Geometric Image Distortion Research Articles

Selection of Singular Points Stable to Blurring and Geometric Distortions of Images

Selection of Singular Points Stable to Blurring and Geometric Distortions of Images

Turbo Gradient and Spin Echo PROPELLER-Diffusion Weighted Imaging for Orbital Tumors: A Comparative Study With Readout-Segmented Echo-Planar Imaging.

Purpose: To qualitatively and quantitatively compare the image quality and diagnostic performance of turbo gradient and spin echo PROPELLER diffusion-weighted imaging (TGSE-PROPELLER-DWI) vs. readout-segmented echo-planar imaging (rs-EPI) in the evaluation of orbital tumors.Materials and Methods: A total of 43 patients with suspected orbital tumors were enrolled to perform the two DWIs with comparable spatial resolution on 3T. The overall image qualities, geometric distortions, susceptibility artifacts, and lesion conspicuities were scored by using a four-point scale (1, poor; 4, excellent). Quantitative measurements, including contrast-to-noise ratios (CNRs), apparent diffusion coefficients (ADCs), geometric distortion rates (GDRs), and lesion sizes, were calculated and compared. The two ADCs for differentiating malignant from benign orbital tumors were evaluated. Wilcoxon signed-rank test, Kappa statistic, and receiver operating characteristics (ROC) curves were used.Results: TGSE-PROPELLER-DWI performed superior in all subjective scores and quantitative GDR evaluation than rs-EPI (p < 0.001), and excellent interobserver agreement was obtained for Kappa value ranging from 0.876 to 1.000. ADClesion of TGSE-PROPELLER-DWI was significantly higher than those of rs-EPI (p < 0.001). Mean ADC of malignant tumors was significantly lower than that of benign tumors both in two DWIs. However, the AUC for differentiating malignant and benign tumors showed no significant difference in the two DWIs (0.860 vs. 0.854, p = 0.7448). Sensitivity and specificity could achieve 92.86% and 72.73% for TGSE-PROPELLER-DWI with a cutoff value of 1.23 × 10–3 mm2/s, and 85.71% and 81.82% for rs-EPI with a cutoff value of 0.99 × 10–3 mm2/s.Conclusion: Compared with rs-EPI, TGSE-PROPELLER-DWI showed minimized geometric distortion and susceptibility artifacts significantly improved the image quality for orbital tumors and achieved comparable diagnostic performance in differentiating malignant and benign orbital tumors.

Proposal of an algorithm to evaluate geometric distortion and artifact spreading in digital breast tomosynthesis.

According to the European Reference Organization for Quality Assurance Breast Screening and European Diagnostic Services, the spatial accuracy of reconstructed images and reconstruction artifacts must be evaluated in digital breast tomosynthesis (DBT) quality control procedures. To propose a computational algorithm to evaluate the geometric distortion and artifact spreading (GDAS) in DBT images. The proposed algorithm analyzed tomosynthesis images of a phantom that contains aluminum spheres (1 mm in diameter) arranged in a rectangular matrix spaced 5 cm apart that was inserted in 5-mm-thick polymethylmethacrylate (PMMA). The obtained results were compared with the values provided by the algorithm developed by the National Coordinating Center for the Physics of Mammography (NCCPM). In the comparison, the results depended on the dimensions of the region of interest (ROI). This dependence proves the benefit of the proposed algorithm because it allows the user to select the ROI. The computational algorithm proved to be useful for the evaluation of GDAS in DBT images, in the same way as the reference algorithm (NCCPM), as well as allowing the selection of the ROI dimensions that best suit the spreading of the artifact in the analyzed images.

Design and construction of a customizable phantom for the characterization of the three-dimensional magnetic resonance imaging geometric distortion.

One of the main challenges to using magnetic resonance imaging (MRI) in radiotherapy is the existence of system‐related geometric inaccuracies caused mainly by the inhomogeneity in the main magnetic field and the nonlinearities of the gradient coils.Several physical phantoms, with fixed configuration, have been developed and commercialized for the assessment of the MRI geometric distortion. In this study, we propose a new design of a customizable phantom that can fit any type of radio frequency (RF) coil. It is composed of 3D printed plastic blocks containing holes that can hold glass tubes which can be filled with any liquid. The blocks can be assembled to construct phantoms with any dimension.The feasibility of this design has been demonstrated by assembling four phantoms with high robustness allowing the assessment of the geometric distortion for the GE split head coil, the head and neck array coil, the anterior array coil, and the body coil. Phantom reproducibility was evaluated by analyzing the geometric distortion on CT acquisition of five independent assemblages of the phantom.This solution meets all expectations in terms of having a robust, lightweight, modular, and practical tool for measuring distortion in three dimensions. Mean error in the position of the tubes was less than 0.2 mm. For the geometric distortion, our results showed that for all typical MRI sequences used for radiotherapy, the mean geometric distortion was less than 1 mm and less than 2.5 mm over radial distances of 150 mm and 250 mm, respectively.These tools will be part of a quality assurance program aimed at monitoring the image quality of MRI scanners used to guide radiation therapy.

