Drift Correction Research Articles

Geometric phase correction: A direct phase correction method to register low contrast noisy TEM images

A major challenge in the emerging field of low-dose electron microscopy lies in the development of drift correction algorithms against beam-induced specimen motion and compatible with highly noisy transmission electron microscopy (TEM) images. We report here a new drift correction method, namely geometric phase correlation (GPC), to correlate the specimen motion in real space by directly measuring the unwrapped geometric phase shift in the spatial frequency domain of the TEM image (especially from the intensive Bragg spots for crystalline materials) with sub-pixel precision. The GPC method outperforms cross-correlation-based methods in both accuracy of specimen motion prediction from highly noisy TEM movies and computational efficiency of drift calculation from abundant image frames, which holds great promise for diverse applications in low-dose TEM imaging of beam-sensitive materials, such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs).

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

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. A rosette MRSI sequence was implemented to acquire data from the brains of 5 healthy volunteers. FIDs from the center of k-space ( FIDs) were isolated from each shot of the rosette acquisition, and time-domain spectral registration was used to estimate the frequency offset of each FID relative to a reference scan (the first FID in the series). The estimated frequency offsets were then used to apply corrections throughout -space. Improvements in spectral quality were assessed before and after drift correction. 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. 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.

Calibration and Uncertainty Quantification for Single-Ended Raman-Based Distributed Temperature Sensing: Case Study in a 800 m Deep Coaxial Borehole Heat Exchanger

Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method was proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods were implemented for a distributed thermal response test (DTRT) case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty increasing non-linearly from about 0.4 K near the surface to about 1.7 K at 800 m. The temperature uncertainty is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation.

Analysis of clock drift based on a three‐dimensional controlled‐source ocean bottom seismometer experiment

AbstractClock drift is common for the autonomous ocean bottom seismometer, and a technique of linear drift correction is usually used to fix this kind of timing error assuming that the internal clock has constant drift rate during deployment. However, non‐linear component of clock drift has also been found recently. In order to test the behaviours of the clock drift for 19 Chinese ocean bottom seismometers from a three‐dimensional controlled‐source seismic survey, we have analysed two types of differences in direct wave travel‐times, including one from all shots to the ocean bottom seismometer onboard and another one from the nearest two shots on different shooting lines to the ocean bottom seismometer on the seafloor. Comparison of the differences in direct wave travel‐times from seismic data with and without clock drift correction shows that linear drift correction can commendably eliminate them for most of the ocean bottom seismometers, but some large residuals remain for part of the ocean bottom seismometers. These residuals are inferred to be possibly related to the non‐linear component of clock drift caused by the temperature shock during deployment. The non‐linear clock drift usually varies from different ocean bottom seismometers and thus difficult to correct completely and resultantly will influence the timing accuracy somewhat. Hence, higher uncertainties are suggested to assign to the picked travel‐times for the ocean bottom seismometer with possibly larger non‐linear clock drift in the further tomographic modelling and the internal clock for this kind of ocean bottom seismometer should be recalibrated prior to the next experiment if which requires higher timing accuracy.

Customization of an atomic force microscope for multidimensional measurements under environmental conditions.

Atomic force microscopy (AFM) is an analytical surface characterization tool that reveals the surface topography at a nanometer length scale while probing local chemical, mechanical, and even electronic sample properties. Both contact (performed with a constant deflection of the cantilever probe) and dynamic operation modes (enabled by demodulation of the oscillation signal under tip-sample interaction) can be employed to conduct AFM-based measurements. Although surface topography is accessible regardless of the operation mode, the resolution and the availability of the quantified surface properties depend on the mode of operation. However, advanced imaging techniques, such as frequency modulation, to achieve high resolution, quantitative surface properties are not implemented in many commercial systems. Here, we show the step-by-step customization of an atomic force microscope. The original system was capable of surface topography and basic force spectroscopy measurements while employing environmental control, such as temperature variation of the sample/tip, etc. We upgraded this original setup with additional hardware (e.g., a lock-in amplifier with phase-locked loop capacity, a high-voltage amplifier, and a new controller) and software integration while utilizing its environmental control features. We show the capabilities of the customized system with frequency modulation-based topography experiments and automated voltage and/or distance spectroscopy, time-resolved AFM, and two-dimensional force spectroscopy measurements under ambient conditions. We also illustrate the enhanced stability of the setup with active topography and frequency drift corrections. We believe that our methodology can be useful for the customization and automation of other scanning probe systems.

