Image reconstruction for synchronous data acquisition in fluorescence molecular tomography.

In some of the sensor network applications, low latency and reserved bandwidth to a particular device may have a significant importance. Guaranteed Time Slot (GTS) mechanism defined in IEEE 802.15.4 Standard provides solutions for such necessities. In this study, performance of GTS is measured on 13192 Evolution Kit modules from Freescale Semiconductor. This performance is compared with the theoretical throughput and maximum goodput values. Besides the performance measurements, synchronous data acquisition with two sensor nodes has been successfully realized. Synchronization of sensor nodes or chronologically sorting the events happened in a WSN are important phenomenon that need to be considered carefully for direction of arrival, array signal processing or surveillance applications. In order to synchronize the nodes beacon notification indication primitive defined in the 802.15.4 standard is used in the developed applications. Thus, each reception of beacon from the coordinator of the WSN resynchronizes the nodes.

In this study, an integrated synchronous data acquisition subsystem for high-speed dual-channel ultra-wideband ground penetrating radar (GPR) system is designed and tested. Factors that could cause data missing and asynchronisation are analyzed. According to timing sequence, a field-programmable gate array-based onboard digital control module is integrated to synchronise the data acquisition and data storage subsystems. By applying simultaneous multi-buffer acquisition and readout mode and multi-threading configuration, as well as the critical section control and queue storage structure, the GPR data throughput is considerably improved. For the practicality of this system, a smart End-of-Job control design is performed between different threads to control data acquisition. By using different performance indexes, receiver operation characteristic curve, maximum scan rate, and the rebar detection experiment demonstrate the superiority of author's method.

Aiming at the requirement of synchronous multi-channel data acquisition in large-scale acquisition netwrok, it presents a distributed measurement and control system that is based on EtherCAT with real-time ethernet technology and distributed clock. It ensures the synchronization, timeliness and ordering of the system. And the board-level data synchronization acquisition was implemented by device selection and signal management of SYNC0 and SYNC1. The system has the advantages such as good synchronization, simple realization, high data rate, strong extensibility, etc. The results showed that data transfer rate of 100Mbps, and the systems achieved a less than 750ns synchronous error over 300m.

To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm. Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDIvol : 0.9, 1.2, 3.6, 7.0, and 22.3mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (fav ) and frequency at which the TTF was reduced to 50% (f50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDIvol : 1.0, 2.1, 3.0, 6.0, and 10.0mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, , following AAPM TG233 methods. All measurements were compared among the algorithms. Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74±6% and 68±4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55±4% lower NPS fav compared to FBP while True Fidelity had only marginally different noise frequency content with 9±5% lower NPS fav compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0mGy dose level values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f50% of up to 42% and 36%, respectively. The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

In digital holography, holograms are recorded by a CCD-array, and the complex amplitude of the object wave is numerically reconstructed via computer. For different recording conditions and different properties of objects, different reconstruction algorithms are required. The conventional reconstruction algorithms were conceived directly by replacing the diffraction integral with summation. Each method has its limitation in the valid range for correctly calculating the diffraction integral. The Single Fourier Transform method is valid for far Fresnel zone hologram, whereas the convolution method is appropriate for near Fresnel holograms. Here, we present a general reconstruction model from the perspective of “Generalized sampling theory”. Given that the function space in which the unknown complex amplitude lies, an approximation of the continuous complex amplitude at the CCD can be synthesized from a set of basis functions with the recorded samples as weights. Back-propagation of the approximated complex amplitude to the original object plane yields an expression relating the continuous complex amplitude of the object with the recorded samples. By adopting different basis functions and different formulas for describing the diffraction process, an optimal reconstruction algorithm can be developed for various recording conditions and different diffraction characteristics of the object. Contrary to the conventional algorithms where values are available only at specific grid, complex amplitude at any position of the object can be obtained using this model. In addition, the effect due to the non-zero fill factor of the CCD can also be incorporated into the reconstruction algorithm to be further compensated by over-weighting the high frequency components. Two basis functions: Dirac delta- and Sinc-, are studied in detail.

We present a real-time data acquisition (DAQ) system for the long-distance reflective ghost imaging experiment which has broad application prospects in remote sensing. In the experimental setup, the light from a pseudo thermal light source is separated into signal beam and reference beam. The signal beam illuminates the target, and then the reflected light is collected by a bucket detector. In the mean time, the reference beam is directly captured by an area detector. In order to obtain the target image, the experiment requires high-speed and high-precision correlated data acquisition to collect the light intensity fluctuation in the signal beam and the reference beam. Furthermore, to reduce imaging time, real-time data acquisition is also required. The DAQ system consists of a photon-multiplier tube (PMT), a charge-coupled device (CCD) camera, a DAQ module and DAQ software. The CCD camera as the area detector connects to computer via Gigabit Ethernet. The PMT and a telescope compose the bucket detector which outputs signals to the DAQ module. The DAQ module sends acquired data to computer through universal serial bus (USB). The DAQ software collects the data and reconstructs the target image. The results of electronic tests and experiments show that the DAQ system satisfies the requirements of the experiment.

