Geometric Distortion Correction Research Articles

Correction for geometric distortion in the flattened representation of pipeline external surface

Conventional cameras often produce external pipeline surface images with significant perspective-projection distortions, making determining the type and size of defects on the pipeline surface difficult. To address this issue, we present a novel approach for correcting the geometric distortion of a constant diameter pipeline (CDP) external surface image based on apparent contour pairs (ACPs). First, the camera is calibrated and the real projection position of ACPs accurately extracted. Second, a single-view perspective projection model of the CDP is adopted to realize a three-dimensional reconstruction of the pipeline. Subsequently, the flattened image of the CDP’s outer surface is obtained by performing equidistant sampling of cross sections, determining the visibility of discrete points, and mapping projection points onto a flat surface. Simulations and real experiments demonstrated the high accurate method in the correction of CDP images. Experiments conducted under different outdoor scenarios confirmed the robustness of the proposed method.

Algorithm for correction of geometric distortions of spectral images recorded using an acousto-optical hyperspectrometer under conditions of angular deviations

Algorithm for correction of geometric distortions of spectral images recorded using an acousto-optical hyperspectrometer under conditions of angular deviations

Enhancing Single-Plane Fluoroscopy: A Self-Calibrating Bundle Adjustment for Distortion Modeling.

Single-plane fluoroscopy systems with image intensifiers remain commonly employed in a clinical setting. The imagery they capture is vulnerable to several types of geometric distortions introduced by the system's components and their assembly as well as interactions with the local and global magnetic fields. In this study, the application of a self-calibrating bundle adjustment is investigated as a method to correct geometric distortions in single-plane fluoroscopic imaging systems. The resulting calibrated imagery is then applied in the quantitative analysis of diaphragmatic motion and potential diagnostic applications to hemidiaphragm paralysis. The calibrated imagery is further explored and discussed in its potential impact on areas of surgical navigation. This work was accomplished through the application of a controlled experiment with three separate Philips Easy Diagnost R/F Systems. A highly redundant (~2500 to 3500 degrees-of-freedom) and geometrically strong network of 18 to 22 images of a low-cost target field was collected. The target field comprised 121 pre-surveyed tantalum beads embedded on a 25.4 mm × 25.4 mm acrylic base plate. The modeling process resulted in the estimation of five to eight distortion coefficients, depending on the system. The addition of these terms resulted in 83-85% improvement in terms of image point precision (model fit) and 85-95% improvement in 3D object reconstruction accuracy after calibration. This study demonstrates significant potential in enhancing the accuracy and reliability of fluoroscopic imaging, thereby improving the overall quality and effectiveness of medical diagnostics and treatments.

High-precision Astrometry and Photometry with the JWST/MIRI Imager

Astrometry is one of the main pillars of astronomy, and one of its oldest branches. Over the years, an increasing number of astrometric works by means of Hubble Space Telescope (HST) data have revolutionized our understanding of various phenomena. With the launch of JWST, it becomes almost instinctive to want to replicate or improve these results with data taken with the newest, state-of-the-art, space-based telescope. In this regard, the initial focus of the community has been on the Near-Infrared detectors on board of JWST because of their high spatial resolution. This paper begins the effort to capture and apply what has been learned from HST to the Mid-InfraRed Instrument (MIRI) of JWST by developing the tools to obtain high-precision astrometry and photometry with its imager. We describe in detail how to create accurate effective point-spread-function (ePSF) models and geometric-distortion corrections, analyze their temporal stability, and test their quality to the extent of what is currently possible with the available data in the JWST MAST archive. We show that careful data reduction provides deep insight on the performance and intricacies of the MIRI imager, and of JWST in general. In an effort to help the community devise new observing programs, we make our ePSF models and geometric-distortion corrections publicly available.

