In measuring cerebral blood flow (CBF) noninvasively using optical techniques, diffusing-wave spectroscopy is often combined with near-infrared spectroscopy to obtain a reliable blood flow index. Measuring the blood flow index at a determined depth remains the ultimate goal. In this study, we present a simple approach using dual-comb lasers where we simultaneously measure the absorption coefficient (μ_a), the reduced scattering coefficient (μ_s^′), and dynamic properties. This system can also effectively differentiate dynamics from various depths, which is crucial for analyzing multilayer dynamics. For CBF measurements, this capability is particularly valuable as it helps mitigate the influence of the scalp and skull, thereby enhancing the specificity of deep tissue.

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In direct time-of-flight (D-TOF) light detection and ranging (LIDAR), accuracy and full-scale range (FSR) are the main performance parameters to consider. Particularly, in single-photon avalanche diodes (SPAD) based systems, the photon-counting statistics plays a fundamental role in determining the LIDAR performance. Also, the intrinsic performance ultimately depends on the system parameters and constraints, which are set by the application. However, the best-achievable performance directly depends on the selected depth estimation method and is not necessarily equal to intrinsic performance. We evaluate a D-TOF LIDAR system, in the particular context of smartphone applications, in terms of parameter trade-offs and estimation efficiency. First, we develop a simulation model by combining radiometry and photon-counting statistics. Next, we perform a trade-off analysis to study dependencies between system parameters and application constraints, as well as non-linearities caused by the detection method. Further, we derive an analytical model to calculate the Cramér–Rao lower bound (CRLB) of the LIDAR system, which analytically accounts for the shot noise. Finally, we evaluate a depth estimation method based on artificial intelligence (AI) and compare its performance to the CRLB. We demonstrate that the AI-based estimator fully compensates the non-linearity in depth estimation, which varies depending on application conditions such as target reflectivity.

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Positron Emission Tomography (PET) is one of the most relevant medical imaging techniques utilized for cancer detection and tumor staging. The success of PET relies on the high sensitivity and accuracy to detect and quantify molecular probe concentrations, in the order of picomole/liter. Although there are several positron-emitting molecular probes available, the 18F-fludeoxyglucose (18F-FDG) contributes remarkably to the high PET specificity and sensitivity. Since the success of PET imaging is strongly connected to the 18F-FDG, this imaging technique is also known as FDG-PET. In FDG-PET imaging three elements are key: - the molecular probe, - a PET scanner, - and an image reconstruction algorithm. The molecular probe is the contrast enhancement agent, which is administrated to the patient and absorbed by the target volumes. The emitted radiation produced by electron-positron annihilation is detected by the PET scanner, and the detection information is utilized to reconstruct a volumetric probe distribution. In essence, a PET scanner is a large acquisition system composed of thousands of channels that detect coincident gamma-photons generated during electron-positron annihilations. Typically, a single detection channel is composed of a scintillation material and a photodetector. The scintillation material absorbs the gamma-energy and emits light photons that produce digital or analog signals in the photodetectors. Nowadays, novel silicon-based photodetectors known as silicon photomultipliers (SiPMs) have been adopted as the next-generation photodetectors for PET applications. In order to further improve the FDG-PET molecular sensitivity and specificity, next-generation instrumentation requires a more accurate time estimation of the detected gamma-photon. Since in time-of-flight (TOF) PET the reconstructed images have an improved signal-to-noise ratio (SNR), which depends on the gamma-photon timemark precision. Additionally, increasing the detection sensitivity improves the statistical quality of information utilized during the image reconstruction process. This thesis introduces the basic concepts of molecular imaging and the key elements of FDG-PET in chapters 1 and 2. A comprehensive theoretical analysis on the utilization of the scintillation light information for gamma-photon timemark estimation is presented in chapter 3. Several estimation methods, such as maximum-likelihood estimation (MLE) and best linear unbiased estimation (BLUE) are presented, as well as a performance comparison with respect to the Cramér-Rao lower bound. Additionally, a detailed study is performed to determine the conditions that allow to reach the Cramér-Rao lower bound. Currently, FDG-PET imaging equipment is not equally available worldwide and one of the reasons is the high costs involved. Often, the design and implementation of TOF-PET instrumentation requires application specific integrated circuit (ASIC) designs, which increases the complexity of the design and required long prototyping phases. Chapter 4 describes the design, implementation, and characterization of TOF-PET instrumentation based on off-the-shelf components, configurable time-to-digital converters (TDCs) implemented on field-programmable gate arrays (FPGAs), and analog SiPMs (A-SiPMs). The proposed solution achieves TOF precision with a full-flexible, fast-prototyping, and ASIC-less designs. Recently, digital SiPMs (D-SiPMs) emerged as a next-generation photodetector for PET applications. In particular, the multichannel digital SiPM (MD-SiPM) architecture integrates single-photon avalanche diodes (SPADs), TDCs, and a readout logic into a monolithic CMOS photodetector. This type of photodetector confines all the measurement devices and circuits within an integrated solution. Therefore, it allows a direct system integration of a large number of channels since only digital signals are required for its operation. However, D-SiPM research and development requires long development and integration cycles due to the high complexity involved. Chapter 5 describes an individual building block and full-system comprehensive analysis of a monolithic array of 18x9 MD-SiPMs. Additionally, it describes in detail the methods developed for multiple TDC systems. In chapter 6, the system integration of MD-SiPMs for building PET detector modules is explained. The challenges of utilizing complex photodetectors for building PET modules, attachment of scintillator matrices, and digital readout strategies are described in a comprehensive manner. Finally, a conclusion of the PET technologies investigated throughout this thesis is given. In addition, an outlook of newer detection methods based on Cherenkov-PET and the corresponding requirements and eventual advantages is discussed.@en

