Multimodal CTC detection using stem cell antigen-specific immunosilica particles and immunofluorescent quantum dots

Amid the COVID-19 pandemic, cancer continues to be the most devastating disease worldwide. Liquid biopsy of circulating tumor cells (CTCs) has recently become a painless and noninvasive tool for obtaining carcinoma cell samples for molecular profiling. Here, we report efficient detection and collection of cancer cells in blood samples by combining stem cell antigen (CD44)-specific immunosilica particles and immunofluorescent quantum dots with spectrally and temporally resolved single-photon counting. We accurately detect 1–10 cells among 100 cancer cells of the breast, lungs, or cervix in 1 mL blood samples. In addition, the bright and narrowband emission of CdSe/ZnS quantum dots enables temporally and spectrally resolved photon counting for multiplexed cancer cell detection. The cancer cell-specific and large immunosilica particles helped us collect the specific cells. We validate the detection efficiency and multimodality of this strategy by time-stamped and energy-dispersed single-photon counting of orange- and red-emitting quantum dots and green-fluorescing nuclei stained with Syto-13/25 dye. Thus, the present work highlights the prospects of multimodal CTC detection for noninvasive cancer screening and postsurgical or therapeutic follow-up.

Analog and Photon Signal Splicing for CO2-DIAL Based on Piecewise Nonlinear Algorithm

In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal.

Comparison of the Agatston score acquired with photon-counting detector CT and energy-integrating detector CT: ex vivo study of cadaveric hearts

The purpose of this study was to compare the correlation and agreement between AS derived from either an energy-integrating detector CT (EID-CT) or a photon-counting detector CT (PCD-CT). Reproducibility was also compared. In total, 26 calcified coronary lesions (from five cadaveric hearts) were identified for inclusion. The hearts were positioned in a chest phantom and scanned in both an EID-CT and a prototype PCD-CT. The EID-CT and PCD-CT acquisition and reconstruction parameters were matched. To evaluate the reproducibility, the phantom was manually repositioned, and an additional scan was performed using both methods. The EID-CT reconstructions were performed using the dedicated calcium score kernel Sa36. The PCD-CT reconstructions were performed with a vendor-recommended kernel (Qr36). Several monoenergetic energy levels (50–150 keV) were evaluated to find the closest match with the EID-CT scans. A semi-automatic evaluation of calcium score was performed on a post-processing multimodality workplace. The best match with Sa36 was PCD-CT Qr36 images, at a monoenergetic level of 72 keV. Statistical analyses showed excellent correlation and agreement. The correlation and agreement with regards to the Agatston score (AS) between the two methods, for each position as well as between the two positions for each method, were assessed with the Spearman´s rank correlation. The correlation coefficient, rho, was 0.98 and 0.97 respectively 0.99 and 0.98. The corresponding agreements were investigated by means of Bland–Altman plots. High correlation and agreement was observed between the AS derived from the EID-CT and a PCD-CT. Both methods also demonstrated excellent reproducibility.

Feature extraction and neural network-based multi-peak analysis on time-correlated LiDAR histograms

Time correlated single photon counting (TCSPC) is a statistical method to generate time-correlated histograms (TC-Hists), which are based on the time-of-flight (TOF) information measured by photon detectors such as single-photon avalanche diodes. With restricted measurements per histogram and the presence of high background light, it is challenging to obtain the target distance in a TC-Hist. In order to improve the data processing robustness under these conditions, the concept of machine learning is applied to the TC-Hist. Using the neural network-based multi-peak analysis (NNMPA), introduced by us, including a physics-guided feature extraction, a neural network multi-classifier, and a distance recovery process, the analysis is focused on a small amount of critical features in the TC-Hist. Based on these features, possible target distances with correlated certainty values are inferred. Furthermore, two optimization approaches regarding the learning ability and real-time performance are discussed. In particular, variants of the NNMPA are evaluated on both synthetic and real datasets. The proposed method not only has higher robustness in allocating the coarse position (±5 %) of the target distance in harsh conditions, but also is faster than the classical digital processing with an average-filter. Thus, it can be applied to improve the system robustness, especially in the case of high background light and middle-range detections.

Structure and photophysics of rubrene-tetracene blends

The application potential of singlet fission (SF), describing the spontaneous conversion of an excited singlet into two triplets, underlines the necessity to independently control SF rates, energetics and the optical band gap. Heterofission, whereby the singlet splits into triplets on chemically distinct chromophores, is a promising approach to control the above-mentioned parameters, but its details are not yet fully understood. Here, we investigate the photophysics of blends of two prototypical SF chromophores, tetracene (TET) and rubrene (RUB) using time-resolved photoluminescence spectroscopy and time-correlated single photon counting (TCSPC) to explore the potential for heterofission in combinations of endothermic SF chromophores.

Multi-contrast CT imaging using a high energy resolution CdTe detector and a CZT photon-counting detector

Computed tomography (CT) imaging with high energy resolution detectors shows great promise in material decomposition and multi-contrast imaging. Multi-contrast imaging was studied by imaging a phantom with iodine (I), gadolinium (Gd), and gold (Au) solutions, and mixtures of the three using a cadmium telluride (CdTe) spectrometer with an energy resolution of 1% as well as with a cadmium zinc telluride (CZT) detector with an energy resolution of 13%. The phantom was imaged at 120 kVp and 1.1 mA with 7 mm of aluminum filtration. For the CdTe data collection, the phantom was imaged using a 0.2 mm diameter x-ray beam with 96 ten-second data acquisitions across the phantom at 45 rotation angles. For the CZT detector, we had 720 projections using a cone beam, and the six detector energy thresholds were set to 23, 33, 50, 64, 81, and 120 keV so that three thresholds corresponded to the K-edges of the contrast agents. Contrast agent isolation methods were then examined. K-edge subtraction and novel spectrometric algebraic image reconstruction (SAIR) were used for the CdTe data. K-edge subtraction alone was used for the CZT data. Linearity plots produced similar R 2 values and slopes for all three reconstruction methods. Comparing CdTe methods, SAIR offered less noise than CdTe K-edge subtraction and better geometric accuracy at low contrast concentrations. CdTe contrast agent images of I, Gd, and Au offered less noise and greater contrast than the CZT images, highlighting the benefits of high energy resolution CdTe detectors for possible use in pre-clinical or clinical CT imaging.

Pixel readout IC for CdTe detectors operating in single photon counting mode with interpixel communication

This paper presents a readout integrated circuit (IC) of pixel architecture called MPIX (Multithreshold PIXels), designed for CdTe pixel detectors used in X-ray imaging applications. The MPIX IC area is 9.6 mm × 20.3 mm and it is designed in a CMOS 130 nm process. The IC core is a matrix of 96 × 192 square-shaped pixels of 100 µm pitch. Each pixel contains a fast analog front-end followed by four independently working discriminators and four 12-bit ripple counters. Such pixel architecture allows photon processing one by one and selecting the X-ray photons according to their energy (X-ray colour imaging). To fit the different range of applications the MPIX IC has 8 possible different gain settings, and it can process the X-ray photons of energy up to 154 keV. The MPIX chip is bump-bonded to the CdTe 1.5 mm thick pixel sensor with a pixel pitch of 100 µm. To deal with the charge sharing effect coming from a thick semiconductor pixel sensor, multithreshold pattern recognition algorithm is implemented in the readout IC. The implemented algorithm operates both in the analog domain (to recover the total charge spread between neighboring pixels, when a single X-ray photon hits the border of the pixel) and in the digital domain (to allocate a hit position to a single pixel).