Nail it! vision-based drift correction for accurate mixed reality surgical guidance.

Mixed reality-guided surgery through head-mounted displays (HMDs) is gaining interest among surgeons. However, precise tracking of HMDs relative to the surgical environment is crucial for successful outcomes. Without fiducial markers, spatial tracking of the HMD suffers from millimeter- to centimeter-scale drift, resulting in misaligned visualization of registered overlays. Methods and workflows capable of automatically correcting for drift after patient registration are essential to assuring accurate execution of surgical plans. We present a mixed reality surgical navigation workflow that continuously corrects for drift after patient registration using only image-based methods. We demonstrate its feasibility and capabilities using the Microsoft HoloLens on glenoid pin placement in total shoulder arthroplasty. A phantom study was conducted involving five users with each user placing pins on six glenoids of different deformity, followed by a cadaver study by an attending surgeon. In both studies, all users were satisfied with the registration overlay before drilling the pin. Postoperative CT scans showed 1.5mm error in entry point deviation and 2.4[Formula: see text] error in pin orientation on average in the phantom study and 2.5mm and 1.5[Formula: see text] in the cadaver study. A trained user takes around 90s to complete the workflow. Our method also outperformed HoloLens native tracking in drift correction. Our findings suggest that image-based drift correction can provide mixed reality environments precisely aligned with patient anatomy, enabling pin placement with consistently high accuracy. These techniques constitute a next step toward purely image-based mixed reality surgical guidance, without requiring patient markers or external tracking hardware.

High-Throughput Single-Molecule Sensors: How Can the Signals Be Analyzed in Real Time for Achieving Real-Time Continuous Biosensing?

Single-molecule sensors collect statistics of single-molecule interactions, and the resulting data can be used to determine concentrations of analyte molecules. The assays are generally end-point assays and are not designed for continuous biosensing. For continuous biosensing, a single-molecule sensor needs to be reversible, and the signals should be analyzed in real time in order to continuously report output signals, with a well-controlled time delay and measurement precision. Here, we describe a signal processing architecture for real-time continuous biosensing based on high-throughput single-molecule sensors. The key aspect of the architecture is the parallel computation of multiple measurement blocks that enables continuous measurements over an endless time span. Continuous biosensing is demonstrated for a single-molecule sensor with 10,000 individual particles that are tracked as a function of time. The continuous analysis includes particle identification, particle tracking, drift correction, and detection of the discrete timepoints where individual particles switch between bound and unbound states, yielding state transition statistics that relate to the analyte concentration in solution. The continuous real-time sensing and computation were studied for a reversible cortisol competitive immunosensor, showing how the precision and time delay of cortisol monitoring are controlled by the number of analyzed particles and the size of the measurement blocks. Finally, we discuss how the presented signal processing architecture can be applied to various single-molecule measurement methods, allowing these to be developed into continuous biosensors.

Survey of oligoethylene glycol-based self-assembled monolayers on electrochemical aptamer-based sensor in biological fluids

The ability to monitor levels of endogenous markers and clearance profiles of drugs and their metabolites can improve the quality of biomedical research and precision with which therapies are individualized. Towards this end, electrochemical aptamer-based (EAB) sensors have been developed that support the real-time monitoring of specific analytes in vivo with clinically relevant specificity and sensitivity. A challenge associated with the in vivo deployment of EAB sensors, however, is how to manage the signal drift which, although correctable, ultimately leads to unacceptably low signal-to-noise ratios, limiting the measurement duration. Motivated by the correction of signal drift, in this paper, we have explored the use of oligoethylene glycol (OEG), a widely employed antifouling coating, to reduce the signal drift in EAB sensors. Counter to expectations, however, when challenged in 37 °C whole blood in vitro, EAB sensors employing OEG-modified self-assembled monolayers exhibit both greater drift and reduced signal gain, compared with those employ a simple, hydroxyl-terminated monolayer. On the other hand, when EAB sensor was prepared with a mix monolayer using MCH and lipoamido OEG 2 alcohol, reduced signal noise was observed compared to the same sensor prepared with MCH presumably due to improved SAM construction. These results suggest broader exploration of antifouling materials will be required to improve the signal drift of EAB sensors.