Application of UAV-Borne Visible-Infared Pushbroom Imaging Hyperspectral for Rice Yield Estimation Using Feature Selection Regression Methods

Rice yield estimation is vital for enhancing food security, optimizing agricultural management, and promoting sustainable development. However, traditional satellite/aerial and ground-based/tower-based platforms face limitations in rice yield estimation, and few studies have explored the potential of UAV-borne hyperspectral remote sensing for this purpose. In this study, we employed a UAV-borne push-broom hyperspectral camera to acquire remote sensing data of rice fields during the filling stage, and the machine learning regression algorithms were applied to rice yield estimation. The research comprised three parts: hyperspectral data preprocessing, spectral feature extraction, and model construction. To begin, the preprocessing of hyperspectral data involved geometric distortion correction, relative radiometric calibration, and rice canopy mask construction. Challenges in geometric distortion correction were addressed by tracking linear features during flight and applying a single-line correction method. Additionally, the NIR reflectance threshold method was applied for rice canopy mask construction, which was subsequently utilized for average reflectance extraction. Then, spectral feature extraction was carried out to reduce multicollinearity in the hyperspectral data. Recursive feature elimination (RFE) was then employed to identify the optimal feature set for model performance. Finally, six machine learning regression models (SVR, RFR, AdaBoost, XGBoost, Ridge, and PLSR) were used for rice yield estimation, achieving significant results. PLSR showed the best R2 of 0.827 with selected features, while XGBoost had the best R2 of 0.827 with full features. In addition, the spatial distribution of absolute error in rice yield estimation was assessed. The results suggested that this UAV-borne imaging hyperspectral-based approach held great potential for crop yield estimation, not only for rice but also for other crops.

Patient-specific geometrical distortion corrections of MRI images improve dosimetric planning accuracy of vestibular schwannoma treated with gamma knife stereotactic radiosurgery.

To investigate the impact of MRI patient-specific geometrical distortion (PSD) on the quality of Gamma Knife stereotactic radiosurgery (GK-SRS) plans of the vestibular schwannoma (VS) tumors. Three open access datasets including the MPI-Leipzig Mind-Brain-Body (318 patients), the slow event-related fMRI designs dataset (62 patients), and the VS dataset (242 patients) were used. We used first two datasets to train a 3D convolution network to predict the distortion map of third dataset that were then used to calculate and correct the PSD. GK-SRS plans of VS dataset were used to evaluate dose distribution of PSD-corrected MRI images. GK-SRS prescription dose of VS cases was 12Gy. Geometric and dosimetric discrepancies were assessed between the dose distributions and contours before and after the PSD corrections. Geometry indices were center of the contours, Dice coefficient (DC), Hausdorff distance (HD), and dosimetric indices were , , , and doses, target coverage (TC), Paddick's conformity index (PCI), Paddick's gradient index (GI), and homogeneity index (HI). Geometric distortions of about 1.2mm were observed at the air-tissue interfaces at the air canal and nasal cavity borders. Average center of the targets was significantly distorted along the frequency encoding direction after the PSD-correction. Average DC and HD metrics were 0.90 and 2.13mm. Average , and in Gy significantly increased after PSD correction from 16.85 to 17.25, 12.30 to 12.77, and from 8.98 to 9.92. did not significantly change after the correction. Average TC and PCI significantly increased from 0.97 to 0.98, and 0.94 to 0.96. Average GI decreased significantly from 2.24 to 2.15 after PSD correction. However, HI did not significantly change after the correction. The proposed method could predict and correct the PSD that indicates the importance of PSD correction before GK-SRS plans of the VS patients.

Effect of geometric distortion correction on thickness and volume measurements of cortical parcellations in 3D T1w gradient echo sequences.

Automated brain volumetric analysis based on high-resolution T1-weighted MRI datasets is a frequently used tool in neuroimaging for early detection, diagnosis, and monitoring of various neurological diseases. However, image distortions can corrupt and bias the analysis. The aim of this study was to explore the variability of brain volumetric analysis due to gradient distortions and to investigate the effect of distortion correction methods implemented on commercial scanners. 36 healthy volunteers underwent brain imaging using a 3T magnetic resonance imaging (MRI) scanner, including a high-resolution 3D T1-weighted sequence. For all participants, each T1-weighted image was reconstructed directly on the vendor workstation with (DC) and without (nDC) distortion correction. For each participant's set of DC and nDC images, FreeSurfer was used for the determination of regional cortical thickness and volume. Overall, significant differences were found in 12 cortical ROIs comparing the volumes of the DC and nDC data and in 19 cortical ROIs comparing the thickness of the DC and nDC data. The most pronounced differences for cortical thickness were found in the precentral gyrus, the lateral occipital and postcentral ROI (2.69, -2.91% and -2.79%, respectively) while cortical volumes differed most prominently in the paracentral, the pericalcarine and lateral occipital ROI (5.52%, -5.40% and -5.11%, respectively). Correcting for gradient non-linearities can have significant influence on volumetric analysis of cortical thickness and volume. Since the distortion correction is an automatic feature of the MR scanner, it should be stated by each study that applies volumetric analysis which images were used.

