Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus.

BACKGROUND: Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients. METHODS: R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC. RESULTS: The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset. CONCLUSIONS: In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.

High-speed ADC based on photonic time-stretched technology with dispersion-tunable CFBG.

We propose a photonic time-stretched analog-to-digital converter (PTS-ADC) based on dispersion-tunable chirped fiber Bragg grating (CFBG), by demonstrating a economical ADC system with seven different stretch factors. The stretch factors are tunable by changing the dispersion of CFBG, in order to obtain different sampling points. Accordingly, the total sampling rate of the system can be improved. Only a single channel is required to increase the sampling rate and achieve the effect of multi-channel sampling. Finally, seven groups of different stretch factors ranging from 1.882 to 2.206 are obtained, which are equivalent to seven groups of different sampling points. We successfully recover the input radio frequency (RF) signals with frequencies from 2 GHz to 10 GHz. In addition, the sampling points are increased by 14.4 times and the equivalent sampling rate is increased to 288 GSa/s. The proposed scheme is suitable for commercial microwave radar systems, which can obtain a much higher sampling rate at a low cost.

Compact optical fiber sensor based on Vernier effect with speckle patterns.

We propose a Vernier effect-based sensor for temperature and salinity measurements. This sensor utilizes the correlation speckle pattern generated by spatial multimode interference and has undergone testing to validate its effectiveness. The speckle demodulation method is used to solve the problem of inconsistent envelope measurement when tracking with different upper and lower envelopes. The device consists of two Fabry Perot interferometers (FPIs) created by connecting hole core fiber (HCF) and erbium-doped fiber (EDF) in series. The speckle image produced by the interferometers is analyzed using the Zero means normalized cross-correlation (ZNCC) technique. The ZNCC value demonstrates a linear relationship with salinity and temperature, allowing for the measurement of these parameters. The sensor exhibits a temperature detection sensitivity of -0.0224 /°C and a salinity detection sensitivity of -0.0439/%. The sensor offers several advantageous features, including its compact size, low-cost manufacturing, high sensitivity, stability, and convenient reflection measurements. These characteristics make it a valuable tool for various applications. The proposed Vernier effect-based temperature and salinity sensor shows great potential for simultaneous monitoring and measurement of temperature and salinity in environments such as marine settings or industrial processes where accurate control of these parameters is crucial.

Accelerating the phase demodulation process for heterodyne Φ-OTDR using spatial phase shifting.

An effective orthogonal signal generation method for heterodyne-detection-based phase-sensitive optical time-domain reflectometer systems is proposed to accelerate the phase demodulation process. The demodulation principle is based on the spatial phase shifting technique. By exploiting the relative phase difference between adjacent spatial sampling channels, the orthogonal signal is easily obtained from basic algebra calculations. The simulation and experimental results showed that the proposed method achieved >100% computation speed improvement compared with the conventional methods, with a slight trade-off in phase demodulation performance. Therefore, the proposed method is potentially beneficial for the distributed acoustic sensing technology for reducing the computation complexity of phase demodulation procedures.

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS: A total of 603 patients who underwent NAC were retrospectively included between January 2018 and June 2021 from three different institutions. Four different deep convolutional neural networks (DCNNs) were trained by pretreatment ultrasound images using annotated training dataset (n = 420) and validated in a testing cohort (n = 183). Comparing the predictive performance of these models, the best one was selected for image-only model structure. Furthermore, the integrated DLR model was constructed based on the image-only model combined with independent clinical-pathologic variables. Areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. RESULTS: As the optimal basic model, Resnet50 achieved an AUC and accuracy of 0.879 and 82.5% in the validation set. The integrated DLR model, yielding the highest classification performance in predicting response to NAC (AUC 0.962 and 0.939 in the training and validation cohort), outperformed the image-only model and the clinical model and also performed better than two radiologists' prediction (all p < 0.05). In addition, predictive efficacy of the radiologists was improved under the assistance of the DLR model significantly. CONCLUSION: The pretreatment US-based DLR model could hold promise as a clinical guidance for predicting NAC response of patients with breast cancer, thereby providing benefit of timely treatment strategy adjustment to potential poor NAC responders. KEY POINTS: • Multicenter retrospective study showed that deep learning radiomics (DLR) model based on pretreatment ultrasound image and clinical parameter achieved satisfactory prediction of tumor response to neoadjuvant chemotherapy (NAC) in breast cancer. • The integrated DLR model could become an effective tool to guide clinicians in identifying potential poor pathological responders before chemotherapy. • The predictive efficacy of the radiologists was improved under the assistance of the DLR model.