Augmented Reality for high precision fabrication of Glued Laminated Timber beams

In recent years, the utilization of Augmented-Reality (AR) within the construction industry has increased. However, high-precision applications such as glulam beam fabrication requires tolerances of less than 2 mm, which is smaller than what current AR workflows can offer. This research investigates the use of QR-Code Markers on glulam beams to encode additional positional data in the environment to better interpolate between the physical space, the glulam beam, and the headset. The objective is to understand the effects of Marker placement, size, and frequency on the accuracy of AR projection for glulam fabrication. This paper describes the AR workflow, the effects of Markers, and the framework the Twinbuild software for drift correction in large-scale AR applications. The method can achieve an average tolerance as low as 0.97 mm when Markers are placed in 1.25 ft. increments along the beam edge. The research demonstrates the viability of AR for high-precision fabrication applications.

Equivalent electrical model for high current density zinc electrodeposition: Investigation through polarization curves and electrochemical impedance spectroscopy

Polarization resistance of a batch process at 2000 Am−2 of zinc electrodeposition is investigated by means of Electrochemical Impedance Spectroscopy (EIS) and polarization curves U(j). This work proposes EIS and U(j) characterisations over time with current density range from 50 Am−2 to 4000 Am−2, corresponding to a variation from [H2SO4] = 130 gL−1 and [Zn2+] = 120 gL−1 to approximately [H2SO4] = 280 gL−1 and [Zn2+] = 20 gL−1. A drift correction of EIS measurement using linear interpolation in time is provided. These measurements allow for the observation of the main physicochemical reactions occurring during the Ergosup batch process and contributing to the polarization resistance of zinc electrodeposition at high current density: charge transfer and adsorption at the cathode, charge transfer and mass transport at the anode. Simplified reaction mechanisms are used and has led to the development of an equivalent electrical model, which accounts for the various phenomena observed during the process. The equivalent electrical model is developed from the kinetic equations describing the electrochemical process. The model is able to reproduce the experimental impedance curves at high current densities with high correlation coefficients (R2> 0.9990).

Application of optical flow algorithm for drift correction in electron microscopy images.

Transmission electron microscopy (TEM) image drift correction has been effectively addressed using diverse approaches, including the cross correlation algorithm (CC) and other strategies. However, most of the strategies fall short of achieving sufficient accuracy or cannot strike a balance between time consumption and accuracy. The present study proposes a TEM image drift correction strategy that enhances accuracy without any additional time consumption. Unlike the CC algorithm that matches pixels one by one, our approach involves the extraction of multiple feature points from the first TEM image and then uses the Lucas-Kanade (LK) optical flow algorithm to calculate the optical field of these feature points in the subsequent TEM images. The LK algorithm is used to calculate the instantaneous velocity of these feature points, which can help track the movement of the TEM image series. In addition, a high-precision sub-pixel level correction strategy by the utilization of linear interpolation during the correction process is developed in this work. Experimental results confirm that this strategy offers superior accuracy in comparison with the CC algorithm and also is insensitive to the size of the image. Furthermore, we offer a semantic segmentation neural network for electron microscope image pre-processing, thereby expanding the applicability of our methodology.

OC-0279 Validation of intra-fraction adaptations for the MR-linac: novel drift correction and replanning

OC-0279 Validation of intra-fraction adaptations for the MR-linac: novel drift correction and replanning

Serial intravital 2-photon microscopy and analysis of the kidney using upright microscopes.

Serial intravital 2-photon microscopy of the kidney and other abdominal organs is a powerful technique to assess tissue function and structure simultaneously and over time. Thus, serial intravital microscopy can capture dynamic tissue changes during health and disease and holds great potential to characterize (patho-) physiological processes with subcellular resolution. However, successful image acquisition and analysis require significant expertise and impose multiple potential challenges. Abdominal organs are rhythmically displaced by breathing movements which hamper high-resolution imaging. Traditionally, kidney intravital imaging is performed on inverted microscopes where breathing movements are partly compensated by the weight of the animal pressing down. Here, we present a custom and easy-to-implement setup for intravital imaging of the kidney and other abdominal organs on upright microscopes. Furthermore, we provide image processing protocols and a new plugin for the free image analysis software FIJI to process multichannel fluorescence microscopy data. The proposed image processing pipelines cover multiple image denoising algorithms, sample drift correction using 2D registration, and alignment of serial imaging data collected over several weeks using landmark-based 3D registration. The provided tools aim to lower the barrier of entry to intravital microscopy of the kidney and are readily applicable by biomedical practitioners.