In order to get more azimuthal information of formation anisotropy, the array of acoustic receiver is employed in latest acoustic logging tools. As a part of basic experimental apparatus of acoustic logging, a synchronous 32-channel high performance acoustic data acquisition with 16-bit resolution, 1MSPS and 32K sampling depth is designed. This system is based on principal and subordinate structure. The host PC and embedded front-end interconnect via Ethernet. The embedded front-end based on ARM-uClinux extends a self-define bus, applying the physical structure of the PC104 module, to access acoustic data acquisition modules. Each acquisition module has eight analog-to-digital converters. Acquisition modules share the same start conversion signal and low noise, high accuracy voltage reference. To isolate the noise of the embedded front-end from ADC circuit, high speed optcoupler is used to form a local high speed serial bus. System control logic and software design of host PC and embedded front-end are discussed. In addition, design of low noise, low power acoustic signal amplifier is described. This acquisition system has been used in three-dimensional acoustic logging detector design successfully.

Wireless sensor nodes for generic signal conditioning: Application to Structural Health Monitoring of wind turbines

Imaging plays a vital role in radiation therapy and with recent advances in technology considerable emphasis has been placed on cone-beam CT (CBCT). Attaching a kV x-ray source and a flat panel detector directly to the linear accelerator gantry has enabled progress in target localization techniques, which can include daily CBCT setup scans for some treatments. However, with an increasing number of CT scans there is also an increasing concern for patient exposure. An intensity-weighted region-of-interest (IWROI) technique, which has the potential to greatly reduce CBCT dose, in conjunction with the chord-based backprojection-filtration (BPF) reconstruction algorithm, has been developed and its feasibility in clinical use is demonstrated in this article. A nonuniform filter is placed in the x-ray beam to create regions of two different beam intensities. In this manner, regions outside the target area can be given a reduced dose but still visualized with a lower contrast to noise ratio. Image artifacts due to transverse data truncation, which would have occurred in conventional reconstruction algorithms, are avoided and image noise levels of the low- and high-intensity regions are well controlled by use of the chord-based BPF reconstruction algorithm. The proposed IWROI technique can play an important role in image-guided radiation therapy.

Learning-based methods have been proved to perform well in a variety of areas in the biomedical field, such as biomedical image segmentation, and histopathological image analysis. Deep learning, as the most recently presented approach of learning-based methods, has attracted more and more attention. For instance, massive researches of deep learning methods for image reconstructions of computed tomography (CT) and magnetic resonance imaging (MRI) have been reported, indicating the great potential of deep learning for inverse problems. Optical technology-related medical imaging modalities including diffuse optical tomography (DOT), fluorescence molecular tomography (FMT), bioluminescence tomography (BLT), and photoacoustic tomography (PAT) are also dramatically innovated by introducing learning-based methods, in particular deep learning methods, to obtain better reconstruction results. This review depicts the latest researches on learning-based optical tomography of DOT, FMT, BLT, and PAT. According to the most recent studies, learning-based methods applied in the field of optical tomography are categorized as kernel-based methods and deep learning methods. In this review, the former are regarded as a sort of conventional learning-based methods and the latter are subdivided into model-based methods, post-processing methods, and end-to-end methods. Algorithm as well as data acquisition strategy are discussed in this review. The evaluations of these methods are summarized to illustrate the performance of deep learning-based reconstruction.

The equipment that the payload electronic control box matches the satellite Charge-Coupled Device (CCD) camera must be rigorously tested before being put into use. The traditional testing process is to make it with the bulky CCD camera system docking and then to capture a certain image to analyze, this will cause a waste of time and material. In consideration for the above problems, a simulative CCD camera image resource system is proposed in this paper. The control signal of a simulative CCD camera image resource is usually implemented in the Field Programmable Gate Array (FPGA), thus increasing the complexity of the hardware language, and stability will fall and making post-adjustment is more trouble. This paper presents a system which is controlled based on the host computer software. It makes complex code integrate into the host computer and sets aside a adjust interface to the software in advance, achieving with the FPGA circuit board to send the picture generated by the system cycle together. Sending images, image display and serial communication are usually for a thread in the general simulative CCD camera image resource system, this will inevitably cause some problems that often appear in the process. However, the system utilizes a three-run thread synchronization mode which makes the system more flexible, very easy to adjust. Extensive tests show that the simulative CCD camera image resource system has been running with satisfactory results. So it can replace CCD camera to test the payload electrical control box, which is stable and reliable test for the project and provides a strong support.