Integrated Fiber Ring Laser Temperature Sensor Based on Vernier Effect with Lyot-Sagnac Interferometer.

The Vernier effect created using an incorporated Lyot-Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor's performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.

Optical curvature sensor with high resolution based on in-line fiber Mach-Zehnder interferometer and microwave photonic filter.

Curvature measurement plays an important role in structural health monitoring, robot-pose measuring, etc. High-resolution curvature measurement is highly demanded. In this paper, an optical curvature sensor with high resolution based on in-fiber Mach-Zehnder interferometer (MZI) and microwave photonic filter (MPF) is proposed and experimentally demonstrated. The in-fiber MZI is constructed with a ring-core fiber (RCF) which is fusion spliced between two coreless fibers (CLFs). The structure of CLF-RCF-CLF is then sandwiched between two segments of single-mode fiber (SMF), making the whole interferometer structure of SMF-CLF-RCF-CLF-SMF. The operating principle is that different curvatures will cause the variations of the interference spectrum of MZI due to elastic-optic effect, and then the variations are converted into the frequency-shift of the MPF. The factors affecting the visibility of the interference spectrum are researched. A preliminary exploration of the multiplexing demodulation for the in-fiber-MZIs is also investigated and discussed, which is for the first time to the best of our knowledge, holding great potential to pave the way for constructing the sensing network composed of interferometric sensors. The curvature measurement sensitivity is -147.634 MHz/m-1, and the resolution is 6.774 × 10-6 m-1 which is the highest value up to now.

Quantitative demodulation of distributed low-frequency vibration based on phase-shifted dual-pulse phase-sensitive OTDR with direct detection.

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been proposed for distributed vibration sensing purpose over recent years. Emerging applications, including seismic and hydroacoustic wave detection, demand accurate low-frequency vibration reconstruction capability. We propose to use the direct-detection Φ-OTDR configuration to achieve quantitative demodulation of external low-frequency vibrations by phase-shifted dual-pulse probes. Simultaneous pulsing and phase shifting modulation is realized with a single acousto-optic modulator to generate such probes, relaxing the need for an additional optical phase modulator. In the experiments, vibrations with frequency as low as 0.5 Hz are successfully reconstructed with 10 m spatial resolution and 35 dB signal-to-noise ratio. Excellent linearity and repeatability are demonstrated between the optical phase demodulation results and the applied vibration amplitudes. The proposed method is capable of quantitative demodulation of low-frequency vibrations with a cost-effective system configuration and high computation efficiency, showing potential for commercial applications of distributed seismic or hydroacoustic wave acquisition.

Ultra-low sampling resolution technique for heterodyne phase-OTDR based distributed acoustic sensing.

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) based on heterodyne detection is widely used for its simple structure and high signal-to-noise ratio (SNR). However, the large amount of raw data of Φ-OTDR places a heavy burden on the storage device and also limits the transferability of the data. In this Letter, we propose an ultra-low sampling resolution technique to solve the data storage problem in heterodyne Φ-OTDR. Experimental results show that the optical phase variations induced by external vibrations can be successfully demodulated from the 1-bit-resolution raw data, and a vibration SNR of 58.03 dB is achieved. In addition, this work also reveals that a data acquisition device with extremely low sampling resolution is sufficient for heterodyne Φ-OTDR, signifying that the cost of the system can be further decreased.

Data Reduction in Phase-Sensitive OTDR with Ultra-Low Sampling Resolution and Undersampling Techniques.

Data storage is a problem that cannot be ignored in the long-term monitoring of a phase-sensitive optical time-domain reflectometry (Φ-OTDR) system. In this paper, we proposed a data-reduction approach for heterodyne Φ-OTDR using an ultra-low sampling resolution and undersampling techniques. The operation principles were demonstrated and experiments with different sensing configurations were carried out to verify the proposed method. The results showed that the vibration signal could be accurately reconstructed from the undersampled 1-bit data. A space saving ratio of 98.75% was achieved by converting 128 MB of data (corresponding to 268.44 ms of sensing time) to 1.6 MB. The proposed method led to a potentially new data-reduction approach for heterodyne Φ-OTDR, which also provided economical guidance for the selection of the data-acquisition device.

