Geometric Distortion Research Articles

Geometric distortions in clinical MRI sequences for radiotherapy: insights gained from a multicenter investigation

Background As magnetic resonance imaging (MRI) becomes increasingly integrated into radiotherapy (RT) for enhanced treatment planning and adaptation, the inherent geometric distortion in acquired MR images pose a potential challenge to treatment accuracy. This study aimed to evaluate the geometric distortion levels in the clinical MRI protocols used across Danish RT centers and discuss influence of specific sequence parameters. Based on the variety in geometric performance across centers, we assess if harmonization of MRI sequences is a relevant measure. Materials and methods Nine centers participated with 12 MRI scanners and MRI-Linacs (MRL). Using a travelling phantom approach, a reference MRI sequence was used to assess variation in baseline distortion level between scanners. The phantom was also scanned with local clinical MRI sequences for brain, head/neck (H/N), abdomen, and pelvis. The influence of echo time, receiver bandwidth, image weighting, and 2D/3D acquisition was investigated. Results We found a large variation in geometric accuracy across 93 clinical sequences examined, exceeding the baseline variation found between MRI scanners (σ = 0.22 mm), except for abdominal sequences where the variation was lower. Brain and abdominal sequences showed lowest distortion levels ([0.22, 2.26] mm), and a large variation in performance was found for H/N and pelvic sequences ([0.19, 4.07] mm). Post hoc analyses revealed that distortion levels decreased with increasing bandwidth and a less clear increase in distortion levels with increasing echo time. 3D MRI sequences had lower distortion levels than 2D (median of 1.10 and 2.10 mm, respectively), and in DWI sequences, the echo-planar imaging read-out resulted in highest distortion levels. Conclusion There is a large variation in the geometric distortion levels of clinical MRI sequences across Danish RT centers, and between anatomical sites. The large variation observed makes harmonization of MRI sequences across institutions and adoption of practices from well-performing anatomical sites, a relevant measure within RT.

Radar Interferometry for Urban Infrastructure Stability Monitoring: From Techniques to Applications

Urban infrastructure is an important part of supporting the daily operation of a city. The stability of infrastructure is subject to various deformations related to disasters, engineering activities, and loadings. Regular monitoring of such deformations is critical to identify potential risks to infrastructure and take timely remedial actions. Among the advanced geodetic technologies available, radar interferometry has been widely used for infrastructure stability monitoring due to its extensive coverage, high spatial resolution, and accurate deformation measurements. Specifically, spaceborne InSAR and ground-based radar interferometry have become increasingly utilized in this field. This paper presents a comprehensive review of both technologies for monitoring urban infrastructures. The review begins by introducing the principles and their technical development. Then, a bibliometric analysis and the major advancements and applications of urban infrastructure monitoring are introduced. Finally, the paper identifies several challenges associated with those two radar interferometry technologies for monitoring urban infrastructure. These challenges include the inconsistent in the distribution of selected measurements from different methods, obstacles arising from rapid urbanization and geometric distortion, specialized monitoring techniques for distinct urban features, long-term deformation monitoring, and accurate interpretation of deformation. It is important to carry out further research to tackle these challenges effectively.

Self-supervised Semantic Segmentation: Consistency over Transformation.

Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical image segmentation encounter significant challenges due to their heavy dependence on extensive labeled training data. To tackle this issue, we propose a novel self-supervised algorithm, , which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. This architectural enhancement makes it possible to comprehensively capture contextual information while preserving local intricacies, thereby enabling precise semantic segmentation. Furthermore, considering that lesions in medical images often exhibit deformations, we leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Additionally, our self-supervised strategy emphasizes the acquisition of invariance to affine transformations, which is commonly encountered in medical scenarios. This emphasis on robustness with respect to geometric distortions significantly enhances the model's ability to accurately model and handle such distortions. To enforce spatial consistency and promote the grouping of spatially connected image pixels with similar feature representations, we introduce a spatial consistency loss term. This aids the network in effectively capturing the relationships among neighboring pixels and enhancing the overall segmentation quality. The approach iteratively learns pixel-level feature representations for image content clustering in an end-to-end manner. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.

