Fast4DReg - fast registration of 4D microscopy datasets.

Unwanted sample drift is a common issue that plagues microscopy experiments, preventing accurate temporal visualization and quantification of biological processes. Although multiple methods and tools exist to correct images post acquisition, performing drift correction of three-dimensional (3D) videos using open-source solutions remains challenging and time consuming. Here, we present a new tool developed for ImageJ or Fiji called Fast4DReg that can quickly correct axial and lateral drift in 3D video-microscopy datasets. Fast4DReg works by creating intensity projections along multiple axes and estimating the drift between frames using two-dimensional cross-correlations. Using synthetic and acquired datasets, we demonstrate that Fast4DReg can perform better than other state-of-the-art open-source drift-correction tools and significantly outperforms them in speed. We also demonstrate that Fast4DReg can be used to register misaligned channels in 3D using either calibration slides or misaligned images directly. Altogether, Fast4DReg provides a quick and easy-to-use method to correct 3D imaging data before further visualization and analysis.

Retrospective frequency drift correction of rosette MRSI data using spectral registration.

PURPOSE: Frequency drift correction is an important postprocessing step in MRS that yields improvements in spectral quality and metabolite quantification. Although routinely applied in single-voxel MRS, drift correction is much more challenging in MRSI due to the presence of phase-encoding gradients. Thus, separately acquired navigator scans are normally required for drift estimation. In this work, we demonstrate the use of self-navigating rosette MRSI trajectories combined with time-domain spectral registration to enable retrospective frequency drift corrections without the need for separately acquired navigator echoes. METHODS: A rosette MRSI sequence was implemented to acquire data from the brains of 5 healthy volunteers. FIDs from the center of k-space ( k = 0 $$ k=0 $$ FIDs) were isolated from each shot of the rosette acquisition, and time-domain spectral registration was used to estimate the frequency offset of each k = 0 $$ k=0 $$ FID relative to a reference scan (the first k = 0 $$ k=0 $$ FID in the series). The estimated frequency offsets were then used to apply corrections throughout k $$ k $$ -space. Improvements in spectral quality were assessed before and after drift correction. RESULTS: Spectral registration resulted in significant improvements in signal-to-noise ratio (12.9%) and spectral linewidths (18.5%). Metabolite quantification was performed using LCModel, and the average Cramer-Rao lower bounds uncertainty estimates were reduced by 5.0% for all metabolites, following field drift correction. CONCLUSION: This study demonstrated the use of self-navigating rosette MRSI trajectories to retrospectively correct frequency drift errors in in vivo MRSI data. This correction yields meaningful improvements in spectral quality.

Arduino-Based Low-Cost Device for the Measurement of Detonation Times in Blasting Caps.

The use of equipment such as oscilloscopes, high-speed cameras or acoustic sensors is quite common to measure detonation times from surface connectors and detonators. However, these solutions are expensive and, sometimes, not adequate to use in field conditions, such as mining or civil works. In this regard, a low-cost portable device is designed and tested using the Arduino platform, achieving a simple, robust and precise system to carry out field measurements. This study describes the characteristics and working principles of the designed device, as well as the verifications carried out to check the accuracy of the Arduino ceramic oscillator. Additionally, a field test was carried out using 100 actual detonators and surface connectors to verify the correct operation of the designed equipment. We have designed a device, and a methodology, to measure detonation instants with a minimum accuracy of 0.1 ms, being sufficient to carry out subsequent studies of detonation time dispersion for non-electric detonators.

An Improved Method for Accurate Radiation Measurement Based on Dark Output Noise Drift Compensation.

This paper verified through experiments that change in ambient temperature are the main cause of dark output noise drift. Additionally, the impact of dark output noise drift in fiber optic spectrometers on emissivity measurements has been investigated in this work. Based on an improved fiber optic spectrometer, two methods were proposed for characterizing and correcting the dark output noise offset in fiber optic spectrometers: the mean correction scheme and the linear fitting correction scheme. Compared to the mean correction scheme, the linear fitting correction scheme is more effective in solving the problem of dark output noise drift. When the wavelength is greater than 1600 nm, the calibration relative error of silicon carbide (SIC) emissivity is less than 0.8% by the mean correction scheme, while the calibration relative error of silicon carbide emissivity is less than 0.62% by the linear fitting correction scheme. This work solves the problem of dark output noise drift in prolonged measurement based on fiber optic spectrometers, improving the accuracy and reliability of emissivity and quantitative radiation measurement.