Algorithm for Correction of Geometric Distortions of Hyperspectral Images Formed during Angular Oscillations of an Unmanned Aerial Vehicle

Algorithm for Correction of Geometric Distortions of Hyperspectral Images Formed during Angular Oscillations of an Unmanned Aerial Vehicle

Virtual Sensor-Based Geometry Prediction of Complex Sheet Metal Parts Formed by Robotic Rollforming

Sheet metal parts can often replace milled components, strongly improving the buy-to-fly ratio in the aeronautical sector. However, the sheet metal forming of complex parts traditionally requires expensive tooling, which is usually prohibitive for low manufacturing rates. To achieve precise parts, non-productive and cost-intensive geometry straightening processes are additionally often required after forming. Rollforming is a possible technology for producing profiles at large rates. For low manufacturing rates, robotic rollforming can be an interesting option, significantly reducing investment at the cost of higher manufacturing times while keeping a high process flexibility. Forming is performed incrementally by a single roller set moved by the robot along predefined bending curves. The present work’s contribution to the overall solution is the development of an intelligent algorithm to calculate geometry after a robotic rollforming process based on process reaction forces. This information is required for in-process geometric distortion correction. Reaction forces and torques are acquired during the process, and geometry is calculated based on artificial intelligence (AI) applied to that information. The present paper describes the AI development for this virtual geometry sensing system.

The Design and Implementation of the Lookup Table in Distortion Correction for the Optical System

Image geometric distortion is common in a large field of view optical system. It becomes a technical difficulty for high distortion rate optical systems and high resolution image geometric distortion correction. But, a crucial step in distortion correction is figuring out how to precisely obtain the index coordinates and correction parameters, otherwise known as the lookup table. In this paper, the problems in the traditional lookup table method are analyzed, and an improved lookup table method is proposed. The method is based on the traditional lookup table method with the addition of removing the wrong data of position coordinates in the correction image feature points, calculating the ideal image feature point position coordinates, handling the index coordinate out-of-bounds, and adjusting the output order of index coordinates and correction parameters. The experimental results show that the improved design method is feasible, and improves the accuracy of the correction image, compared with the traditional lookup table, the root mean square error is reduced by 93%, and the average accuracy of the fitted position over the full field of view is reduced by 51.9%, and the maximum deviation is only 0.2917%. For the correction of the large field of view optical display devices, it offers good correction effects.

Geometric Distortion Correction of Images Received From Biometric Fingerprint Devices

The quality of fingerprint images is very important for recognition and identification in biometric systems. Geometric distortions and different image scales may worsen fingerprint recognition. It is necessary to adjust distortions and scale in biometric finger capture systems. Algorithms for correcting distortions and scale are investigated, analyzed, and developed in this paper. A polynomial model of second order is used as a mathematical model of spatial distortions. To correct the brightness of image points, bicubic interpolation is used.

System-dependent image distortion related to gantry positions of a 0.35 T MRgRT: Characterization and the corresponding correction.

