Scanner Calibration Research Articles

Active Laser-Camera Scanning for High-Precision Fruit Localization in Robotic Harvesting: System Design and Calibration

Robust and effective fruit detection and localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving fruit detection, less emphasis has been placed on the fruit localization aspect, which is a crucial yet challenging task due to limited depth accuracy from existing sensor measurements in the natural orchard environment with variable lighting conditions and foliage/branch occlusions. In this paper, we present the system design and calibration of an Active LAser-Camera Scanner (ALACS), a novel perception module for robust and high-precision fruit localization. The hardware of the ALACS mainly consists of a red line laser, an RGB camera, and a linear motion slide, which are seamlessly integrated into an active scanning scheme where a dynamic-targeting laser-triangulation principle is employed. A high-fidelity extrinsic model is developed to pair the laser illumination and the RGB camera, enabling precise depth computation when the target is captured by both sensors. A random sample consensus-based robust calibration scheme is then designed to calibrate the model parameters based on collected data. Comprehensive evaluations are conducted to validate the system model and calibration scheme. The results show that the proposed calibration method can detect and remove data outliers to achieve robust parameter computation, and the calibrated ALACS system is able to achieve high-precision localization with the maximum depth measurement error being less than 4 mm at distance ranging from 0.6 to 1.2 m.

Modern Scanning Electron Microscopy. 2. Test objects for Scanning Electron Microscopy

The review of the test objects intended for calibration of scanning electron microscopes and researches to secondary electronic emission of a relief surface of a solid state in scanning electron microscope is carried out. The test objects are divided on two parameters – kind of a relief and structure of a relief. By the form of relief the test objects are divided on single, pitch and periodic. On a structure of a relief of the test objects are divided into objects with a rectangular structure and objects with a trapezoid structure with the large and small corners of an inclination of lateral walls. The examples of such the test objects are given. Their characteristics and methods of certification of parameters are described. The advantages and lacks of the test objects are considered. Is shown, that the best characteristics have the tests objects representing pitch structures consisting of trapezoid trenches with the large corners of an inclination of lateral walls. The test objects are created in monosilicon with of a surface orientation by {100} by method liquid anisotropic etching of silicon. These test objects allow defining all characteristics scanning electron microscopes, influencing on measurement of the linear sizes of relief structures used in microelectronics and nanotechnology. With their help it is possible to carry out correlation measurements, which raise accuracy of calibration scanning electron microscopes up to ten times.

SOME ISSUES OF THE CALCULATION PROCESS OPTIMIZATION IN TERRESTRIAL LASER SCANNERS CALIBRATION

A conventional and widely used mathematical model of laser scanners calibration has such an imperfection as correlation between some of parameters during the adjustment process and quite complex numerical solution. A method of avoiding the correlation between scanner and polygon transformation parameters and calibration parameters as well as calibration numerical solution simplification is considered. It is proposed to implement a separate transformation parameters computation by using Kabsch-Umeyama algorithm that can compute Helmert transformation parameters even in case of gross errors present in data set. But direct transformation before adjustment leads to parametric equations and following numerical solution complication. Therefore it is also proposed to conduct a reverse transformation from polygon coordinate system into scanner coordinate system as it simplifies further equations linearization and allows simplifying the numerical solution in general. As it is more than one station to be scanned during the calibration process, one cannot simply swap coordinate systems. The polygon coordinate system remains as original one but reverse Helmert transformation is conducted. To prove this approach is correct, three datasets using different laser scanners and high-precise total station were obtained. Direct and reverse transformation between scanners and total station coordinate systems was made for all datasets and MSE of transformation was calculated for each case. Thus, the possibility of using the reverse transformation to optimize the process of the calibration parameters determining has been experimentally proven.

Gradient-based geometry learning for fan-beam CT reconstruction

Objective. Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. Approach. The CT fan-beam reconstruction is analytically derived with respect to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion-affected reconstruction alone. Main results. The algorithm improves the structural similarity index measure (SSIM) from 0.848 for the initial motion-affected reconstruction to 0.946 after compensation. It also generalizes to real fan-beam sinograms which are rebinned from a helical trajectory where the SSIM increases from 0.639 to 0.742. Significance. Using the proposed method, we are the first to optimize an autofocus-inspired algorithm based on analytical gradients. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.