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection.

In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

Imaging characteristics and image restoration method for large field circular scanning system

Large field circular scanning system obtains remote sensing images by rotating scanning, which is one of the technical ways to effectively expand the field of view of geostationary orbit optical remote sensing imaging system. However, due to its special rotation scanning imaging mechanism, the on-orbit imaging process of this system is affected by the coupling of detector rotation sampling, satellite platform vibration, optical system aberration and other factors, so the image degradation characteristics are very complex. In this paper, the on-orbit imaging characteristics of large field circular scanning system are first analyzed to provide a theoretical basis for the subsequent research of image restoration method. Secondly, aiming at the problems of annular geometric distortion and spatial blur of the system image, the image restoration method is proposed based on ring block and regularization methodology. Experimental results show that under the condition of detector rotation speed ω <0.06 rad/s, the proposed method exhibited a satisfactory processing performance.

First clinical experience of correcting phantom-based image distortion related to gantry position on a 0.35T MR-Linac.

MR‐guided radiotherapy requires strong imaging spatial integrity to deliver high quality plans and provide accurate dose calculation. The MRI system, however, can be compromised by the integrated linear accelerator (Linac), resulting in inaccurate imaging isocenter position and geometric distortion. Dependence on gantry position further complicates the correction of distortions. This work presents a new clinical application of a commercial phantom and software system that quantifies isocenter alignment and geometric distortion, as well as providing a deformation vector field (DVF). A large distortion phantom and a smaller grid phantom were imaged at multiple gantry angles from 0 to 330° on a 0.35 T integrated MR‐Linac. The software package was used to assess geometric distortion and generate DVFs to correct distortions within the phantom volume. The DVFs were applied to the grid phantom with resampling software then evaluated using structural similarity index measure (SSIM). Scans were also performed with a ferromagnetic clip near the phantom to investigate the correction of more severe artifacts. The mean magnitude isocenter shift was 0.67 mm, ranging from 0.25 to 1.04 mm across all angles. The DVF had a mean component value of 0.27 ± 0.02, 0.24 ± 0.01, and 0.19 ± 0.01 mm in the right‐left (RL), anterior‐posterior (AP), and superior‐inferior (SI) directions. The ferromagnetic clip increased isocenter position error from 1.98 mm to 2.20 mm and increased mean DVF component values in the RL and AP directions. The resampled grid phantom had an increased SSIM for all gantry angles compared to original images, increasing from 0.26 ± 0.001 to 0.70 ± 0.004. Through this clinical assessment, we were able to correct geometric distortion and isocenter shift related to gantry position on a 0.35 T MR‐Linac using the distortion phantom and software package. This provides encouragement that it could be used for quality assurance and clinically to correct systematic distortion caused by imaging at different gantry angles.

BLADE turbo gradient- and spin-echo in the assessment of sinonasal lesions: a comprehensive comparison of image quality in readout-segmented echo-planar imaging.

A two-dimensional turbo gradient-echo and spin-echo diffusion-weighted pulse sequence with a non-Cartesian BLADE trajectory (TGSE BLADE) can eliminate image artifacts and distortion with clinically acceptable scan times. This process has the potential to overcome the shortcomings of current diffusion-weighted imaging (DWI) techniques, especially in the sinonasal region. To investigate the feasibility of TGSE BLADE in the assessment of sinonasal lesions and compare the quality of TGSE BLADE with RESOLVE images both qualitatively and quantitatively. A total of 36 patients with sinonasal lesions were included in this prospective study. DW images acquired using TGSE BLADE and RESOLVE were performed with the same acquisition time. Two independent observers evaluated the qualitative parameters (overall image quality, lesion visibility, and geometric distortion) and quantitative parameters (geometric distortion ratio [GDR], signal-to-noise ratio [SNR], contrast, contrast-to-noise ratio [CNR], and apparent diffusion coefficient [ADC] value) of the two sequences. Qualitative assessment revealed that TGSE BLADE exhibited higher overall image quality (P < 0.001) and lesion visibility (P < 0.001) and less geometric distortion (P < 0.001) than RESOLVE. Quantitative assessment showed that TGSE BLADE images exhibited higher contrast (P < 0.001) and CNR (P < 0.001) and lower GDR (P < 0.05) and SNR (P < 0.001) than RESOLVE images. The ADC value of TGSE BLADE was significantly lower than that of RESOLVE (P < 0.05). TGSE BLADE can reduce susceptibility artifacts and geometric distortion more than RESOLVE and appears to be a promising diffusion imaging sequence for the assessment of sinonasal lesions.