MR-guided radiotherapy with high accuracy treatment planning requires addressing MR imaging artifacts that originate from system imperfections. This work presents the characterization and corresponding correction of gantry-related imaging distortions including geometric distortion and isocenter shift in a 0.35 T magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT) system using distortion vector fields (DVFs). Two phantoms, the magnetic resonance imaging distortion in 3D (MRID3D ) phantom and the Fluke phantom, along with a human volunteer were imaged at different gantry angles on a 0.35 T MR-Linac. The geometric distortion and isocenter shift were characterized for both phantom images. DVFs with a field of view extended beyond the physical boundary of the MRID3D phantom were extracted from images taken at 30° gantry angle increments, with vendor-provided distortion correction turned on and off (DstOff). These extended DVFs were then applied to the relevant phantom images to correct their geometric distortions and isocenter shift at the respective gantry angles. The extended DVFs produced from the MRID3D phantom were also applied to Fluke phantom and human MR images at their respective gantry angles. The resampled images were evaluated using structural similarity index measure (SSIM) comparison with the vendor corrected images from the MRgRT system. Geometric distortion with "mean (±SD) distortion" of 3.2±0.02, 2.9±0.02, and 1.8±0.01mm and isocenter shift (±SD) of 0.49±0.3, 0.05±0.2, and 0.01±0.03mm were present in the DstOff MRID3D phantom images in right-left (RL), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. After resampling the originally acquired images by applying extended DVFs, the distortion was corrected to 0.18±0.02, 0.09±0.01, 0.15±0.01mm, and isocenter shift was corrected to 0.14±0.05, -0.02±0.04, and -0.07±0.05mm in RL, AP, and SI directions, respectively. The Fluke phantom average geometric distortion with "mean (±SD) distortion" of 2.7±0.1mm was corrected to 0.2±0. 1mm and the average isocenter shift (±SD) of 0.51±0.2mm, and 0.05±0.03 was corrected to -0.08±0.03mm, and -0.05±0.01 in RL and AP directions, respectively. SSIM (mean±SD) of the original images to resampled images was increased from 0.49±0.02 to 0.78±0.01, 0.45±0.02 to 0.75±0.01, and 0.86±0.25 to 0.98±0.08 for MRID3D phantom, Fluke phantom, and human MR images, respectively, for all the gantry angles compared to the vendor corrected images. The gantry-related MR imaging distortion including geometric distortion and isocenter shift was characterized and a corresponding correction was demonstrated using extended DVFs on 0.35 T MRgRT system. The characterized gantry-related isocenter shift can be combined with geometric distortion correction to provide a technique for the correction of the full system-dependent distortion in an MRgRT system.

Slice-direction geometric distortion evaluation and correction with reversed slice-select gradient acquisitions

Accurate spatial alignment of MRI data acquired across multiple contrasts in the same subject is often crucial for data analysis and interpretation, but can be challenging in the presence of geometric distortions that differ between acquisitions. It is well known that single-shot echo-planar imaging (EPI) acquisitions suffer from distortion in the phase-encoding direction due to B0 field inhomogeneities arising from tissue magnetic susceptibility differences and other sources, however there can be distortion in other encoding directions as well in the presence of strong field inhomogeneities. High-resolution ultrahigh-field MRI typically uses low bandwidth in the slice-encoding direction to acquire thin slices and, when combined with the pronounced B0 inhomogeneities, is prone to an additional geometric distortion in the slice direction as well. Here we demonstrate the presence of this slice distortion in high-resolution 7T EPI acquired with a novel pulse sequence allowing for the reversal of the slice-encoding gradient polarity that enables the acquisition of pairs of images with equal magnitudes of distortion in the slice direction but with opposing polarities. We also show that the slice-direction distortion can be corrected using gradient reversal-based method applying the same software used for conventional corrections of phase-encoding direction distortion.

Skull phantom-based methodology to validate MRI co-registration accuracy for Gamma Knife radiosurgery.