Peranan Magnetic Resonance Imaging (MRI) Lumbal dalam Penegakan Diagnosis Kasus Low Back Pain: Studi Kasus di Instalasi Radiologi Rumah Sakit Satya Negara Jakarta Utara

Background: Magnetic resonance imaging (MRI) is considered the best imaging for chronic low back pain because of high tissue contrast and no effect of ionizing radiation. The purpose of this study was to determine the management of Lumbar MRI examination with Low Back Pain Cases.Methods: The research used was a qualitative descriptive research method by collecting data by direct observation which was conducted in December 2022 at Satya Negara Hospital, North Jakarta. The research was conducted using direct observation methods, documentation and decision studies. The sample used was 1 patient.Results: The image results are strengthened by the expertise of a radiologist. The results of the study were obtained from the beginning of the patient's process of coming to radiology to perform a Lumbar MRI examination in the administration section until the examination was completed. Using a GE 1.5 T MRI machine and a whole spine coil. The sequences used are 3-plane location, calibration scan lumbar, Coronal T2, Sagittal T2, Sagittal T1, Sagittal T2 + FATSAT, Axial T2, Sagittal Myelography, Coronal Myelography.Conclusions: This study shows that Lumbar MRI image results and the doctor's expertise show that there is grade I spondylolithesis and L4-L5 spondyloarthrosis with disc protrusion. It’s mean that MRI can provide information for making a diagnosis.

Standardizing HER2 immunohistochemistry assessment: calibration of color and intensity variation in whole slide imaging caused by staining and scanning

In the evaluation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) — one of the standard biomarkers for breast cancer— visual assessment is laborious and subjective. Image analysis using whole slide image (WSI) could produce more consistent results; however, color variability in WSIs due to the choice of stain and scanning processes may impact image analysis. We therefore developed a calibration protocol to diminish the staining and scanning variations of WSI using two calibrator slides. The IHC calibrator slide (IHC-CS) contains peptide-coated microbeads with different concentrations. The color distribution obtained from the WSI of stained IHC-CS reflects the staining process and scanner characteristics. A color chart slide (CCS) is also useful for calibrating the color variation due to the scanner. The results of the automated HER2 assessment were compared to confirm the effectiveness of two calibration slides. The IHC-CS and HER2 breast cancer cases were stained on different days. All stained slides and CCS were digitized by two different WSI scanners. Results revealed 100% concordance between automated evaluation and the pathologist’s assessment with both the scanner and staining calibration. The proposed method may enable consistent evaluation of HER2.

Magnetization in superconducting corrector magnets and impact on luminosity–calibration scans in the Large Hadron Collider

Superconducting accelerator magnets have a nonlinear dependence of field on current due to the magnetization associated with the iron or with persistent currents in the superconducting filaments. This also gives rise to hysteresis phenomena that create a dependence of the field on the powering history. Magnetization effects are of particular importance for luminosity–calibration scans in the Large Hadron Collider, during which a small number of Nb–Ti superconducting orbit correctors are excited at low field and with frequent flipping of the sign of the current ramp. This paper focuses on the analysis of special measurements carried out to estimate these nonlinear effects under the special cycling conditions used in these luminosity scans. For standard powering cycles, we evaluate the effect of the main magnetization loop; for complex operational schemes, magnetization-branch transitions occur that depend on the details of the current cycle. The modelling of these effects is not included in the magnetic field prediction software currently implemented in the LHC control system; here we present an approach to predict the transitions between the main magnetization branches. The final aim is to estimate the impact of magnetic hysteresis on the accuracy of luminosity-calibration scans.