Salinity and Temperature Dual-Parameter Sensor Based on Fiber Ring Laser with Tapered Side-Hole Fiber Embedded in Sagnac Interferometer.

This paper presented a new kind of salinity and temperature dual-parameter sensor based on a fiber ring laser (FRL) with tapered side-hole fiber (SHF) embedded in a Sagnac interferometer. The sensing structure is majorly composed of tapered SHF located in the middle of SHF inside the Sagnac interferometer loop structure. The influences of the SHF's diameters of different tapered in the Sagnac interferometer loop on the FRL sensing system are studied. The presence of air holes in the SHF makes the cladding mode easier to excite, and the interaction between the cladding mode with its surroundings is enhanced, thus having higher salinity sensitivity. Besides, the unique advantages of high resolution, narrower linewidth, and high signal-to-noise ratio (SNR) of fiber laser make the measurement results more accurate. In this experiment, the SHF with different taper diameters was made, and it was found that reducing the diameter of the taper waist diameter could further improve the salinity sensitivity. When the waist diameter was 9.70 µm, the maximum salinity sensitivity of 0.2867 nm/‱ was achieved. Temperature sensing experiments were also carried out. The maximum temperature sensitivity of the FRL sensing system was -0.3041 nm/°C at the temperature range from 20 to 30 °C. The sensor has the characteristics of easy manufacture, good selectivity, and high sensitivity, proving the feasibility of simultaneous measurement of seawater salinity and temperature.

Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm.

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial-temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.

Optical sensor network interrogation system based on nonuniform microwave photonic filters.

Based on the nonuniformly spaced microwave photonic delay-line filter technology, a new design of a generic optical fiber sensor network interrogation platform is proposed and demonstrated. Sensing information from different types of optical sensors embedded in filter taps is converted into the variations of delay time and amplitude of each filter tap individually. Information to be measured can be decoded from the complex temporal impulse response of the microwave photonic filter. As proof-of-concept, our proposed approach is verified by simulations and experimental demonstrations successfully. Four optical sensors of different types are simultaneously interrogated via inverse Fourier transform of the filter frequency response. The experiment results show good linearity between the variation of temporal impulse response and the variations of the twist, the lateral pressure, the transversal loading and the temperature. The sensitivity of the sensors in the proposed platform is -2.130×10-5 a.u/degree, 6.1039 ps/kPa, -1.9146×10-5 a.u/gram, and 5.1497 ps/°C, respectively. Compared to the conventional optical sensors interrogation system, the presented approach provides a centralized solution that works for different types of optical sensors and can be easily expanded to cover larger optical sensor networks.

Automatic lens-splitting optimization algorithm for spherical aberration correction.

This paper proposes the automatic lens-splitting optimization algorithm to correct spherical aberration by the interaction between ZEMAX and MATLAB. This method tackled the problem that classic optimization cannot add the elements automatically. A high numerical aperture collimator is considered for the test sample, and the selection of four thickness constraints (minimum center and edge of glass and air thickness constraints) is discussed. To determine the feasibility and applicability of this method, more design systems (a retro-focus objective system and a telesystem) are tested. The simulated results under the feasible thickness constraints show that the automatic lens-splitting optimization algorithm generates reasonable and realizable solutions.

A Temperature Independent Inclinometer Based on a Tapered Fiber Bragg Grating in a Fiber Ring Laser.

We demonstrate a new concept for an all-fiber inclinometer based on a tapered fiber Bragg grating (tFBG) in a fiber ring laser (FRL) with the capability of measuring the tilt angle and temperature simultaneously. The sensor performance is analyzed theoretically and investigated experimentally. The dependence of tilt angle on the spectral response in variable temperature conditions was measured. Two inclinometers with different lengths have been fabricated and characterized in FRL. The sensitivity is 0.583 dB/° and 0.849 dB/°, respectively, in the range of 0° to 90°. Thanks to the FRL system, narrow 3-dB bandwidth (<0.1 nm) and high optical signal-to-noise ratio (~60 dB) are achieved. The tFBG in the FRL system can be used for working as a temperature insensitive inclinometer. The results suggested that the proposed inclinometer has the advantages of compact size and convenient manufacture, enhancing its potential for application prospect.