Target localization, for stereotactic radiosurgery (SRS) treatment with Gamma Knife, has become increasingly reliant on the co-registration between the planning MRI and the stereotactic cone-beam computed tomography (CBCT). Validating image registration between modalities would be particularly beneficial when considering the emergence of novel functional and metabolic MRI pulse sequences for target delineation. This study aimed to develop a phantom-based methodology to quantitatively compare the co-registration accuracy of the standard clinical imaging protocol to a representative MRI sequence that was likely to fail co-registration. The comparative methodology presented in this study may serve as a useful tool to evaluate the clinical translatability of novel MRI sequences. A realistic human skull phantom with fiducial marker columns was designed and manufactured to fit into a typical MRI head coil and the Gamma Knife patient positioning system. A series of "optimized" 3D MRI sequences-T1 -weighted Dixon, T1 -weighted fast field echo (FFE), and T2 -weighted fluid-attenuated inversion recovery (FLAIR)-were acquired and co-registered to the CBCT. The same sequences were "compromised" by reconstructing without geometric distortion correction and re-collecting with lower signal-to-noise-ratio (SNR) to simulate a novel MRI sequence with poor co-registration accuracy. Image similarity metrics-structural similarity (SSIM) index, mean squared error (MSE), and peak SNR (PSNR)-were used to quantitatively compare the co-registration of the optimized and compromised MR images. The ground truth fiducial positions were compared to positions measured from each optimized image volume revealing a maximum median geometric uncertainty of 0.39mm (LR), 0.92mm (AP), and 0.13mm (SI) between the CT and CBCT, 0.60mm (LR), 0.36mm (AP), and 0.07mm (SI) between the CT and T1 -weighted Dixon, 0.42mm (LR), 0.23mm (AP), and 0.08mm (SI) between the CT and T1 -weighted FFE, and 0.45mm (LR), 0.19mm (AP), and 1.04mm (SI) between the CT and T2 -weighted FLAIR. Qualitatively, pairs of optimized and compromised image slices were compared using a fusion image where separable colors were used to differentiate between images. Quantitatively, MSE was the most predictive and SSIM the second most predictive metric for evaluating co-registration similarity. A clinically relevant threshold of MSE, SSIM, and/or PSNR may be defined beyond which point an MRI sequence should be rejected for target delineation based on its dissimilarity to an optimized sequence co-registration. All dissimilarity thresholds calculated using correlation coefficients with in-plane geometric uncertainty would need to be defined on a sequence-by-sequence basis and validated with patient data. This study utilized a realistic skull phantom and image similarity metrics to develop a methodology capable of quantitatively assessing whether a modern research-based MRI sequence can be co-registered to the Gamma Knife CBCT with equal or less than equal accuracy when compared to a clinically accepted protocol.

Improved geometric accuracy of whole body diffusion-weighted imaging at 1.5T and 3T using reverse polarity gradients

Whole body diffusion-weighted imaging (WB-DWI) is increasingly used in oncological applications, but suffers from misalignments due to susceptibility-induced geometric distortion. As such, DWI and structural images acquired in the same scan session are not geometrically aligned, leading to difficulties in e.g. lesion detection and segmentation. In this work we assess the performance of the reverse polarity gradient (RPG) method for correction of WB-DWI geometric distortion. Multi-station DWI and structural magnetic resonance imaging (MRI) data of healthy controls were acquired at 1.5T (n = 20) and 3T (n = 20). DWI data was distortion corrected using the RPG method based on b = 0 s/mm2 (b0) and b = 50 s/mm2 (b50) DWI acquisitions. Mutual information (MI) between low b-value DWI and structural data increased with distortion correction (P < 0.05), while improvements in region of interest (ROI) based similarity metrics, comparing the position of incidental findings on DWI and structural data, were location dependent. Small numerical differences between non-corrected and distortion corrected apparent diffusion coefficient (ADC) values were measured. Visually, the distortion correction improved spine alignment at station borders, but introduced registration-based artefacts mainly for the spleen and kidneys. Overall, the RPG distortion correction gave an improved geometric accuracy for WB-DWI data acquired at 1.5T and 3T. The b0- and b50-based distortion corrections had a very similar performance.

Increased brain volumetric measurement precision from multi-site 3D T1-weighted 3 T magnetic resonance imaging by correcting geometric distortions

PurposeMagnetic resonance imaging (MRI) scanner-specific geometric distortions may contribute to scanner induced variability and decrease volumetric measurement precision for multi-site studies. The purpose of this study was to determine whether geometric distortion correction increases the precision of brain volumetric measurements in a multi-site multi-scanner study. MethodsGeometric distortion variation was quantified over a one-year period at 10 sites using the distortion fields estimated from monthly 3D T1-weighted MRI geometrical phantom scans. The variability of volume and distance measurements were quantified using synthetic volumes and a standard quantitative MRI (qMRI) phantom. The effects of geometric distortion corrections on MRI derived volumetric measurements of the human brain were assessed in two subjects scanned on each of the 10 MRI scanners and in 150 subjects with cerebrovascaular disease (CVD) acquired across imaging sites. ResultsGeometric distortions were found to vary substantially between different MRI scanners but were relatively stable on each scanner over a one-year interval. Geometric distortions varied spatially, increasing in severity with distance from the magnet isocenter. In measurements made with the qMRI phantom, the geometric distortion correction decreased the standard deviation of volumetric assessments by 35% and distance measurements by 42%. The average coefficient of variance decreased by 16% in gray matter and white matter volume estimates in the two subjects scanned on the 10 MRI scanners. ConclusionGeometric distortion correction using an up-to-date correction field is recommended to increase precision in volumetric measurements made from MRI images.