Accurate, repeatable, and geometrically precise diffusion-weighted imaging on a 0.35 T magnetic resonance imaging-guided linear accelerator

Background and purposeDiffusion weighted imaging (DWI) allows for the interrogation of tissue cellularity, which is a surrogate for cellular proliferation. Previous attempts to incorporate DWI into the workflow of a 0.35 T MR-linac (MRL) have lacked quantitative accuracy. In this study, accuracy, repeatability, and geometric precision of apparent diffusion coefficient (ADC) maps produced using an echo planar imaging (EPI)-based DWI protocol on the MRL system is illustrated, and in vivo potential for longitudinal patient imaging is demonstrated. Materials and methodsAccuracy and repeatability were assessed by measuring ADC values in a diffusion phantom at three timepoints and comparing to reference ADC values. System-dependent geometric distortion was quantified by measuring the distance between 93 pairs of phantom features on ADC maps acquired on a 0.35 T MRL and a 3.0 T diagnostic scanner and comparing to spatially precise CT images. Additionally, for five sarcoma patients receiving radiotherapy on the MRL, same-day in vivo ADC maps were acquired on both systems, one of which at multiple timepoints. ResultsPhantom ADC quantification was accurate on the 0.35 T MRL with significant discrepancies only seen at high ADC. Average geometric distortions were 0.35 (±0.02) mm and 0.85 (±0.02) mm in the central slice and 0.66 (±0.04) mm and 2.14 (±0.07) mm at 5.4 cm off-center for the MRL and diagnostic system, respectively. In the sarcoma patients, a mean pretreatment ADC of 910x10-6 (±100x10-6) mm2/s was measured on the MRL. ConclusionsThe acquisition of accurate, repeatable, and geometrically precise ADC maps is possible at 0.35 T with an EPI approach.

Lightweight Distortion-Aware Network for Salient Object Detection in Omnidirectional Images

Compared with 2D image salient object detection (SOD), SOD in omnidirectional images (or 360° images) usually suffers from geometric distortion. Although existing omnidirectional image SOD (ODI-SOD) methods have improved the detection accuracy obviously, their application may be cumbersome in real scenes due to their high computational cost. To avoid distortion and reduce the computational cost simultaneously in ODI-SOD, we propose a novel lightweight distortion-aware network, named LDNet, in this paper. First, to extract features with less distortion from ODIs, we integrate the distortion-aware convolution and depth-wise separable convolution (DSConv) into distortion-aware DSConv (DDSConv) and replace the regular convolutions in the last two blocks of the ResNet-18 with DDSConvs to obtain our lightweight backbone network (LD-ResNet-18). To enhance spatial information in each channel of the extracted features at each level comprehensively, then, we propose a lightweight distortion-aware channel-wise enhancement (DCE) module (only 0.05M parameters) including DDSConvs with various dilation rates, channel shuffle operation and attention mechanism, and employ a high-to-low dense connection structure to modulate the enhanced multi-level features. Besides, we design a distortion-aware self-correlation (DSC) module (only 0.02M parameters) for mining the contextual dependency of the features via a coarse-fine strategy, and the correlated features are refined by DCE modules and integrated by another dense connection structure. The final saliency map is predicted from the densely integrated features. Compared with 12 state-of-the-art methods on two public datasets, our lightweight LDNet achieves competitive or even better performance with only 2.9M parameters and 3.4G FLOPs, which balances the efficiency and performance.

Quantitative Study of Geometric Distortion Variation Caused by Air Susceptibility in Simultaneous Multi-Slice Resolve Diffusion Weighted Imaging Using Phantom and Python OpenCV

Quantitative Study of Geometric Distortion Variation Caused by Air Susceptibility in Simultaneous Multi-Slice Resolve Diffusion Weighted Imaging Using Phantom and Python OpenCV

Improved Value of Multiplexed Sensitivity Encoding DWI with Reversed Polarity Gradients in Diagnosing Prostate Cancer: A Comparison Study with Single-Shot DWI and MUSE DWI