Combined noise reduction and DOA estimation algorithm for MEMS vector hydrophone based on variational mode decomposition

PurposeThis paper aims to solve the problem that strong noise interference seriously affects the direction of arrival (DOA) estimation in complex underwater acoustic environment. In this paper, a combined noise reduction algorithm and micro-electro-mechanical system (MEMS) vector hydrophone DOA estimation algorithm based on singular value decomposition (SVD), variational mode decomposition (VMD) and wavelet threshold denoising (WTD) is proposed.Design/methodology/approachFirstly, the parameters of VMD are determined by SVD, and the VMD method can decompose the signal into multiple intrinsic mode functions (IMFs). Secondly, the effective IMF component is determined according to the correlation coefficient criterion and the IMF less than the threshold is processed by WTD. Then, reconstruction is carried out to achieve the purpose of denoising and calibration baseline drift. Finally, DOA estimation is achieved by the combined directional algorithm of preprocessed signal.FindingsSimulation and field experiments results show that the algorithm has good noise reduction and baseline drift correction effects for nonstationary underwater signals, and high-precision azimuth estimation is realized.Originality/valueThis research provides the basis for MEMS hydrophone detection and positioning and has great engineering significance in underwater detection system.

Drift correction in laboratory nanocomputed tomography using joint feature correlation.

Laboratory nanocomputed tomography (nano-CT), which can provide a spatial resolution of up to 100nm, has been widely used due to its volume advantage. However, the drift of the x-ray source focal spot and the thermal expansion of the mechanical system can cause projection drift during long-time scanning. The three-dimensional result reconstructed from the drifted projections contains severe drift artifacts, which reduce the spatial resolution of nano-CT. Registering the drifted projections using rapidly acquired sparse projections is one of the mainstream correction methods, but the high noise and contrast differences of projections in nano-CT affect the correction effectiveness of existing methods. Herein, we propose a rough-to-refined projection registration method, which fully combines the information of the features in the gray and frequency domains of the projections. Simulation data show that the drift estimation accuracy of the proposed method is improved by 5× and 16× compared with the mainstream random sample consensus and locality preserving matching based on features. The proposed method can effectively improve the imaging quality of nano-CT.

A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture.

Tracking skeletal muscle architecture using B-mode ultrasound is a widely used method in the field of human movement science and biomechanics. Sequential methods based on optical flow algorithms allow for smooth and coherent muscle tracking but are known to drift over time. Non-sequential feature detection methods on the other hand, do not suffer from drift, but are limited to tracking only lower-dimensional features. They are also known to be sensitive to image noise, and therefore often result in highly irregular tracking patterns. Building on the complimentary nature of both approaches, we present a novel automated hybrid muscle tracking approach that combines a sequential feature-point tracking method and a non-sequential method based on Hough transform. Tibialis anterior fascicle pennation angle and length, and central aponeurosis displacement, were measured in five healthy individuals during isometric contractions at five different ankle angles. Our hybrid method was demonstrated to significantly (p < 0.001) reduce drift compared to two sequential methods, and curve irregularity was significantly (p < 0.001) decreased compared to a non-sequential method. These findings suggest that the proposed hybrid approach can uniquely mitigate drift and irregularity limitation of individual methods used for tracking skeletal muscle architecture. Automated muscle tracking allows for convenient analysis of large datasets, whereas automatic drift correction opens the door for tracking muscle architecture in long ultrasound recordings during common movements, such as walking, running, and jumping without the need for manual intervention.