On-Orbit Geometric Distortion Correction on Star Images through 2D Legendre Neural Network

The star tracker is a prerequisite device to realize high-precision attitude determination for a spacecraft. However, due to the errors in optical lens machining, optical path assembly, and temperature alternation, optical instruments suffer from some amount of optical geometric distortion, resulting in declining star tracker accuracy. The on-orbit distortion correction of star images is indispensable for precise performance. In this paper, a novel single-layer 2D Legendre neural network (2DLNN) to automatically correct the geometric distortion of the star tracker is proposed. An offline training method grounded on batch star images and an online training algorithm based on sequential star images are designed, respectively. The 2DLNN realizes the ground-based and on-orbit online correction of optical geometric distortion for the star tracker. The 2DLNN features self-learning, lifelong learning, and good adaptability. The single-layer neural network is simple, quick convergence, which is suitable for on-orbit implementation. The simulations demonstrate that the average distortion error can be reduced to less than 0.04 px after ground-based training. In the earth-orientation mode of the LEO satellite, the on-orbit sequential training algorithm can converge in 2500 star images under 1 frame/s. The proposed 2DLNN can achieve high-precision correction at the sub-pixel level, effectively improving the star tracker’s attitude determination accuracy.

A deeper solution to the actual geometry of CCD mosaic chips

Context. For charge-coupled device (CCD) mosaic chips in the focal plane of a large telescope, the unification for all the measurements of each chip is vital to some scientific projects, such as deep astrometric standards or construction for deeper images that can also seamlessly cover a larger area of the sky. A key part of the reduction involves the accurate geometric distortion (GD) correction and the precise determination of the relative positions of the CCD chips. The short-term and long-term stabilities of them are also important when it comes to studying whether there are systematic variations in the optical system of the telescope. Aims. We present a solution to determine the actual or physical relative positions between CCD chips. Due to the limited depth of the Gaia catalogue, there may be few stars identified from the Gaia catalogue for astrometric calibration on the deep observation of a large, ground-based or space-based telescope, such as the planned two-metre Chinese Space Station Telescope (CSST). For this reason, we referred to the idea from the Hubble Space Telescope (HST) astrometry to only use stars’ pixel positions to derive the relative positions between chips. We refer to the practice as differential astrometry in this paper. In order to ensure the results are reliable, we took advantage of Gaia EDR3 to derive the relative positions between chips, to provide a close comparison. We refer to the practice as photographic astrometry. Methods. By taking advantage of the GD solution and the common distortion-free frame derived from the observations, we related the physical positions of the adjacent pixel edges of two CCD chips and estimated the actual relative positions between chips. We implemented the technique for the CCD mosaic chips of the Bok 2.3-m telescope at Kitt Peak based on two epochs of observations (January 17, 2016 and March 5, 2017). Results. There is a good agreement between the two types of astrometry for the relative positions between chips. For the two epochs of observations, the averages of the gaps derived from photographic astrometry and differential astrometry differ to about 0.046 pixels (~0.021 arcsec) and 0.001 pixels (&lt;0.001 arcsec), respectively, while the average precisions of the gaps are about 0.018 pixel (~0.008 arcsec) and 0.028 pixels (~0.013 arcsec), respectively. The results provide us with more confidence in applying this solution for the CCD mosaic chips of the CSST by means of differential astrometry. Compared with the solution described by Anderson &amp; King, which has been used to determine the interchip offset of Wide Field Planetary Camera 2 (WFPC2) chips and Wide Field Camera 3 (WFC3) chips at the HST, the solution proposed in this paper shows at least a factor of two improvement in precision, on average. Conclusions. We think there are two definite advantages of our method. On one hand, we perform the measurements for two adjacent edges instead of two individual chips, allowing the results to be as local as possible, and meanwhile we alleviate the propagated error of residual distortions of each observation deviating from the average solution throughout the field of view (FOV). On the other hand, the final outcome is not mixed up with GD effects, which would bias the realistic geometry of the CCD mosaic chips. Therefore, the proposed method is expected to be an effective technique to monitor the stability of the CCD mosaic chips in the CSST and other ground-based CCD mosaic as well.