Silicon Photomultipliers (SiPMs), which emerged as all solid state, MRI compatible alternative to PMTs, can provide high miniaturization and increased timing performance. Large effort has been spent in improving the figures of merit of such devices (e.g. jitter, timing resolution, sensitivity, noise, etc.). In this paper we propose a novel approach relying on the combination of photonic crystals with microlens arrays, integrated on top of each SPAD cell in the SiPM, to collimate and focus the light generated by a scintillator, in order to improve the overall timing performance of the system so as to approach the 10ps target, while at the same time reducing the noise floor (Dark Count Rate).

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We present a design that implements digitization of an analog SiPM's fast output on chip to pave the way to higher granularity in the digital conversion of photon detection. The design comprises a comparator bank, time-to-digital converters (TDCs), and electronics for interfacing with the external world. The TDC is a multipath, gated ring oscillator with a counter and phase detector, implemented in 0.35μm CMOS technology. Simulation results indicate a DNL of ±0.55LSB and an INL of ±1LSB, a large, adjustable range, and a typical resolution of 65ps (LSB).

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Typically, a time-of-flight (TOF) PET block detector is built using application-specific integrated circuits (ASICs), since they integrate a high number of channels at a reasonable power consumption and into a small area. However, ASICs’ flexibility is limited and prototyping times are long because a semiconductor fabrication process is required in every design iteration. Alternatively, fast terminal (FT) silicon photomultipliers (SiPMs) require a simplified analog front-end in order to achieve time-of-flight (TOF) accuracy. In addition, field-programmable gate arrays (FPGAs) can allocate time-to-digital converters (TDCs) as well as complex digital readout logics. In this work, we propose building TOF-PET block detectors based on FPGAs, FT-SiPMs, and minimal amount of off-the-shelf components. In this way, TOF-PET accuracy is achieved with a full-flexible and fast prototyping solution. We evaluated the coincidence resolving time (CRT), performance degradations due to channel multiplexing, energy resolution, and scintillator pixel encoding performance of SiPM arrays utilizing the proposed approach. Experimental results show minimal timing degradations, when multiplexing FTs. Moreover, simulation results show a low reduction in the singles count rate of multiplexed channels at typical brain-PET radioactive doses.@en

In time-of-flight (TOF) positron emission tomography (PET), the coincidence resolving time (CRT) has a strong influence on the overall performance. Multichannel digital silicon photomultipliers (MD-SiPMs) are able to obtain several timestamps for gamma photon timemark estimation. Using this feature, the CRT is improved and the system robustness is significantly increased by utilizing multiple photoelectron timestamps. In addition, the PET instrumentation chain is simplified because of the intrinsic digitization and integrated functionality of the MD-SiPM. The main objective of this work is to demonstrate the possibility of building a complete highly-miniaturized PET detector module for endoscopic applications. In addition, we show that it's possible to operate simultaneously several MD-SiPM array chips in order to build a small-animal PET detector modules. We present the implementation of two PET detector modules that are based on MD-SiPMs: a small animal and an endoscopic PET detector modules. The small animal PET detector module consists of 2×4 monolithic MD-SiPM array chips. In addition, this module includes a low-cost field programmable gate array (FPGA), a temperature controlling system and data transfer interfaces. The endoscopic PET detector module comprises a single monolithic array of 9×18 MD-SiPM and a small form-factor FPGA. In this module, a remarkable level of compactness is achieved. Eventually, a thermal characterization and a preliminary radiation measurement are presented.

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In this paper we present a silicon photomultiplier (SiPM) readout system based on a field-programmable gate array (FPGA), which is capable of converting any commercial 16-channel analog SiPM array into a hybrid device with fullydigital readout for application in time of flight positron emission tomography (TOF-PET). In principle this hybrid SiPM array can be implemented with several leading edge discriminators (LEDs) per channel, so that multiple timestamps can be acquired per scintillation pulse allowing to estimate the time of interaction more robustly than if a single timestamp is used. These concepts were studied experimentally in two different ways. In the first approach, we utilized discrete components in combination with an array of time-to-digital converters (TDCs) implemented and validated on the FPGA. The overall resolution of each channel was 56 ps FWHM or better, while crosstalk was undetectable. In the second approach, we utilized a four-channel high-speed acquisition board operating at 5 GSPS and emulated a multiple-LED acquisition by post-processing the digitized waveforms. We present, for the first time, experimental results obtained with the best linear unbiased estimator (BLUE) to estimate the time of interaction from the multiple timestamps.

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