Research on Remote Sensing Image Matching with Special Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

A Universal Automatic Bottom Tracking Method of Side Scan Sonar Data Based on Semantic Segmentation

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors.

Accurate mapping of Brazil nut trees (Bertholletia excelsa) in Amazonian forests using WorldView-3 satellite images and convolutional neural networks

The commercialization of Brazil nuts, seeds of Bertholletia excelsa Bonpl. (Lecythidaceae), represents one of the main income-generation activities for local and indigenous people from the Brazilian Amazon region. Because trees of B. excelsa grow and bear fruit almost exclusively in natural forests, information on their spatial distribution is crucial for nut harvest planning. However, this information is difficult to obtain with traditional approaches such as ground-based surveys. Here, we show the potential of convolutional neural networks (CNNs) and WorldView-3 satellite images (pixel size = 30 cm) to map individual tree crowns (ITCs) and groves of B. excelsa in Amazonian forests. First, we manually outlined B. excelsa ITCs in the WorldView-3 images using field-acquired geolocation information. Then, based on ITC boundaries, we sequentially extracted image patches and selected 80% of them for training and 20% for testing. We trained the DeepLabv3+ architecture with three backbones: ResNet-18, ResNet-50, and MobileNetV2. The average producer's accuracy was 93.87 ± 0.85%, 93.89 ± 1.6% and 93.47 ± 3.6% for ResNet-18, ResNet-50 and MobileNetV2, respectively. We then developed a new random patch extraction training strategy and assessed how a reduction in the percentage of training patches impacted the classification accuracy. To illustrate the robustness of the new training strategy, similar F1-scores were achieved whether 80% or 10% of the total number of patches were used to train the CNN model. By analyzing the feature maps derived from ResNet-18, we found that the shadow of emergent B. excelsa trees are important for their discrimination. Geometric distortions in the WorldView-3 images resulting from extreme off-nadir viewing angles compromise the presence of shadows, thus potentially hampering B. excelsa detection. Our results show that ITCs and groves of B. excelsa can be mapped by integrating CNNs and very-high-resolution (VHR) satellite images, paving the way for monitoring this important tree species in large tracts of Amazonian forests.

A Tree Ring Measurement Method Based on Error Correction in Digital Image of Stem Analysis Disk

Stem analysis is an essential aspect in forestry investigation and forest management, as it is a primary method to study the growth law of trees. Stem analysis requires measuring the width and number of tree rings to ensure the accurate measurement, expand applicable tree species, and reduce operation cost. This study explores the use of Open Source Computer Vision Library (Open CV) to measure the ring radius of analytic wood disk digital images, and establish a regression equation of ring radius based on image geometric distortion correction. Here, a digital camera was used to photograph the stem disks’ tree rings to obtain digital images. The images were preprocessed with Open CV to measure the disk’s annual ring radius. The error correction model based on the least-square polynomial fitting method was established for digital image geometric distortion correction. Finally, a regression equation for tree ring radius based on the error correction model was established. Through the above steps, click the intersection point between the radius line and each ring to get the pixel distance from the ring to the pith, then the size of ring radius can be calculated by the regression equation of ring radius. The study’s method was used to measure the digital image of the Chinese fir stem disk and compare it with the actual value. The results showed that the maximum error of this method was 0.15 cm, the average error was 0.04 cm, and the average detection accuracy reached 99.34%, which met the requirements for measuring the tree ring radius by stem disk analysis. This method is simple, accurate, and suitable for coniferous and broad-leaved species, which allows researchers to analyze tree ring radius measurement, and is of great significance for analyzing the tree growth process.