This study aimed to investigate the value of multiplexed sensitivity encoding with reversed polarity gradients in improving the quality of diffusion-weighted imaging (DWI) images of the prostate and the diagnostic efficacy of prostate cancer. Seventy-three patients with prostate disease underwent multiplexed sensitivity encoding with reversed polarity gradients (RPG-MUSE), multiplexed sensitivity encoding (MUSE), and single-shot echo-planar imaging (ssEPI) DWI. Three radiologists performed a qualitative image analysis of the three DWI sequences. Qualitative image analysis included artifact minimization, anatomical detail, and sharpness of prostate edges. Two radiologists measured the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), geometric distortion rate, and the apparent diffusion coefficient (ADC) values of the prostate disease tissue. Two radiologists jointly performed Prostate Imaging Reporting and Data System scoring of prostate lesions and compared the diagnostic efficacy of the three DWI sequences for prostate cancer. There was good agreement among radiologists in the evaluation and measurement of the three DWI sequence images (intraclass correlation coefficient >0.75, P<0.05). The RPG-MUSE DWI images were rated higher than those of MUSE and ssEPI in terms of artifact minimization, anatomical details, and sharpness of prostate edges (P<0.05). The SNR and CNR of the RPG-MUSE DWI images were higher than those of MUSE and ssEPI (P<0.05), and the geometric distortion rate was lower than that of the other two sequences (P<0.05). There were no statistical differences in ADC values between the three DWI sequences (P>0.05). The diagnostic efficacy of RPG-MUSE and MUSE DWI was higher than that of ssEPI (P<0.017). RPG-MUSE can reduce the artifacts and geometric distortion in DWI images of the prostate, improve the SNR and CNR of the images, improve the clarity of anatomical details and boundaries without affecting the measurement of ADC values, has the potential to improve the diagnostic efficacy of prostate lesions, and facilitates the clear display and accurate assessment of prostate lesions.

Identification of Chemical and Structural Characteristics of Acrylic Paint Layer Using Terahertz Metasurfaces.

The precise investigation and monitoring of the internal structural change within complex layered systems are crucial, as the emergence of undesirable defects or formation of secondary internal structures significantly exerts a profound influence on the overall properties of the system. We demonstrate an advanced sensing platform utilizing terahertz metasurfaces, allowing chemical detection and precise identification within an acrylic paint layer with a noticeable sensitivity, reaching down to several hundreds of nanometers, in nondestructive and noncontact manners. The identification of solid and mixed paint samples was achieved by analyzing their optical properties, including the refractive index and absorption coefficient. Notably, the presence of internal pore defects within the mixed acrylic paint led to geometric distortions, affecting the state of the overall system. Intriguingly, even in cases where acrylic paint exhibited identical colors perceptible under visible light, distinct discrimination and identification of chemical compositions were successfully proposed.

Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring.

To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI. A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera. Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy." Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.

Structured low-rank reconstruction for navigator-free water/fat separated multi-shot diffusion-weighted EPI.

Multi-shot diffusion-weighted EPI allows an increase in image resolution and reduced geometric distortions and can be combined with chemical-shift encoding (Dixon) to separate water/fat signals. However, such approaches suffer from physiological motion-induced shot-to-shot phase variations. In this work, a structured low-rank-based navigator-free algorithm is proposed to address the challenge of simultaneously separating water/fat signals and correcting for physiological motion-induced shot-to-shot phase variations in multi-shot EPI-based diffusion-weighted MRI. We propose an iterative, model-based reconstruction pipeline that applies structured low-rank regularization to estimate and eliminate the shot-to-shot phase variations in a data-driven way, while separating water/fat images. The algorithm is tested in different anatomies, including head-neck, knee, brain, and prostate. The performance is validated in simulations and in-vivo experiments in comparison to existing approaches. In-vivo experiments and simulations demonstrated the effectiveness of the proposed algorithm compared to extra-navigated and an alternative self-navigation approach. The proposed algorithm demonstrates the capability to reconstruct in the multi-shot/Dixon hybrid space domain under-sampled datasets, using the same number of acquired EPI shots compared to conventional fat-suppression techniques but eliminating fat signals through chemical-shift encoding. In addition, partial Fourier reconstruction can also be achieved by using the concept of virtual conjugate coils in conjunction with the proposed algorithm. The proposed algorithm effectively eliminates the shot-to-shot phase variations and separates water/fat images, making it a promising solution for future DWI on different anatomies.

