed post-acquisition and was not in real-time. In the present article, we present CPU and GPU processing implementations for this method with special emphasis on processing time. The obtained results show that the proposed custom direct method using a General Purpose Graphic Processing Unit (GPGPU) and C CUDA (Compute Unified Device Architecture) implementation enables 1.6 milliseconds processing time for a 1 Mega-pixel image with a maximum average error of 0.1% in extracting optical properties.

In this paper, two of the most common calibration methods of synchronous TDCs, which are the bin-by-bin calibration and the average-bin-width calibration, are first presented and compared. Then, an innovative new robust calibration method for asynchronous TDCs is proposed and evaluated. Simulation results showed that: (i) For a synchronous TDC, the bin-by-bin calibration, applied to a histogram, does not improve the TDC’s differential non-linearity (DNL); nevertheless, it improves its Integral Non-Linearity (INL), whereas the average-bin-width calibration significantly improves both the DNL and the INL. (ii) For an asynchronous TDC, the DNL can be improved up to 10 times by applying the bin–by-bin calibration, whereas the proposed method is almost independent of the non-linearity of the TDC and can improve the DNL up to 100 times. The simulation results were confirmed by experiments carried out using real TDCs implemented on a Cyclone V SoC-FPGA. For an asynchronous TDC, the proposed calibration method is 10 times better than the bin-by-bin method in terms of the DNL improvement.

During the last decade, the design of mixed signal systems on chip has been in a continuous expansion. The advance in systems integration allows the implementation of millions of active or passive pixel sensors (APS or PPS) on the same chip (image matrix). It has become crucial for the design of these systems to accurately predict their behaviour prior to manufacture. Now, the emergence of standard description languages as VHDL-AMS offers an effective and efficient way to describe these multiple domain and mixed-signal electronic systems. The aim of this work is the modelling of pixel sensors, basic cells of CMOS image systems. Pixel sensors are composed of photodetectors delivering an output current and associated circuitry for conversion and/or pre-amplification of this current. PPS are usually associated to operational amplifiers. Hence, in this paper we present models for each one of these parts and we instantiate them to simulate pixel sensors.

We present the performance characteristics of a Single Photon Avalanche Detector fabricated in a 180 nm standard CMOS image sensor technology. The SPAD implemented in 8 different diameters between 5 μm and 40 μm shows a DCR below 10 kHz at 15°C with a low afterpulsing probability (0.2% at 300 mV), a good photodetection efficiency (20%) and a very good time resolution (80 ps at 450 nm).