Chromatic component in the modulation transfer function of fully depleted charge-coupled devices

The effect of spectrally dependent geometric distortions of images in high-aperture optical systems equipped with thick high-rho charge-coupled devices is studied. Aberration is observed in the near-infrared spectral region (0.9–1.1 µm) due to the optical–geometric properties of the matrices. In this paper we calculate the point spread function and present a model. The effect of aberration is described in terms of the signal processing theory. The effect of antireflective coatings is estimated.

Research of automatic recognition of car license plates based on deep learning for convergence traffic control system

The technology that can recognize the license plates of vehicles in real time and manage them automatically is a key element of building an intelligent transportation system. License plate recognition is the most important technique in vehicle image processing used to identify a vehicle. Object recognition using a camera is greatly influenced by environmental factors in which the camera is installed. When the vehicle image is acquired, the image is distorted due to the tilting of the license plate, reflection of light, lighting effects, rainy weather, and nighttime, so that it is difficult to accurately recognize the license plate. In addition, when the geometric distortion of the license plate image or the degradation of the image quality is intensified, it may be more difficult to automatically recognize the license plate image. Therefore, in this paper, we propose a deep learning–based vehicles’ license plate recognition method to detect license plate and recognize characters accurately in complex and diverse environments. As a deep learning model, the YOLO model can be used to detect robust license plates in a variety of environments and to recognize characters quickly and accurately. It can also be seen that the license plate accurately recognizes the license plate with geometric distortion.

Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images.

image can be represented with different formats, such as the equirectangular projection (ERP) image, viewport images or spherical image, for its different processing procedures and applications. Accordingly, the 360-degree image quality assessment (360-IQA) can be performed on these different formats. However, the performance of 360-IQA with the ERP image is not equivalent with those with the viewport images or spherical image due to the over-sampling and the resulted obvious geometric distortion of ERP image. This imbalance problem brings challenge to ERP image based applications, such as 360-degree image/video compression and assessment. In this paper, we propose a new blind 360-IQA framework to handle this imbalance problem. In the proposed framework, cubemap projection (CMP) with six inter-related faces is used to realize the omnidirectional viewing of 360-degree image. A multi-distortions visual attention quality dataset for 360-degree images is firstly established as the benchmark to analyze the performance of objective 360-IQA methods. Then, the perception-driven blind 360-IQA framework is proposed based on six cubemap faces of CMP for 360-degree image, in which human attention behavior is taken into account to improve the effectiveness of the proposed framework. The cubemap quality feature subset of CMP image is first obtained, and additionally, attention feature matrices and subsets are also calculated to describe the human visual behavior. Experimental results show that the proposed framework achieves superior performances compared with state-of-the-art IQA methods, and the cross dataset validation also verifies the effectiveness of the proposed framework. In addition, the proposed framework can also be combined with new quality feature extraction method to further improve the performance of 360-IQA. All of these demonstrate that the proposed framework is effective in 360-IQA and has a good potential for future applications.

A phantom based laser marking workflow to visually assess geometric image distortion in magnetic resonance guided radiotherapy.

Magnetic resonance (MR)-only workflows require quality assurance due to potential dosimetric impacts of using geometry distorted MR images in radiotherapy planning. MR-visible silicone-based fiducials were arranged in regular 3D structures to cover extended imaging volumes. The scanner’s patient marking workflow with a 2-axes movable laser bridge allowed to visually check geometric distortions of each MR reconstructed fiducial against its true position in 3D space. A measurement resolution and uncertainty of the order of 0.5 mm in sagittal and coronal, and 1 mm in transversal direction was found. The proposed workflow required 1 min of evaluation time per fiducial position, and a 9 min 3D MR volume acquisition.

FPGA-based real-time atmospheric turbulence image restoration algorithm and its implementation

Due to the influence of atmospheric turbulence, the images obtained by the remote optical imaging equipment will appear serious degradation phenomena, such as geometric distortion, motion blur and defocus blur, etc. At present, the mainstream turbulence restoration algorithms usually rely on the non-rigid registration, reconstruction or finding rare lucky regions from long-term video sequences. These methods require huge calculated amount or complete video data in advance, which cannot meet the real-time requirements of actual application scenarios. Therefore, a real-time turbulence restoration algorithm that could be implemented in field programmable gate array (FPGA) was proposed. This method used the randomness of atmospheric turbulence to filter the image in the time domain through the continuous frames and solved the problem of image geometric distortion. Then, the Wiener filter in the frequency domain was converted into a convolution in the spatial domain, which was easier to implement, and solved the problem of image blur. The experimental results show that the proposed algorithm not only meets the real-time requirements, but also effectively realizes the restoration of turbulence images.