Hyperthermia enhances radiosensitivity of colorectal cancer cells through ROS inducing autophagic cell death.

Hyperthermia (HT) enhances the anti-cancer effects of radiotherapy (RT), but the precise biochemical mechanisms involved are unclear. This study was aim to investigate if mild HT sensitizes colorectal cancer cells to RT through reactive oxygen species (ROS)-inducing autophagic cell death in a mice model of HCT116 human colorectal cancer. HCT116 mice model were randomly divided into five groups: mock group, hyperthermia group (HT), radiotherapy group (RT), HT + RT group, and HT + RT +N-acetyl L-cysteine (NAC) group (HT + CT + NAC). After four weeks of treatment, cancer growth inhibition, rate and mitochondrial membrane potential were measured with MTT and JC-1 assays, respectively, while ROS were estimated fluorimetrically. The relationship of these parameters to expressions of autophagy-related genes Beclin1, LC3B, and mTOR was analyzed. Gene expression was measured by Real-Time polymerase chain reaction (RT-PCR). There were significant increases in ROS levels and mitochondrial membrane potential in the HT + RT group. ROS levels in the HT + RT group increased more significantly than in any other group. In contrast, ROS levels in the HT + RT + NAC group were significantly decreased relative to the HT + RT group. The number of autophagic bodies in HT + RT group was higher than that of mock group. There were significant increases in the expression of Beclin1 and LC3B genes, while mTOR expression was significantly decreased in the HT + CT group. Treatment with NAC reversed the pattern of these changes. These results indicate that HT enhances the radiosensitivity of colorectal cancer cells to RT through ROS inducing autophagic cell death.

Aberration correction method for the catadioptric imaging system design.

A practical method is proposed for aberration correction in the catadioptric imaging system design. Based on the analytical relationship between the object distance and the image distance of the aspherical mirror imaging system in the meridional plane, the catadioptric imaging system aberration correction becomes a refractive lens optimization problem that is much simpler, thus there is no need of a complex surface figure to insure the optimization convergence. With the proposed method, it is convenient for the designer to keep the aberrations at a low level throughout the process and to focus on distortion correction or search for robust initial configuration. The design process of a catadioptric ultrashort distance projector is presented to illustrate the proposed method more clearly. The real performance of the ultrashort distance projector is also presented.

General construction method for a rotationally symmetric ultrashort distance catadioptric projector.

In this work, the off-axis imaging of a fan of rays with a rotationally symmetric mirror is studied, based on which a general construction method is proposed for the design of a rotationally symmetric ultrashort distance catadioptric projector. With the proposed method, the intermediate images at arbitrary planes of incidence can be obtained, and thus the designer has the freedom to choose suitable intermediate images to simplify the aberration correction of the catadioptric starting point. To prove the effectiveness of the proposed method, the construction process of a convex-type ultrashort distance catadioptric projector is presented. The intermediate image at the meridional plane is discontinuous, and the image distances for some fields are infinite. In contrast, the intermediate image at the sagittal plane is continuous, smooth, and finite-and thus is selected to correct the skew aberrations in the catadioptric starting point. An initial configuration, which has small aberrations, is obtained without increasing the system complexity. Further automatic optimization is applied to the initial configuration to get the optimal solution. The simulated performance of the optimal solution is excellent.

Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm.

In an effort to improve compressive sensing and spare signal reconstruction by way of the backtracking-based adaptive orthogonal matching pursuit (BAOMP), a new sparse coding algorithm called improved adaptive backtracking-based OMP (ABOMP) is proposed in this study. Many aspects have been improved compared to the original BAOMP method, including replacing the fixed threshold with an adaptive one, adding residual feedback and support set verification, and others. Because of these ameliorations, the proposed algorithm can more precisely choose the atoms. By adding the adaptive step-size mechanism, it requires much less iteration and thus executes more efficiently. Additionally, a simple but effective contrast enhancement method is also adopted to further improve the denoising results and visual effect. By combining the IABOMP algorithm with the state-of-art dictionary learning algorithm K-SVD, the proposed algorithm achieves better denoising effects for astronomical images. Numerous experimental results show that the proposed algorithm performs successfully and effectively on Gaussian and Poisson noise removal.