Low-Pass Filters for a Temperature Drift Correction Method for Electromagnetic Induction Systems.

Electromagnetic induction (EMI) systems are used for mapping the soil's electrical conductivity in near-surface applications. EMI measurements are commonly affected by time-varying external environmental factors, with temperature fluctuations being a big contributing factor. This makes it challenging to obtain stable and reliable data from EMI measurements. To mitigate these temperature drift effects, it is customary to perform a temperature drift calibration of the instrument in a temperature-controlled environment. This involves recording the apparent electrical conductivity (ECa) values at specific temperatures to obtain a look-up table that can subsequently be used for static ECa drift correction. However, static drift correction does not account for the delayed thermal variations of the system components, which affects the accuracy of drift correction. Here, a drift correction approach is presented that accounts for delayed thermal variations of EMI system components using two low-pass filters (LPF). Scenarios with uniform and non-uniform temperature distributions in the measurement device are both considered. The approach is developed using a total of 15 measurements with a custom-made EMI device in a wide range of temperature conditions ranging from 10 °C to 50 °C. The EMI device is equipped with eight temperature sensors spread across the device that simultaneously measure the internal ambient temperature during measurements. To parameterize the proposed correction approach, a global optimization algorithm called Shuffled Complex Evolution (SCE-UA) was used for efficient estimation of the calibration parameters. Using the presented drift model to perform corrections for each individual measurement resulted in a root mean square error (RMSE) of <1 mSm-1 for all 15 measurements. This shows that the drift model can properly describe the drift of the measurement device. Performing a drift correction simultaneously for all datasets resulted in a RMSE <1.2 mSm-1, which is considerably lower than the RMSE values of up to 4.5 mSm-1 obtained when using only a single LPF to perform drift corrections. This shows that the presented drift correction method based on two LPFs is more appropriate and effective for mitigating temperature drift effects.

Correction Model for Metal Oxide Sensor Drift Caused by Ambient Temperature and Humidity.

For decades, Metal oxide (MOX) gas sensors have been commercially available and used in various applications such as the Smart City, gas monitoring, and safety due to advantages such as high sensitivity, a high detection range, fast reaction time, and cost-effectiveness. However, several factors affect the sensing ability of MOX gas sensors. This article presents the results of a study on the cross-sensitivity of MOX gas sensors toward ambient temperature and humidity. A gas sensor array consisting of temperature and humidity sensors and four different MOX gas sensors (MiCS-5524, GM-402B, GM-502B, and MiCS-6814) was developed. The sensors were subjected to various relative gas concentrations, temperatures (from 16 °C to 30 °C), and humidity levels (from 75% to 45%), representing a typical indoor environment. The results proved that the gas sensor responses were significantly affected by the temperature and humidity. The increased temperature and humidity levels led to a decreased response for all sensors, except for MiCS-6814, which showed the opposite response. Hence, this work proposed regression models for each sensor, which can correct the gas sensor response drift caused by the ambient temperature and humidity variations. The models were validated, and the standard deviations of the corrected sensor response were found to be 1.66 kΩ, 13.17 kΩ, 29.67 kΩ, and 0.12 kΩ, respectively. These values are much smaller compared to the raw sensor response (i.e., 18.22, 24.33 kΩ, 95.18 kΩ, and 2.99 kΩ), indicating that the model provided a more stable output and minimised the drift. Overall, the results also proved that the models can be used for MOX gas sensors employed in the training process, as well as for other sets of gas sensors.

Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems.

Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to mitigate drift effects in EMI instruments rely on a temperature drift calibration, where the instrument is heated up to specific temperatures in a controlled environment and the observed drift is determined to derive a static thermal apparent electrical conductivity (ECa) drift correction. In this study, a novel correction method is presented that models the dynamic characteristics of drift using a low-pass filter (LPF) and uses it for correction. The method is developed and tested using a customized EMI device with an intercoil spacing of 1.2 m, optimized for low drift and equipped with ten temperature sensors that simultaneously measure the internal ambient temperature across the device. The device is used to perform outdoor calibration measurements over a period of 16 days for a wide range of temperatures. The measured temperature-dependent ECa drift of the system without corrections is approximately 2.27 mSm-1K-1, with a standard deviation (std) of only 30 µSm-1K-1 for a temperature variation of around 30 K. The use of the novel correction method reduces the overall root mean square error (RMSE) for all datasets from 15.7 mSm-1 to a value of only 0.48 mSm-1. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm-1. The results show that modeling the dynamic thermal characteristics of the drift helps to improve the accuracy by a factor of four compared to a purely static characterization. It is concluded that the modeling of the dynamic thermal characteristics of EMI systems is relevant for improved drift correction.

Atomic-Resolution Imaging of Fast Nanoscale Dynamics with Bright Microsecond Electron Pulses.

Atomic-resolution electron microscopy is a crucial tool to elucidate the structure of matter. Recently, fast electron cameras have added the time domain to high-resolution imaging, allowing static images to be acquired as movies from which sample drift can later be removed computationally and enabling real-time observations of atomic-scale dynamics on the millisecond time scale. Even higher time resolution can be achieved with short electron pulses, yet their potential for atomic-resolution imaging remains unexplored. Here, we generate high-brightness microsecond electron pulses from a Schottky emitter whose current we briefly drive to near its limit. We demonstrate that drift-corrected imaging with such pulses can achieve atomic resolution in the presence of much larger amounts of drift than with a continuous electron beam. Moreover, such pulses enable atomic-resolution observations on the microsecond time scale, which we employ to elucidate the crystallization pathways of individual metal nanoparticles as well as the high-temperature transformation of perovskite nanocrystals.

Synchronization of acquisition devices in neuroimaging: An application using co-registration of eye movements and electroencephalography.

Interest in applications for the simultaneous acquisition of data from different devices is growing. In neuroscience for example, co-registration complements and overcomes some of the shortcomings of individual methods. However, precise synchronization of the different data streams involved is required before joint data analysis. Our article presents and evaluates a synchronization method which maximizes the alignment of information across time. Synchronization through common triggers is widely used in all existing methods, because it is very simple and effective. However, this solution has been found to fail in certain practical situations, namely for the spurious detection of triggers and/or when the timestamps of triggers sampled by each acquisition device are not jointly distributed linearly for the entire duration of an experiment. We propose two additional mechanisms, the "Longest Common Subsequence" algorithm and a piecewise linear regression, in order to overcome the limitations of the classical method of synchronizing common triggers. The proposed synchronization method was evaluated using both real and artificial data. Co-registrations of electroencephalographic signals (EEG) and eye movements were used for real data. We compared the effectiveness of our method to another open source method implemented using EYE-EEG toolbox. Overall, we show that our method, implemented in C++ as a DOS application, is very fast, robust and fully automatic.

Comparison of preconcentration methods for nontargeted analysis of natural waters using HPLC-HRMS: Large volume injection versus solid-phase extraction.

Nontargeted analysis of water samples using liquid chromatography combined with high-resolution mass spectrometers is an emerging approach for surface water monitoring and evaluation of water treatment processes. In this study, sample preconcentration via direct, large volume injection with 500 µL and 1000 µL injection volumes was compared to SPE regarding analytical performance parameters in targeted and nontargeted workflows. In targeted analysis, the methods were evaluated in terms of LOD and intrabatch precision of the selected compounds, whereas in nontargeted analysis, the number of detected unknown compounds, the method's intra-batch precision, and the retention time versus molecular mass pattern of the detected unknowns were evaluated. In addition, a novel intensity drift correction method was developed that is not based on quality control samples and makes use of the signals obtained for continuously infused reference compounds, which are conventionally utilized for online mass drift correction. It could be demonstrated that the new correction method significantly reduced the bias introduced by instrumental drift and is important for the reliable intercomparison of different nontargeted methods. Intercomparison of results showed that the 1000 µL large volume injection method revealed the best performance in terms of precision under repeatability conditions of measurement as well as lower LODs for targeted compound analysis. In nontargeted analysis, the SPE method detected a higher number of unknown compounds but exhibited also a higher uncertainty of measurement caused by matrix effects.

Optimization of electronic nose drift correction applied to tomato volatile profiling.

E-noses can be routinely used to evaluate the volatile profile of tomato samples once the sensor drift and standardization issues are adequately solved. Short-term drift can be corrected using a strategy based on a multiplicative drift correction procedure coupled with a PLS adaptation of the component correction. It must be performed specifically for each sequence, using all sequence signals data. With this procedure, a drastic reduction of sensor signal %RSD can be obtained, ranging between 91.5 and 99.7% for long sequences and between 75.7 and 98.8% for short sequences. On the other hand, long-term drift can be fixed up using a synthetic reference standard mix (with a representation of main aroma volatiles of the species) to be included in each sequence that would enable sequence standardization. With this integral strategy, a high number of samples can be analyzed in different sequences, with a 94.4% success in the aggrupation of the same materials in PLS-DA two-dimensional graphical representations. Using this graphical interface, e-noses can be used to developed expandable maps of volatile profile similitudes, which will be useful to select the materials that most resemble breeding objectives or to analyze which preharvest and postharvest procedures have a lower impact on the volatile profile, avoiding the costs and sample limitations of gas chromatography.

Thermal Drift Correction for Laboratory Nano Computed Tomography via Outlier Elimination and Feature Point Adjustment.

Thermal drift of nano-computed tomography (CT) adversely affects the accurate reconstruction of objects. However, feature-based reference scan correction methods are sometimes unstable for images with similar texture and low contrast. In this study, based on the geometric position of features and the structural similarity (SSIM) of projections, a rough-to-refined rigid alignment method is proposed to align the projection. Using the proposed method, the thermal drift artifacts in reconstructed slices are reduced. Firstly, the initial features are obtained by speeded up robust features (SURF). Then, the outliers are roughly eliminated by the geometric position of global features. The features are refined by the SSIM between the main and reference projections. Subsequently, the SSIM between the neighborhood images of features are used to relocate the features. Finally, the new features are used to align the projections. The two-dimensional (2D) transmission imaging experiments reveal that the proposed method provides more accurate and robust results than the random sample consensus (RANSAC) and locality preserving matching (LPM) methods. For three-dimensional (3D) imaging correction, the proposed method is compared with the commonly used enhanced correlation coefficient (ECC) method and single-step discrete Fourier transform (DFT) algorithm. The results reveal that proposed method can retain the details more faithfully.

CDrift: An Algorithm to Correct Linear Drift From A Single High-Resolution STEM Image.

In this work, a new method to determine and correct the linear drift for any crystalline orientation in a single-column-resolved high-resolution scanning transmission electron microscopy (HR-STEM) image, which is based on angle measurements in the Fourier space, is presented. This proposal supposes a generalization and the improvement of a previous work that needs the presence of two symmetrical planes in the crystalline orientation to be applicable. Now, a mathematical derivation of the drift effect on two families of asymmetric planes in the reciprocal space is inferred. However, though it was not possible to find an analytical solution for all conditions, a simple formula was derived to calculate the drift effect that is exact for three specific rotation angles. Taking this into account, an iterative algorithm based on successive rotation/drift correction steps is devised to remove drift distortions in HR-STEM images. The procedure has been evaluated using a simulated micrograph of a monoclinic material in an orientation where all the reciprocal lattice vectors are different. The algorithm only needs four iterations to resolve a 15° drift angle in the image.

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking.

Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most significant impact in determining the correct orientation and accurate tracking. A gyroscope drift correction approach is proposed in this study and is incorporated in an INS and ultra-wideband (UWB) fusion IPS where only distance measurements from UWB subsystem are used. The drift correction approach is based on turn detection to account for the fact that gyroscope drift is accumulated during a turn. Practical pedestrian tracking experiments are conducted to demonstrate the accuracy of the drift correction approach. With the gyroscope drift corrected, the fusion IPS is able to provide more accurate tracking performance and achieve up to 64.52% mean position error reduction when compared to the INS only tracking result.

Quantitative MR thermometry based on phase-drift correction PRF shift method at 0.35 T.

BACKGROUND: Noninvasive magnetic resonance thermometry (MRT) at low-field using proton resonance frequency shift (PRFS) is a promising technique for monitoring ablation temperature, since low-field MR scanners with open-configuration are more suitable for interventional procedures than closed systems. In this study, phase-drift correction PRFS with first-order polynomial fitting method was proposed to investigate the feasibility and accuracy of quantitative MR thermography during hyperthermia procedures in a 0.35 T open MR scanner. METHODS: Unheated phantom and ex vivo porcine liver experiments were performed to evaluate the optimal polynomial order for phase-drift correction PRFS. The temperature estimation approach was tested in brain temperature experiments of three healthy volunteers at room temperature, and in ex vivo porcine liver microwave ablation experiments. The output power of the microwave generator was set at 40 W for 330 s. In the unheated experiments, the temperature root mean square error (RMSE) in the inner region of interest was calculated to assess the best-fitting order for polynomial fit. For ablation experiments, relative temperature difference profile measured by the phase-drift correction PRFS was compared with the temperature changes recorded by fiber optic temperature probe around the microwave ablation antenna within the target thermal region. RESULTS: The phase-drift correction PRFS using first-order polynomial fitting could achieve the smallest temperature RMSE in unheated phantom, ex vivo porcine liver and in vivo human brain experiments. In the ex vivo porcine liver microwave ablation procedure, the temperature error between MRT and fiber optic probe of all but six temperature points were less than 2 °C. Overall, the RMSE of all temperature points was 1.49 °C. CONCLUSIONS: Both in vivo and ex vivo experiments showed that MR thermometry based on the phase-drift correction PRFS with first-order polynomial fitting could be applied to monitor temperature changes during microwave ablation in a low-field open-configuration whole-body MR scanner.

Localization Microscopy of Actin Cytoskeleton in Human Platelets.

Here, we measure the actin cytoskeleton arrangement of different morphological states of human platelets using a new protocol for photo-switching of rhodamine class fluorophores. A new medium composition was established for imaging the cytoskeleton using Alexa Fluor 488 conjugated to phalloidin. Morphological states of platelets bound to a glass substrate are visualized and quantified by two-dimensional localization microscopy at nanoscopic resolution. Marker-less drift correction yields localization of individual Alexa 488 conjugated to phalloidin with a positional accuracy of 12 nm.

Focus: The interface between data collection and data processing in cryo-EM.

We present a new software package called Focus that interfaces cryo-transmission electron microscopy (cryo-EM) data collection with computer image processing. Focus creates a user-friendly environment to import and manage data recorded by direct electron detectors and perform elemental image processing tasks in a high-throughput manner while new data is being acquired at the microscope. It provides the functionality required to remotely monitor the progress of data collection and data processing, which is essential now that automation in cryo-EM allows a steady flow of images of single particles, two-dimensional crystals, or electron tomography data to be recorded in overnight sessions. The rapid detection of any errors that may occur greatly increases the productivity of recording sessions at the electron microscope.

Impact of and correction for instrument sensitivity drift on nanoparticle size measurements by single-particle ICP-MS.

The effect of ICP-MS instrument sensitivity drift on the accuracy of nanoparticle (NP) size measurements using single particle (sp)ICP-MS is investigated. Theoretical modeling and experimental measurements of the impact of instrument sensitivity drift are in agreement and indicate that drift can impact the measured size of spherical NPs by up to 25 %. Given this substantial bias in the measured size, a method was developed using an internal standard to correct for the impact of drift and was shown to accurately correct for a decrease in instrument sensitivity of up to 50 % for 30 and 60 nm gold nanoparticles. Graphical Abstract Correction of nanoparticle size measurement by spICP-MS using an internal standard.

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10(-1) to 5.3 × 10(-7), respectively.

A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation.

Poor calibration and inaccurate drift correction can pose severe problems for eye-tracking experiments requiring high levels of accuracy and precision. We describe an algorithm for the offline correction of eye-tracking data. The algorithm conducts a linear transformation of the coordinates of fixations that minimizes the distance between each fixation and its closest stimulus. A simple implementation in MATLAB is also presented. We explore the performance of the correction algorithm under several conditions using simulated and real data, and show that it is particularly likely to improve data quality when many fixations are included in the fitting process.