Adopting Image Integration Techniques to Simulate Satellite Images

Mathematical integration techniques rely on mathematical relationships such as addition, subtraction, division, and subtraction to merge images with different resolutions to achieve the best effect of the merger. In this study, a simulation is adopted to correct the geometric and radiometric distortion of satellite images based on mathematical integration techniques, including Brovey Transform (BT), Color Normalization Transform (CNT), and Multiplicative Model (MM). Also, interpolation methods, namely the nearest neighborhood, Bi-linear, and Bi-cubic were adapted to the images captured by an optical camera. The evaluation of images resulting from the integration process was performed using several types of measures; the first type depends on the determination of quality in the regions of the edges using a contrast measure as well as the number of edges and threshold. The second type is the global one that is based on the parameters of the image region, including the Mean (µ), Standard Deviation (SD), and Signal to Noise Ratio (SNR). The parameters also included the Amount of Information Added (AIA) to the original image, such as those for the total (AIAt) , edges (AIAe), and homogenous (AIAh) regions. The results showed the efficiency of the integration process in the image fusion with different resolutions in one image integrated resolution. The quality measures used were also capable in evaluating the most efficient techniques and determining the accurate information of the resulting image.

Inter- and intra-scanner variations in four magnetic resonance imaging image quality parameters.

Purpose: In addition to less frequent and more comprehensive tests, quality assurance (QA) protocol for a magnetic resonance imaging (MRI) scanner may include cursory daily or weekly phantom checks to verify equipment constancy. With an automatic image analysis workflow, the daily QA images can be further used to study scanner baseline performance and both long- and short-term variations in image quality. With known baselines and variation profiles, automatic error detection can be employed. Approach: Four image quality parameters were followed for 17 MRI scanners over six months: signal-to-noise ratio (SNR), image intensity uniformity, ghosting artifact, and geometrical distortions. Baselines and normal variations were determined. An automatic detection of abnormal QA images was compared with image deviations visually detected by human observers. Results: There were significant inter-scanner differences in the QA parameters. In some cases, the results exceeded commonly accepted tolerances. Scanner field strengths, or a unit being stationary versus mobile, did not have a clear relationship with the QA results. Conclusions: The variations and baseline levels of image QA parameters can differ significantly between MRI scanners. Scanner specific error thresholds based on parameter means and standard deviations are a viable option for detecting abnormal QA images.

Evaluation of cine imaging during multileaf collimator and gantry motion for real-time magnetic resonance guided radiation therapy.

PurposeReal‐time magnetic resonance guided radiation therapy (MRgRT) uses 2D cine imaging for target tracking. This work evaluates the percent image uniformity (PIU) and spatial integrity of cine images in the presence of multileaf collimator (MLC) and gantry motion in order to simulate sliding window and volumetric modulated arc therapy (VMAT) conditions.MethodsPercent image uniformity and spatial integrity of cine images were measured (1) during MLC motion, (2) as a function of static gantry position, and (3) during gantry rotation. PIU was calculated according to the ACR MRI Quality Control Manual. Spatial integrity was evaluated by measuring the geometric distortion of 16 measured marker positions (10 cm or 15.225 cm from isocenter).ResultsThe PIU of cine images did not vary by more than 1% from static linac conditions during MLC motion and did not vary by more than 3% during gantry rotation. Banding artifacts were present during gantry rotation. The geometric distortion in the cine images was less than 0.88 mm for all points measured throughout MLC motion. For all static gantry positions, the geometric distortion was less than 0.88 mm at 10 cm from isocenter and less than 1.4 mm at 15.225 cm from isocenter. During gantry rotation, the geometric distortion remained less than 0.92 mm at 10 cm from isocenter and less than 1.60 mm at 15.225 cm from isocenter.ConclusionDuring MLC motion, cine images maintained adequate PIU, and the geometric distortion of points within 15.225 cm from isocenter was less than the 1 mm threshold necessary for real‐time target tracking and gating. During gantry rotation, PIU was negatively affected by banding artifacts, and spatial integrity was only maintained within 10 cm from isocenter. Future work should investigate the effects imaging artifacts have on real‐time target tracking during MRgRT.