Automatic calibration of terrestrial laser scanners using intensity features

AbstractWe propose an in situ self‐calibration method by detecting and matching intensity features on the local planes in overlapping point clouds based on the Förstner operator. We successfully matched the intensity features from scans at different locations by feature matching on common local planes rather than on the rasterised grids of the horizontal and vertical angles adopted by the affirmed keypoint‐based algorithm. The capability of extracting features from different stations offers the possibility of comprehensive scanner calibration, solving the disadvantage that the existing keypoint‐based methods can only estimate the two‐face‐sensitive model parameters. The proposed algorithm has been tested with a high‐precision panoramic scanner, Leica RTC360, using datasets from a calibration hall and a general working scenario. It has been shown that the proposed approach consistently calibrates the two‐face‐sensitive model parameters with the affirmed keypoint‐based one. For the case of comprehensive calibration with the offset estimated and some angular parameters separated where the previous keypoint‐based one failed, the proposed algorithm achieves an accuracy of 0.16 mm, 2.7″ and 2.1″ in range, azimuth and elevation for the estimated target centres. The proposed algorithm can accurately calibrate two‐face‐sensitive and more comprehensive model parameters without any preparation on‐site, for example, mounting targets.

Quality control of fat fraction quantification in magnetic resonance imaging: A two-center phantom study

BACKGROUND: Assessment of quantitative parameters using magnetic resonance imaging (MRI) is a relevant trend. Fat fraction (FF) calculation provides new opportunities for accurate diagnosis and will replace invasive methods such as biopsy in the future. Quantification will enable reliable dynamic monitoring and assessment of drug therapy. However, radiologists and clinical specialists must be confident in the accuracy and reliability of the quantitative measures.
 AIM: To assess the accuracy of quantitative FF measurement using phantom simulation in the range of 0% to 60%.
 METHODS: Emulsions of the oil-in-water type were chosen to simulate the objects of the study. Concentrations of vegetable oil-based emulsions were presented in the range of 0% to 60%. Tubes containing the emulsions were placed in a cylindrical phantom. Scans were performed on an Optima, MR450w 1.5 Tesla (GE) tomograph in Lava Flex mode and on an Ingenia, 1.5 Tesla (Philips) tomograph in DIXON mode. FF was determined by formulas using images in In-phase and Out-phase based on signal characteristics (FF=[InOut]/2∙In∙100) and images weighted by Water and Fat data (FF=Fat/[Fat+water]∙100.
 RESULTS: The accuracy of the fat percentage measurement with the DIXON technique was identical to that of the Lava Flex. The data of the measured fat concentration were systematically overestimated in relation to the target values by an average of 57.6% with an average absolute difference of 17.2%. In addition, an irregular underestimation in the range of 20% to 40% was detected.
 CONCLUSIONS: Phantom simulation using direct oil-in-water emulsions allowed to control the performance of the Dixon sequences in quantifying the FF. For correct FF quantification, calculation from Water and Fat image data using the formula FF=Fat/(Fat+water)∙100 is preferable. Calculations based on In-phase and Out-phase images provide ambiguous results. The FF calculation in the Lava Flex and DIXON modes must be performed with a correction factor. The use of the phantom allows proper quality control and calibration of the MRI scanner and makes quantitative fat measurement widely available.

A second-order and slice-specific linear shimming technique to improve spinal cord fMRI

PurposeTo develop a second-order and slice-specific linear shimming technique and investigate its efficiency in the mitigation of signal loss and distortions, and the increase of temporal signal-to-noise ratio (tSNR) within the spinal cord during functional Magnetic Resonance Imaging (fMRI) of the human cervical spinal cord. MethodsAll scans were performed on a General Electric Discovery MR750 3 T scanner, using a head, neck and spine coil and a neurovascular array. To improve B0 homogeneity, a field map was acquired, and second-order shims (SOS) were optimized over manually defined regions of interest (ROIs). Signal loss from dephasing by susceptibility-induced gradients was reduced by optimizing slice-specific x-, y- and z-shims to maximize signal within the spinal cord. Spectral-spatial excitation pulses were used in both the slice-specific linear shimming calibration scan and fMRI acquisitions. The shimming technique's efficiency was initially tested on eight healthy volunteers by comparing tSNR between images acquired with the manufacturer's standard linear shimming and with our SOS and xyz-shimming technique. Subsequently, using an increased spatial resolution as needed for fMRI of the spinal cord, tSNR measurements were performed on resting-state fMRI images from 14 healthy participants. ResultsSpinal fMRI images acquired with only the standard linear shimming suffered from severe signal loss below the C5 vertebral level. The developed shimming technique compensated for this loss especially at levels C6 and C7, while tSNR was significantly higher at all vertebral levels with SOS and xyz-shimming than without it. ConclusionA comprehensive shimming approach which includes the use of spectral-spatial excitation pulses along with both second-order and slice-specific linear shim optimization reduces regional signal loss and increases tSNR along the c-spine (C3-C7), improving the ability to record functional signals from the human spinal cord.