Information geometry based extreme low-bit neural network for point cloud

Deep learning has significantly advanced three-dimensional computer vision applied to point clouds. Nevertheless, the substantial consumption of time, storage, and energy substantially limits its deployment on edge devices with constrained resources. Extremely low bit quantization has received wide attention due to its extremely high compression ratio, but the problem of a significant drop in accuracy cannot be ignored. To alleviate the obvious accuracy degradation for extreme low-bit quantization, this paper proposes a novel compression framework for binary and ternary neural networks applied to point clouds, which introduces information geometry to model quantization to compensate the severe feature manifold distortion. It applies differential geometry on manifolds to study the implicit information of the point cloud feature data. Based on the theoretical analysis from the novel perspective of information geometry, two optimization modules are proposed to alleviate severe geometry distortions on differential manifolds. The first module, scaling recovery, provides layer-wise scaling parameters to reduce geometric distortion caused by quantization. The second module, Pooling Recovery, is specially designed to alleviate more severe pooling geometry distortions in point clouds. These two modules benefit both binary and ternary neural networks with ignored overheads. For ternary quantization, optimizations on convolution weights and gradients are additionally introduced. The proposed self-adaptive gradient estimation provides a more accurate approximation to the non-differential ternary staircase function. Convolution weight optimization is implemented on an information-geometry optimized model to achieve even higher accuracy and less memory consumption. Experimental results validate that the proposed models significantly outperform state-of-the-art methods and demonstrate better scalability. Overall, this compression framework has the potential to facilitate the deployment of deep learning models on edge devices with limited resources, opening up new opportunities for applications of three-dimensional computer vision.

Integrating Target and Shadow Features for SAR Target Recognition.

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.

Automated Georectification, Mosaicking and 3D Point Cloud Generation Using UAV-Based Hyperspectral Imagery Observed by Line Scanner Imaging Sensors

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel® Core i7-8850H CPU running at 2.60 GHz.

POS-GIFT: A geometric and intensity-invariant feature transformation for multimodal images

Multimodal image matching suffers from severe geometric and nonlinear intensity distortion (NID). Towards this problem, we propose a multimodal image matching algorithm based on multi-orientation filtering results, called position-orientation-scale guided geometric and intensity-invariant feature transformation (POS-GIFT). First, we design a multi-layer circular point sampling pattern to effectively capture the local image structure. Then, we propose a novel feature descriptor that can work robustly across rotational differences in [0°, 360°) in the presence of NID. Specifically, we (1) integrate the multi-orientation filtering response in the local neighborhood with a Gaussian weight to form the feature of each sampled point (GFP), (2) build feature vectors for each orientation by concatenating the features of points grouped by orientation, (3) estimate the primary orientation by finding the feature vector with the largest norm which is constructed in the previous step, (4) modify the order of elements of GFP, and (5) finally concatenate the features of all sampled points in a certain order to form the complete feature descriptor. At last, we propose a position-orientation-scale guided inlier recovery strategy (POS) by integrating the global position, orientation, and scale information to further improve the matching performance, especially the number and distribution of correct matches in texture-less and complex areas. Experimental results on various multimodal datasets from remote sensing, medical, and computer vision imaging domains show that POS-GIFT outperforms eight state-of-the-art multimodal image feature matching algorithms which are five handcrafted-based methods, OS-SIFT, PSO-SIFT, LGHD, RIFT, and LNIFT, and three learning-based methods RedFeat, MatchFormer, and SemLA by several times in terms of correct matches while improving the root-mean-square error to around 1 pixel. Our implementation is available at https://github.com/Zhuolu-Hou/POS-GIFT.

CBCT: The third eye in orthodontics

Accurate diagnostic imaging is essential to derive the correct diagnosis and treatment plan in orthodontics. In two-dimensional diagnosis, geometric distortion, superimposition of structures, projective displacements, and rotational errors are common. This case series, affirming that the CBCT is vital key in the diagnosis of some specific orthodontic conditions, and presenting five case reports which was difficult to diagnose it with two-dimensional radiograph. Case 1 includes horizontally impacted 21 with crown located labially, and root palatally with close proximity to nasopalatine canal, which was veiled in 2D orthopantomogram. Case 2 is an infrazygomatic implant case, the placement of which was debatable by seeing through the 2D IOPA radiograph. Here, CBCT removed the black curtain and proved its monarchy. Case 3 is a premolar impaction, which in 2D radiograph appeared like an iceberg, but its true anatomy was revealed by the CBCT. After this, the next case 4 is about the central incisor mimicking a canine in 2D radiograph, which was found to be supernumerary tooth on CBCT. Followed by temporomandibular disorder (5) which severity was brought to light by CBCT. The need for this case series is to wave the flag for CBCT in the orthodontic diagnosis which will help in delivering proper treatment plan and emphasis where it is essential to do CBCT.

Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C/L Band Data

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future.