Single-molecule localization microscopy reveals the ultrastructural constitution of distal appendages in expanded mammalian centrioles

Distal appendages (DAPs) are vital in cilia formation, mediating vesicular and ciliary docking to the plasma membrane during early ciliogenesis. Although numerous DAP proteins arranging a nine-fold symmetry have been studied using superresolution microscopy analyses, the extensive ultrastructural understanding of the DAP structure developing from the centriole wall remains elusive owing to insufficient resolution. Here, we proposed a pragmatic imaging strategy for two-color single-molecule localization microscopy of expanded mammalian DAP. Importantly, our imaging workflow enables us to push the resolution limit of a light microscope well close to a molecular level, thus achieving an unprecedented mapping resolution inside intact cells. Upon this workflow, we unravel the ultra-resolved higher-order protein complexes of the DAP and its associated proteins. Intriguingly, our images show that C2CD3, microtubule triplet, MNR, CEP90, OFD1, and ODF2 jointly constitute a unique molecular configuration at the DAP base. Moreover, our finding suggests that ODF2 plays an auxiliary role in coordinating and maintaining DAP nine-fold symmetry. Together, we develop an organelle-based drift correction protocol and a two-color solution with minimum crosstalk, allowing a robust localization microscopy imaging of expanded DAP structures deep into the gel-specimen composites.

Multi-echo gradient echo pulse sequences: which is best for PRFS MR thermometry guided hyperthermia?

Purpose MR thermometry (MRT) enables noninvasive temperature monitoring during hyperthermia treatments. MRT is already clinically applied for hyperthermia treatments in the abdomen and extremities, and devices for the head are under development. In order to optimally exploit MRT in all anatomical regions, the best sequence setup and post-processing must be selected, and the accuracy needs to be demonstrated. Methods MRT performance of the traditionally used double-echo gradient-echo sequence (DE-GRE, 2 echoes, 2D) was compared to multi-echo sequences: a 2D fast gradient-echo (ME-FGRE, 11 echoes) and a 3D fast gradient-echo sequence (3D-ME-FGRE, 11 echoes). The different methods were assessed on a 1.5 T MR scanner (GE Healthcare) using a phantom cooling down from 59 °C to 34 °C and unheated brains of 10 volunteers. In-plane motion of volunteers was compensated by rigid body image registration. For the ME sequences, the off-resonance frequency was calculated using a multi-peak fitting tool. To correct for B0 drift, the internal body fat was selected automatically using water/fat density maps. Results The accuracy of the best performing 3D-ME-FGRE sequence was 0.20 °C in phantom (in the clinical temperature range) and 0.75 °C in volunteers, compared to DE-GRE values of 0.37 °C and 1.96 °C, respectively. Conclusion For hyperthermia applications, where accuracy is more important than resolution or scan-time, the 3D-ME-FGRE sequence is deemed the most promising candidate. Beyond its convincing MRT performance, the ME nature enables automatic selection of internal body fat for B0 drift correction, an important feature for clinical application.

A robust method for approximate visual robot localization in feature-sparse sewer pipes.

Buried sewer pipe networks present many challenges for robot localization systems, which require non-standard solutions due to the unique nature of these environments: they cannot receive signals from global positioning systems (GPS) and can also lack visual features necessary for standard visual odometry algorithms. In this paper, we exploit the fact that pipe joints are equally spaced and develop a robot localization method based on pipe joint detection that operates in one degree-of-freedom along the pipe length. Pipe joints are detected in visual images from an on-board forward facing (electro-optical) camera using a bag-of-keypoints visual categorization algorithm, which is trained offline by unsupervised learning from images of sewer pipe joints. We augment the pipe joint detection algorithm with drift correction using vision-based manhole recognition. We evaluated the approach using real-world data recorded from three sewer pipes (of lengths 30, 50 and 90m) and benchmarked against a standard method for visual odometry (ORB-SLAM3), which demonstrated that our proposed method operates more robustly and accurately in these feature-sparse pipes: ORB-SLAM3 completely failed on one tested pipe due to a lack of visual features and gave a mean absolute error in localization of approximately 12%-20% on the other pipes (and regularly lost track of features, having to re-initialize multiple times), whilst our method worked successfully on all tested pipes and gave a mean absolute error in localization of approximately 2%-4%. In summary, our results highlight an important trade-off between modern visual odometry algorithms that have potentially high precision and estimate full six degree-of-freedom pose but are potentially fragile in feature sparse pipes, versus simpler, approximate localization methods that operate in one degree-of-freedom along the pipe length that are more robust and can lead to substantial improvements in accuracy.

DecodeSTORM: A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.