A novel whale-based algorithm for optimizing the ANN approach: application to temperature compensation in pressure scanner calibration systems

To resolve the nonlinear measured values of pressure scanners with temperature change during pressure measurements, resulting in low accuracy, this paper proposes and develops a new calibration system based on neural networks for pressure scanners. Specifically, we first calibrate the pressure scanner production process, then design the sealing device applicable to the working environment of pressure scanners, and finally install the calibration system of the pressure scanner for calibration experiments to obtain accurate measurement results. In addition, at the algorithmic level, this paper forwards a whale optimization algorithm-based backpropagation neural network method instead of the traditional least squares method to complete the temperature compensation. Both the offline compensation results and the compensated online experimental results exhibit the high measurement accuracy of this temperature compensation method. The full-scale (FS) error is 0.07%, the coefficient of determination (R 2) = 99.27%, with similar results for all channels of the pressure sensor in the temperature environment of −40 °C to 60 °C and absolute pressures ranging from 0 to 1.1 MPa. This result presents a significant reduction in the FS error of 0.25%, R 2 = 91.32% based on the conventional algorithm compensation, and also applies to pressure scanners with wide temperature region, wide range, and high accuracy temperature compensation, which is crucial for their future research of low-cost overall calibration and high accuracy algorithms.

Automatic extrinsic calibration of terrestrial laser scanner and digital camera by MoG image correlation

Automatic extrinsic calibration of terrestrial laser scanner and digital camera by MoG image correlation

SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans.

The blood oxygen level dependent (BOLD) signal from functional magnetic resonance imaging (fMRI) is a noninvasive technique that has been widely used in research to study brain function. However, fMRI suffers from susceptibility-induced off resonance fields which may cause geometric distortions and mismatches with anatomical images. State-of-the-art correction methods require acquiring reverse phase encoded images or additional field maps to enable distortion correction. However, not all imaging protocols include these additional scans and thus cannot take advantage of these susceptibility correction capabilities. As such, in this study we aim to enable state-of-the-art distortion correction with FSL's topup algorithm of historical and/or limited fMRI data that include only a structural image and single phase encoded fMRI. To do this, we use 3D U-net models to synthesize undistorted fMRI BOLD contrast images from the structural image and use this undistorted synthetic image as an anatomical target for distortion correction with topup. We evaluate the efficacy of this approach, named SynBOLD-DisCo (synthetic BOLD images for distortion correction), and show that BOLD images corrected using our approach are geometrically more similar to structural images than the distorted BOLD data and are practically equivalent to state-of-the-art correction methods which require reverse phase encoded data. Future directions include additional validation studies, integration with other preprocessing operations, retraining with broader pathologies, and investigating the effects of spin echo versus gradient echo images for training and distortion correction. In summary, we demonstrate SynBOLD-DisCo corrects distortion of fMRI when reverse phase encoding scans or field maps are not available.

Vision-based spatial damage localization method for autonomous robotic laser cladding repair processes

Repair technologies have been considered as sustainable approaches due to their capability to restore value in a damaged component and bring it to like-new condition. However, in contrast to a manufacturing process benefiting from an automated environment, the automation level for repair and remanufacturing processes remains low. With the aim of moving the repair industry towards autonomy, this study proposes a novel repair framework. The developed methodology presents a vision-based Robotic Laser Cladding Repair Cell (RLCRC) that has two features: (a) an intelligent inspection system that uses a deep learning model to automatically detect the damaged region in an image; (b) employing computer vision-based calibration and 3D scanning techniques to precisely identify the geometries of damaged area. The repair of fixed bends is selected as the case study. The results obtained validate the efficacy of the proposed framework, enabling automatic damage detection and damaged volume extraction for worn fixed bends. Following the suggested framework, a time reduction of more than 63% is reported.