Fault diagnosis of bearing-rotor system based on infrared thermography: ReSPP with multi-scaled training method

The fault diagnosis method of bearing-rotor system based on infrared thermography can reflect the global fault information of the equipment, which is an advanced non-contact monitoring measure. Current diagnosis methods focus on the analysis of single-scale input images, and the network only accepts fixed-size inputs. The model is not capable of fusing multi-scaled fault features while processing single-scaled images, and compression for the size constraint will cause geometric distortion. This paper proposed a novel fault diagnosis model ReSPP (Residual Network with Least Spatial Pyramid Pooling (LSPP)) based on the improved LSPP. LSPP solves the problem of fault feature distortion caused by fixed-size constraint of the network. By replacing the single-scaled training with the proposed multi-scaled training method applied to fault diagnosis, the weight parameters of ReSPP pool the deep fault features of the bearing-rotor system at multiple scales, retaining the critical fault semantic information. The proposed model solves the fault feature loss in the process of feature extraction by improving the subsampling residual block. Experimental results show that ReSPP with multi-scaled training method (ReSPP-MSTM) classifies the fault conditions of the bearing-rotor system with an average diagnostic accuracy of 99.18%.

Evidence of pseudogravitational distortions of the Fermi surface geometry in the antiferromagnetic metal FeRh

The confluence between high-energy physics and condensed matter has produced groundbreaking results via unexpected connections between the two traditionally disparate areas. In this work, we elucidate additional connectivity between high-energy and condensed matter physics by examining the interplay between spin-orbit interactions and local symmetry-breaking magnetic order in the magnetotransport of thin-film magnetic semimetal FeRh. We show that the change in sign of the normalized longitudinal magnetoresistance observed as a function of increasing in-plane magnetic field results from changes in the Fermi surface morphology. We demonstrate that the geometric distortions in the Fermi surface morphology are more clearly understood via the presence of pseudogravitational fields in the low-energy theory. The pseudogravitational connection provides additional insights into the origins of a ubiquitous phenomenon observed in many common magnetic materials and points to an alternative methodology for understanding phenomena in locally-ordered materials with strong spin-orbit interactions.

Automatic and Quantitative Measurement of Spectrometer Aberrations.

The performance of electron energy loss spectrometers can often be limited by their electron optical aberrations. Due to recent developments in high energy resolution and momentum-resolved electron energy loss spectroscopy (EELS), there is renewed interest in optimizing the performance of such spectrometers. For example, the "ω - q" mode of momentum-resolved EELS, which uses a small convergence angle and requires aligning diffraction spots with the slot aperture, presents a challenge in the realignments of the spectrometer required by the adjustment of the projection lenses. Automated and robust alignment can greatly benefit such a process. The first step toward this goal is automatic and quantitative measurement of spectrometer aberrations. We demonstrate the measurement of geometric aberrations and distortions in EELS within a monochromated scanning transmission electron microscope (STEM). To better understand the results, we present a wave mechanical simulation of the experiment. Using the measured aberration and distortion coefficients as inputs to the simulation, we find a good match between the simulation and experiment, verifying formulae used in the simulation. From verified simulations with known aberration coefficients, we can assess the accuracy of measurements. Understanding the errors and inaccuracies in the procedure can guide further progress in aberration measurement and correction for new spectrometer developments.

Evaluation of the impact of magnetic field homogeneity on image quality in magnetic resonance imaging: a baseline quantitative study at 1.5 T

BackgroundMagnetic resonance images can be affected in a number of ways by magnetic field inhomogeneity. The study aimed to optimize the main magnetic field homogeneity (MFH) by assessing how magnetic field inhomogeneity affects the signal-to-noise ratio (SNR) and geometric distortion of images acquired along the diameter of a spherical volume phantom from the isocenter of the MRI scanner.ResultsThe MFH ranged between 0.10 and 0.60 ppm. The best MFH and the maximum SNR were determined in the isocenter at 400 mm field of view with the application of shim. However, for all the phantom positions, geometrical distortion in images acquired at 200 mm field of view was generally better and worse at 400 mm field of view. MFH could be optimized to reduce geometrical distortion and increase SNR by increasing the receiver bandwidth and the number of excitations whiles complementing it with shimming during image acquisition. According to Chi-square independent test, there were no significant differences (p > 0.05) in the MFH, SNR, and geometrical distortion values. Compared to findings at higher field strengths, it was observed that MRI systems of higher field strengths (greater than 1.5 T) could offer superior magnetic field homogeneity and SNR without causing observable geometrical distortion.ConclusionsThe optimal field of view for the fast field echo (FFE) sequence required to maximize MFH, SNR, and reduce distortion during image acquisition may vary across MRI systems of different field strengths. To determine the appropriate field of view, the method and results of this study could serve as a guide for medical physicists as part of their routine quality assurance test procedures.