The paper designed a multi-channel data acquisition system based on PXI 6281. It used DAQmx technology in LabVIEW for synchronous analog channel data acquisition. It realized multi-channel real-time data acquisition, processing, display and data auto-saving. Data acquisition system consists of three major modules: the pressure measurement module, the motor speed measurement modules and real-time temperature measurement module. It has advantage of short cycle of technological upgrading, development and low maintenance costs. It has some reference value for other data acquisitions.

In this paper, a unified framework of iterative algebraic reconstruction for emission computed tomography (ECT) and its application to positron emission tomography (PET) is presented. The unified framework is based on an algebraic image restoration model and contains conventional iterative algebraic reconstruction algorithms: ART, SIRT, Landweber iteration (LWB), the generalized Landweber iteration (GLWB), the steepest descent method (STP), as well as iterative filtered backprojection (IFBP) reconstruction algorithms: Chang's method, Walters' method, and a modified iterative MAP. The framework provides an effective tool to systematically study conventional iterative algebraic algorithms and IFBP algorithms. Based on this framework, conventional iterative algebraic algorithms and IFBP algorithms are generalized. It is shown from the algebraic point of view that IFBP algorithms are not only excellent methods for correction of attenuation (either uniform or nonuniform) but are also good general iterative reconstruction algorithms (they can be applied to either attenuated or attenuation-free projections and converge very fast). The convergence behavior of iterative algebraic algorithms is discussed and insight is drawn into the fast convergence property of IFBP algorithms. A simulated PET system is used to evaluate IFBP algorithms and LWB in comparison with the maximum likelihood estimation via expectation maximization algorithm (MLE-EM) and the filtered backprojection (FBP) algorithm. The simulation results indicate that for both attenuation-free projection and attenuated projection cases IFBP algorithms have a significant computational advantage over LWB and MLE-EM, and have performance advantages over FBP in terms of contrast recovery and/or noise-to-signal ratios (NSRs) in regions of interest.

The transportation accuracy of sealed radioisotope sources influences the therapeutic effect of high-dose-rate (HDR) brachytherapy. We have developed a pinhole imaging system for tracking an Ir-192 radiation source during HDR brachytherapy treatment. Our system consists of a dual-pinhole collimator, a scintillator, and a charge-coupled device (CCD) camera. We acquired stereo-shifted images to infer the source position in three dimensions using a dual pinhole collimator with 1.0 mm diameter pinholes. The CCD camera captured consecutive images of scintillation light that corresponds to the source positions every 2 s. The system automatically tracks scintillation light points using template-matching technique and measured the source positions therefrom. By integrating a series of CCD images, we could infer the source dwell time from the pixel values in the integrated image.We investigated the tracking accuracy of our system in monitoring simulated brachytherapy as it would be performed for cervical cancer by using water as a stand-in for human tissue. Ir-192 pellet was moved through a water tank using tandem and ovoid applicators. The CCD camera captured clear images of the scintillation light produced by the underwater Ir-192 source in conditions equivalent to common clinical situations. The differences between the measured and the reference 3D source positions and dwell times were 1.5 ± 0.7 mm and 0.8 ± 0.4 s, respectively.This system has the potential to track in vivo Ir-192 source in real time and may prove a useful tool for quality assurance during HDR brachytherapy treatments in clinical settings.

A new reconstruction algorithm is introduced to obtain a high quality computerized tomography image from limited information. There are many practical cases in which only limited projection data can be collected due to the physical constraints of the hardware system and the object structure. In these cases, conventional reconstruction algorithms which require complete projections are often unacceptable because it causes severe streak artifacts and distortions. So image reconstruction from limited information is very important and many authors have contributed to it. But the methods presented are often time consuming, less efficient and some algorithm's convergency cannot be guaranteed. For iterative algorithms, it is more important to guarantee the convergency and accelerate the convergency speed. In this paper, a CT image reconstruction method which exploits the Helgason-Ludwig consistency condition is designed to consider obtainable information content about object and reconstruction error in the situation of a finite number of projections. Based on the consideration, we developed a new iterative algorithm which can reconstruct high quality artifactless CT image from limited information, and we design a new histogram constraint obtained from a priori knowledge to guarantee the convergency and to acquire high convergency speed. The results of simulated tests show the great advantages of this new method.