New CCD Astrometric Observations of Himalia Using Gaia DR2 in 2015–2021

Abstract The long arc and high-quality astrometric measurements of outer irregular satellites are prerequisites for improving their orbital theories and increasing the precision of their ephemerides. In order to obtain good astrometric positions of Himalia, the largest irregular satellite of Jupiter, we have processed and reduced 911 ground-based CCD frames obtained between 2015 and 2021 by three telescopes (including 1 and 2.4 m telescopes at Yunnan Observatory, and 0.8 m telescope at Purple Mountain Observatory) over 61 nights. Subtracting off the companion star of our target by constructing an effective point-spread function (ePSF) model in some CCD frames, the ePSF-subtracted technique is used to reduce the centering error. Some additional techniques are applied in data reduction to further improve positional accuracy and precision of Himalia. This includes geometric distortion correction, weighted polynomial plate models, and the precision-premium effect, since their relative positional measurements have better precision when two objects are very near (e.g., less than 60″). The star catalog Gaia DR2 is used for astrometric calibration, and theoretical positions of Himalia are retrieved from JPL Horizons ephemeris, including the satellite ephemeris Jup344 and the newest planetary ephemeris DE441. Our results show the mean (O − C)s (observed minus computed) of the positional residuals of Himalia are −0."004 and 0.″005 in R.A. and decl., respectively, and their corresponding standard deviations are about 0.″020 in each direction.

Improving Imaging of the Brainstem and Cerebellum in Autistic Children: Transformation-Based High-Resolution Diffusion MRI (TiDi-Fused) in the Human Brainstem.

Diffusion-weighted magnetic resonance imaging (dMRI) of the brainstem is technically challenging, especially in young autistic children as nearby tissue-air interfaces and motion (voluntary and physiological) can lead to artifacts. This limits the availability of high-resolution images, which are desirable for improving the ability to study brainstem structures. Furthermore, inherently low signal-to-noise ratios, geometric distortions, and sensitivity to motion not related to molecular diffusion have resulted in limited techniques for high-resolution data acquisition compared to other modalities such as T1-weighted imaging. Here, we implement a method for achieving increased apparent spatial resolution in pediatric dMRI that hinges on accurate geometric distortion correction and on high fidelity within subject image registration between dMRI and magnetization prepared rapid acquisition gradient echo (MPnRAGE) images. We call this post-processing pipeline T1 weighted-diffusion fused, or “TiDi-Fused”. Data used in this work consists of dMRI data (2.4 mm resolution, corrected using FSL’s Topup) and T1-weighted (T1w) MPnRAGE anatomical data (1 mm resolution) acquired from 128 autistic and non-autistic children (ages 6–10 years old). Accurate correction of geometric distortion permitted for a further increase in apparent resolution of the dMRI scan via boundary-based registration to the MPnRAGE T1w. Estimation of fiber orientation distributions and further analyses were carried out in the T1w space. Data processed with the TiDi-Fused method were qualitatively and quantitatively compared to data processed with conventional dMRI processing methods. Results show the advantages of the TiDi-Fused pipeline including sharper brainstem gray-white matter tissue contrast, improved inter-subject spatial alignment for group analyses of dMRI based measures, accurate spatial alignment with histology-based imaging of the brainstem, reduced variability in brainstem-cerebellar white matter tracts, and more robust biologically plausible relationships between age and brainstem-cerebellar white matter tracts. Overall, this work identifies a promising pipeline for achieving high-resolution imaging of brainstem structures in pediatric and clinical populations who may not be able to endure long scan times. This pipeline may serve as a gateway for feasibly elucidating brainstem contributions to autism and other conditions.