Superior sagittal sinus flow as a proxy for tracking global cerebral blood flow dynamics during wakefulness and sleep

Sleep, a state of reduced consciousness, affects brain oxygen metabolism and lowers cerebral metabolic rate of oxygen (CMRO2). Previously, we quantified CMRO2 during sleep via Fick’s Principle, with a single-band MRI sequence measuring both hemoglobin O2 saturation (SvO2) and superior sagittal sinus (SSS) blood flow, which was upscaled to obtain total cerebral blood flow (tCBF). The procedure involves a brief initial calibration scan to determine the upscaling factor (fc), assumed state-invariant. Here, we used a dual-band sequence to simultaneously provide SvO2 in SSS and tCBF in the neck every 16 seconds, allowing quantification of fc dynamically. Ten healthy subjects were scanned by MRI with simultaneous EEG for 80 minutes, yielding 300 temporal image frames per subject. Four volunteers achieved slow-wave sleep (SWS), as evidenced by increased δ-wave activity (per American Academy of Sleep Medicine criteria). SWS was maintained for 13.5 ± 7.0 minutes, with CMRO2 28.6 ± 5.5% lower than pre-sleep wakefulness. Importantly, there was negligible bias between tCBF obtained by upscaling SSS-blood flow, and tCBF measured directly in the inflowing arteries of the neck (intra-class correlation 0.95 ± 0.04, averaged across all subjects), showing that the single-band approach is a valid substitute for quantifying tCBF, simplifying image data collection and analysis without sacrificing accuracy.

Self-calibration of terrestrial laser scanner using a M3C2-based planar patch algorithm

Target- or intensity-feature-based calibration has been extensively explored for in-situ calibration of terrestrial laser scanners (TLSs). Those calibration approaches main drawbacks include the necessity of mounting physical targets in the scan area and the need for the presence of rich geometrical features allowing a feature-based distinction of different points in the point cloud. We propose an autonomous TLS calibration algorithm using planar patches that are ubiquitous in urban environments and can be found without manual preparation beforehand. The scanner calibration parameters are estimated and updated by minimizing the M3C2 normal distances between corresponding planar patches. Unlike target- or keypoint-based approaches, only medium-resolution scan data is needed. A comprehensive set of calibration model parameters are estimated using scans acquired from multiple positions, thereby providing a further advantage over current keypoint-based approaches that estimate only two-face sensitive parameters. To increase speed and reduce memory consumption, we propose to execute the calibration using only parts of scan data from a single station. Studies with two high-precision scanners, Leica RTC360 and Z+F Imager 5016, show results consistent with the laborious target-based approach with improved precision. Similar two-face sensitive calibration performance to the target- and keypoint-based approaches has been achieved, but with only subsets of the point cloud using the proposed algorithm.

Multi-channel GAN-based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction.

Diffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging. This study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors. The performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model. The current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.

Calibration of a galvanometric laser scanner for line-art projection in a dynamic scene

Calibration of a galvanometric laser scanner for line-art projection in a dynamic scene

Capacitive Sensing for 2-D Electrostatic MEMS Scanner in a Clinical Endomicroscope.

A flexible fiber-coupled confocal laser endomicroscope has been developed using an electrostatic micro-electromechanical system (MEMS) scanner located in at distal optics to collect in vivo images in human subjects. Long transmission lines are required that deliver drive and sense signals with limited bandwidth. Phase shifts have been observed between orthogonal X and Y scanner axes from environmental perturbations, which impede image reconstruction. Image processing algorithms used for correction depend on image content and quality, while scanner calibration in the clinic can be limited by potential patient exposure to lasers. We demonstrate a capacitive sensing method to track the motion of the electrostatically driven two-dimensional MEMS scanner and to extract phase information needed for image reconstruction. This circuit uses an amplitude modulation envelope detection method on shared drive and sensing electrodes of the scanner. Circuit parameters were optimized for performance given high scan frequencies, transmission line effects, and substantial parasitic coupling of drive signal to circuit output. Extraction of phase information further leverages nonlinear dynamics of the MEMS scanner. The sensing circuit was verified by comparing with data from a position sensing detector measurement. The phase estimation showed an accuracy of 2.18° and 0.79° in X and Y axes for motion sensing, respectively. The results indicate that the sensing circuit can be implemented with feedback control for pre-calibration of the scanner in clinical MEMS-based imaging systems.

TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging.

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging.