Geometric Image Distortion Research Articles

Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality

ObjectivesTo investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2DL) applied to female pelvic MRI, using standard T2-weighted TSE (T2S) as reference.MethodsIn total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2S and T2DL sequences in three planes were performed for each participant. Quantitative image evaluation was conducted by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image geometric distortion was evaluated by measuring the diameters in all three directions of the uterus and lesions. Qualitative image evaluation including overall image quality, artifacts, boundary sharpness of the uterine zonal layers, and lesion conspicuity were assessed by three radiologists using a 5-point Likert scale, with 5 indicating the best quality. Comparative analyses were conducted for the two sequences.ResultsT2DL resulted in a 62.7% timing reduction (1:54 min for T2DL and 5:06 min for T2S in axial, sagittal, and coronal imaging, respectively). Compared to T2S, T2DL had significantly higher SNR (p ≤ 0.001) and CNR (p ≤ 0.007), and without geometric distortion (p = 0.925–0.981). Inter-observer agreement regarding qualitative evaluation was excellent (Kendall’s W > 0.75). T2DL provided superior image quality (all p < 0.001), boundary sharpness of the uterine zonal layers (all p < 0.001), lesion conspicuity (p = 0.002, p < 0.001, and p = 0.021), and fewer artifacts (all p < 0.001) in sagittal, axial, and coronal imaging.ConclusionsCompared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.

LRFFNet: Large Receptive Field Feature Fusion Network for Semantic Segmentation of SAR Images in Building Areas

There are limited studies on the semantic segmentation of high-resolution synthetic aperture radar (SAR) images in building areas due to speckle noise and geometric distortion. For this challenge, we propose the large receptive field feature fusion network (LRFFNet), which contains a feature extractor, a cascade feature pyramid module (CFP), a large receptive field channel attention module (LFCA), and an auxiliary branch. SAR images only contain single-channel information and have a low signal-to-noise ratio. Using only one level of features extracted by the feature extractor will result in poor segmentation results. Therefore, we design the CFP module; it can integrate different levels of features through multi-path connection. Due to the problem of geometric distortion in SAR images, the structural and semantic information is not obvious. In order to pick out feature channels that are useful for segmentation, we design the LFCA module, which can reassign the weight of channels through the channel attention mechanism with a large receptive field to help the network focus on more effective channels. SAR images do not include color information, and the identification of ground object categories is prone to errors, so we design the auxiliary branch. The branch uses the full convolution structure to optimize training results and reduces the phenomenon of recognizing objects outside the building area as buildings. Compared with state-of-the-art (SOTA) methods, our proposed network achieves higher scores in evaluation indicators and shows excellent competitiveness.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition, the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks. This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named SuperFusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion, and semantic segmentation tasks demonstrate the superiority of our SuperFusion compared to the state-of-the-art alternatives. The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/SuperFusion.

Shimming toolbox: An open-source software toolbox forB0and B1 shimming in MRI.

Introduce Shimming Toolbox ( https://shimming-toolbox.org), an open-source software package for prototyping new methods and performing static, dynamic, and real-time B0 shimming as well as B1 shimming experiments. Shimming Toolbox features various field mapping techniques, manual and automatic masking for the brain and spinal cord, B0 and B1 shimming capabilities accessible through a user-friendly graphical user interface. Validation of Shimming Toolbox was demonstrated in three scenarios: (i) B0 dynamic shimming in the brain at 7T using custom AC/DC coils, (ii) B0 real-time shimming in the spinal cord at 3T, and (iii) B1 static shimming in the spinal cord at 7T. The B0 dynamic shimming of the brain at 7T took about 10min to perform. It showed a 47% reduction in the standard deviation of the B0 field, associated with noticeable improvements in geometric distortions in EPI images. Real-time dynamic xyz-shimming in the spinal cord took about 5min and showed a 30% reduction in the standard deviation of the signal distribution. B1 static shimming experiments in the spinal cord took about 10min to perform and showed a 40% reduction in the coefficient of variation of the B1 field. Shimming Toolbox provides an open-source platform where researchers can collaborate, prototype and conveniently test B0 and B1 shimming experiments. Future versions will include additional field map preprocessing techniques, optimization algorithms, and compatibility across multiple MRI manufacturers.

Minimizing magnetic resonance image geometric distortion at 7 Tesla for frameless presurgical planning using skin-adhered fiducials.

7T MRI offers significant benefits to spatial and contrast resolution compared to lower field strengths. This superior image quality can help better delineate targets in stereotactic neurosurgical procedures; however, the potential for increased geometric distortions at 7T has impaired its widespread use for these applications. Image geometric distortions can be due to distortions of B0 arising from tissue magnetic susceptibility effects or inherent field inhomogeneities, and nonlinearity of the magnetic field gradients. The purpose of this study was to investigate the use of 7T MRI for neurosurgical frameless stereotactic navigation procedures. Image geometric distortions at the skin surface in 7T images were minimized and compared to results from clinical 3T frameless imaging protocols. A 3D-printed grid phantom filled with oil was designed to perform a fine calibration of the 7T imaging gradients, and an oil-filled head phantom with internal targets was used to determine ground truth (from computed tomography [CT]) positioning errors. Three volunteers and the head phantom were imaged consecutively at 3T and 7T. Ten skin-adhesive fiducial markers were placed on each subject's exposed skin surface at standard clinical placement locations for frameless procedures. Imaging sequences included MPRAGE (three bandwidths at 7T: 400, 690, and 1020Hz/pixel, and one at 3T: 400Hz/pixel), T2 SPACE, and T2 SPACE FLAIR acquisitions. An additional GRE field map was acquired on both scanners using a multi-echo GRE sequence. Custom Matlab code was used to perform additional distortion correction of the images using the unwrapped field maps. Fiducial localization was performed with 3D Slicer, with absolute fiducial positioning errors determined in phantom experiments following rigid registration to the CT images. For human experiments, 3T and 7T images were registered and relative differences in fiducial locations were compared using two-tailed paired t-tests. Phantom measurements at 7T yielded gradient distance scaling errors of 1.1%, 2.2%, and 1.0% along the x-, y-, and z-axes, respectively. These system miscalibrations were traced back to phantom manufacturing deviations in the sphericity of the vendor's gradient calibration phantom. Correction factors along each gradient axis were applied, and afterward, geometric distortions of less than 1mm were obtained in the 7T MR head phantom images for the 1020Hz/pixel bandwidth MPRAGE sequence. For the human subjects, four fiducial locations were excluded from the analysis due to patient positioning differences. Differences between 3T and 7T MPRAGE with low/medium/high bandwidth were 2.2/2.6/2.3mm, respectively, before the correction, reducing to 1.6/1.3/1.0mm after the correction (p<0.001). T2 SPACE and T2 SPACE FLAIR yielded a similar pattern when the correction was applied, decreasing from 2.1 to 0.8mm, and 2.6 to 1.0mm, respectively. 7T MRI can be used to perform frameless presurgical planning with skin-adhesive fiducials. Geometric distortions can be reduced to a clinically relevant level (errors<∼1mm) with no significant susceptibility-related distortions, by using high receiver bandwidth, ensuring gradients are properly calibrated, and placing skin fiducials in areas where distortions from patient positioning are minimal.

The development of ultra-high field MRI guidance technology for neuronavigation.

Magnetic resonance imaging at 7T offers improved image spatial and contrast resolution for visualization of small brain nuclei targeted in neuromodulation. However, greater image geometric distortion and a lack of compatible instrumentation preclude implementation. In this report, the authors detail the development of a stereotactic image localizer and accompanying imaging sequences designed to mitigate geometric distortion, enabling accurate image registration and surgical planning of basal ganglia nuclei. Magnetization-prepared rapid acquisition with gradient echo (MPRAGE), fast gray matter acquisition T1 inversion recovery (FGATIR), T2-weighted, and T2*-weighted sequences were optimized for 7T in 9 human subjects to visualize basal ganglia nuclei, minimize image distortion, and maximize target contrast-to-noise and signal-to-noise ratios. Extracranial spatial distortions were mapped to develop a skull-contoured image localizer embedded with spherical silicone fiducials for improved MR image registration and target guidance. Surgical plan accuracy testing was initially performed in a custom-developed MRI phantom (n = 5 phantom studies) and finally in a human trial. MPRAGE and T2*-weighted sequences had the best measures among global measures of image quality (3.8/4, p < 0.0001; and 3.7/4, p = 0.0002, respectively). Among basal ganglia nuclei, FGATIR outperformed MPRAGE for globus pallidus externus (GPe) visualization (2.67/4 vs 1.78/4, p = 0.008), and FGATIR, T2-weighted imaging, and T2*-weighted imaging outperformed MPRAGE for substantia nigra visualization (1.44/4 vs 2.56/4, p = 0.04; vs 2.56/4, p = 0.04; vs 2.67/4, p = 0.003). Extracranial distortion was lower in the head's midregion compared with the base and apex ( 1.17-1.33 mm; MPRAGE and FGATIR, p < 0.0001; T2-weighted imaging, p > 0.05; and T2*-weighted imaging, p = 0.013). Fiducial placement on the localizer in low distortion areas improved image registration (fiducial registration error, 0.79-1.19 mm; p < 0.0001) and targeting accuracy (target registration error, 0.60-1.09 mm; p = 0.04). Custom surgical software and the refined image localizer enabled successful surgical planning in a human trial (fiducial registration error = 1.0 mm). A skull-contoured image localizer that accounts for image distortion is necessary to enable high-accuracy 7T imaging-guided targeting for surgical neuromodulation. These results may enable improved clinical efficacy for the treatment of neurological disease.

Calibration of a multiview display with a tilted parallax barrier for reduction of nonlinear extrinsic crosstalk.

Multiview displays, which are capable of displaying different view-images for viewers at different positions, can suffer from undesirable view-image mixing, called crosstalk. In this paper, we propose a method to minimize a class of crosstalk that occurs due to misalignment of flat panel displays and parallax barriers that constitute multiview displays. More specifically, our method is aimed at cases where flat panel displays and parallax barriers are non-parallel. First, we introduce our framework, which associates homography matrices to sets of pixels visible through parallax barriers. Then, we propose an iterative method for finding pixels that are visible for a view-point. By using a stripe test pattern, the homography matrix that corresponds to a set of visible pixels can be found by monitoring the deformation of the stripe test pattern. Last, we experimentally check the validity of the iterative method and demonstrate that the proposed method successfully corrects the geometric distortion of a 3D image.

Optimising diffusion-weighted imaging of the thyroid gland using dedicated surface coil

To assess the feasibility of applying field-of-view (FOV) optimised and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) in the thyroid gland by comparing its image quality with conventional DWI (C-DWI) qualitatively and quantitatively using a dedicated surface coil exclusively designed for the thyroid gland at 3 T magnetic resonance imaging (MRI). In this prospective study, 32 healthy volunteers who had undergone 3 T the thyroid gland MRI with FOCUS-DWI and C-DWI were enrolled. Two independent reviewers assessed the overall image quality, artefacts, sharpness, and geometric distortion based on a five-point Likert scale. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) were quantified for both sequences. Interobserver agreement, qualitative scores, and quantitative parameters were compared between two sequences. Agreement between the two readers was good for FOCUS-DWI (κ=0.714-0.778) and moderate to good for C-DWI (κ=0.525-0.672) in qualitative image quality assessment. Qualitatively, image quality (overall image quality, artefacts, sharpness, and geometric distortion) was significantly better in FOCUS-DWI than that in the C-DWI (all p<0.05); however, quantitatively, FOCUS-DWI had significantly lower SNRs (p<0.001) and CNRs (p=0.012) compared with C-DWI. The ADC value on FOCUS-DWI was significantly higher than that on C-DWI (p<0.001). FOCUS-DWI depicted the thyroid gland with significantly better image quality qualitatively and less ghost artefacts, but had significantly lower SNR and CNR quantitatively, compared with C-DWI, suggesting that both DWI sequences have advantages and could be chosen for different purposes.

A Method for the Interpretation of Sonar Data Recorded during Autonomous Underwater Vehicle Missions

Abstract Image acquisition from autonomous underwater vehicles (AUVs) is useful for mapping objects on the seabed. However, there are few studies on the interpretation of data collected with side-scan sonar during autonomous underwater vehicle missions. By recording the seabed with 3D multibeam sonar, a large number of survey points can be obtained. The collected data are processed using applications based on remote sensing image processing. The data collected during AUV missions (or other sonar carriers) needs to be pre-processed to reach the proper effectiveness level. This process includes corrections of signal amplification (Time Varying Gain, or TVG) and geometric distortions of sonar images (Slant Range Corrections). It should be mentioned that, when carrying out the interpretation process for structures on the sea floor, sonar users need to understand the process of visualising seabed projections and depressions, as well as the resolution limitations of the sonar sensors.

Classification of Transformed and Geometrically Distorted Images using Convolutional Neural Network

ConvolutionalNeural Network is a deep learning method that is used in many image-relatedapplications, such as image recognition and classification, it has achievedgreat performance in these fields, but it still suffers from some shortcomings.One of these shortcomings is not being able to be invariant to the input datadue to some image transformations like translation, rotation, scaling, andgeometric distortions such as skewness, perspective distortion and pincushiondistortion. This study presents anoptimized CNN which uses the Geometric Heat Flow (GHF) to improve theperformance of the CNN regarding the invariant limitation and classificationaccuracy. GHF is a partial differential equation that expresses how the heatwould diffuse on a surface concerning time in a specific location. GHF isinvariant to image transformations and geometric distortions if it was takenconcerning the object's arc length which will lead to an invariant CNN. Theexperiments show that GHF improves the performance of the CNN, and the proposedwork achieves an accuracy of 98.09% on the MNIST handwritten dataset, 92.58% onthe MNIST-Fashion dataset, and 86.09% on the CIFAR-10 dataset.

Development of a vendor neutral MRI distortion quality assurance workflow.

With the utilization of magnetic resonance (MR) imaging in radiotherapy increasing, routine quality assurance (QA) of these systems is necessary. The assessment of geometric distortion in images used for radiotherapy treatment planning needs to be quantified and monitored over time. This work presents an adaptable methodology for performing routine QA for systematic MRI geometric distortion. A software tool and compatible protocol (designed to work with any CT and MR compatible phantom on any scanner) were developed to quantify geometric distortion via deformable image registration. The MR image is deformed to the CT, generating a deformation field, which is sampled, quantifying geometric distortion as a function of distance from scanner isocenter. Configurability of the QA tool was tested, and results compared to those provided from commercial solutions. Registration accuracy was investigated by repeating the deformable registration step on the initial deformed MR image to define regions with residual distortions. The geometric distortion of four clinical systems was quantified using the customisable QA method presented. Maximum measured distortions varied from 2.2 to 19.4 mm (image parameter and sampling volume dependent). The workflow was successfully customized for different phantom configurations and volunteer imaging studies. Comparison to a vendor supplied solution showed good agreement in regions where the two procedures were sampling the same imaging volume. On a large field of view phantom across various scanners, the QA tool accurately quantified geometric distortions within 17–22 cm from scanner isocenter. Beyond these regions, the geometric integrity of images in clinical applications should be considered with a higher degree of uncertainty due to increased gradient nonlinearity and B0 inhomogeneity. This tool has been successfully integrated into routine QA of the MRI scanner utilized for radiotherapy within our department. It enables any low susceptibility MR‐CT compatible phantom to quantify the geometric distortion on any MRI scanner with a configurable, user friendly interface for ease of use and consistency in data collection and analysis.

The Impact of System-Related Magnetic Resonance Imaging Geometric Distortion in Stereotactic Radiotherapy: A Case Report

Magnetic resonance imaging (MRI) is now essential in stereotactic radiotherapy (SRT) planning for brain tumors because of its excellence in soft-tissue contrast and high spatial resolution. However, MRI distortion is sometimes difficult to recognize, and it may cause large misalignments in radiotherapy planning. In this case report, we will show how much difference in the dose distribution of SRT can be made by using MRI without distortion correction.

A calculation method of phenotypic traits of soybean pods based on image processing technology

As a homologous organ of the leaf, the pod is an essential factor in determining the yield and quality of the grain. The physiological and ecological characteristics of pods are a critical basis for soybean breeding and yield estimation. In order to avoid time-consuming and laborious problems in the traditional manual measurement process, a fast calculation method of pod's phenotypic traits was proposed based on image processing technology. First, two varieties (Heihe 49 and Suinong 26) were taken as the research objects. The self-developed image acquisition platform was used to obtain the color images of soybean pods at different growth stages. Second, the geometric distortion of the raw image was registered by using the linear mapping equation. In addition, the edge of the pod was extracted by the Gaussian filter function and Canny detection operator. Finally, the length, width, and area of pods were calculated based on the minimum circumscribed rectangle method, the maximum inscribed circle method, and the pixel count of pod's contour area combined with the template calibration method. The shape and the number of seeds were calculated based on the interval duty cycle method, the interval duty cycle combined with the arc length of pod's convex edge. The results showed that the accuracy between the calculated values and the standard values of pod length, pod width, pod area, pod shape, and the number of seeds was 97.8%, 95.1%, 97.1%, 93.2%, and 85%, respectively. Thus, the proposed method can provide not only a rapid detection for quantitative indexes of soybean seed test and yield estimation, but also a theoretical basis and technical knowledge for soybean breeding and scientific cultivation.

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

An improved synthetic aperture radar‐scale invariant feature transform algorithm for interferometric imaging radar altimeter image registration

Interferometric imaging radar altimeter (InIRA) integrating synthetic aperture radar (SAR) and synthetic aperture radar interferometry (InSAR) is a new type of radar altimeter. InIRA will be widely adopted in marine scientific research and surface water topography measurements. Owing to the geometric distortion of InIRA images, there are difficulties in feature point extraction and matching, making InIRA registration challenging. Here, an improved SAR-scale invariant feature transform (SIFT, SAR-SIFT) algorithm is proposed to address these difficulties. First, a modified Harris non-linear scale space is constructed using the Harris function and non-linear scale space, and extracted the feature points according to the SAR-SIFT algorithm. Second, the orbital parameters of InIRA are used to calculate the geographic coordinates of each feature point by Hermit interpolation. Subsequently, a geographic-coordinate-based method is proposed to match the feature points. The results show that the proposed algorithm gained more correct matches and improved the registration accuracy by 13% and 16.7% compared with the SIFT and SAR-SIFT algorithms, respectively, and was 18% more time efficient than the SAR-SIFT algorithm. The experiments demonstrate that the proposed algorithm outperforms the SAR-SIFT algorithm for InIRA image registration in terms of accuracy and efficiency.

People detection with omnidirectional cameras using a spatial grid of deep learning foveatic classifiers

A novel deep-learning people detection algorithm using omnidirectional cameras is presented, which only requires point-based annotations, unlike most of the prominent works that require bounding box annotations. Thus, the effort of manually annotating the needed training databases is significantly reduced, allowing a faster system deployment. The algorithm is based on a novel deep neural network architecture that implements the concept of Grid of Spatial-Aware Classifiers, but allowing end-to-end training that improves the performance of the whole system. The designed algorithm satisfactorily handles the severe geometric distortions of the omnidirectional images, which typically degrades the performance of state-of-the-art detectors, without requiring any camera calibration. The algorithm has been evaluated in well-known omnidirectional image databases (PIROPO, BOMNI, and MW-18Mar) and compared with several works of the state of the art.

＜Editors' Choice＞ Evaluation of system-related magnetic resonance imaging geometric distortion in radiation therapy treatment planning: two approaches and effectiveness of three-dimensional distortion correction.

ABSTRACTWe propose two methods to evaluate system-related distortion in magnetic resonance imaging (MRI) in radiation therapy treatment planning (RTP) and demonstrate the importance of three-dimensional (3D) distortion correction (DC) by quantitatively measuring the distortion magnitude. First, a small pin phantom was scanned at multiple positions using an external laser guide for accurate phantom placement and combined into one image encompassing a large area. Direct plane images were used for evaluating in-plane distortion and multiplanar reconstruction images for through-plane distortion with no DC, two-dimensional (2D) DC, and 3D DC. Second, a large grid sheet was scanned as the direct plane of the phantom placement. The distortion magnitude was determined by measuring the displacement between the MRI and reference coordinates. The measured distortions were compared between in- and through-plane when applying DC and between the two methods. The small pin phantom method can be used to evaluate a wide range of distortions, whereas data from the entire plane can be obtained with a single scan using the grid sheet without a laser guide. The mean distortion magnitudes differed between the methods. Furthermore, the 3D DC reduced in- and through-plane distortions. In conclusion, the small pin phantom method can be used to evaluate a wide range of distortions by creating a combined image, whereas the grid sheet method is simpler, accurate, repeatable, and does not require a special-order phantom or laser guide. As 3D DC reduces both in- and through-plane distortions, it can be used to improve RTP quality.

Brain diffusion MRI with multiplexed sensitivity encoding for reduced distortion in a pediatric patient population

BackgroundDiffusion-weighted imaging (DWI) is a valuable tool for routine imaging of the pediatric brain. However, the commonly used single-shot (ss) echo-planar imaging (EPI) DWI sequence is prone to geometric distortions and T2*-blurring. This study aimed to investigate in a pediatric population the benefits of using multiplexed sensitivity-encoding (MUSE) without and with reversed polarity gradients (RPG) instead. MethodsThis retrospective study compared image quality, geometric distortions, and diffusion values between three different approaches for DWI (ssEPI, MUSE, and RPG-MUSE) in 14 patients (median age = 4 (0.6–15) years, 11 males). Distortion levels were quantified and compared in two brain regions, i.e., the brain stem and the temporal lobes, using the Dice Coefficient and the Hausdorff Distance, with T2-weighted images as reference. Expected geometrical distortion was further evaluated by comparing the effective echo spacing between the DWI sequences. Apparent diffusion coefficient (ADC) values were determined in the genu of the corpus callosum and the optic nerves. Two raters graded overall image quality and image distortions on a Likert scale. ResultsDistortion levels assessed with Dice coefficient and Hausdorff distance were significantly lower for MUSE (p < 0.05) and RPG-MUSE (p < 0.01) compared to ssEPI. No significant difference in ADC values was observed between methods. The RPG-MUSE method was graded by one rater as significantly higher in overall image quality than ssEPI (p < 0.05) and by both raters as significantly lower in levels of image distortions than both MUSE (p < 0.05) and ssEPI (p < 0.05). These results were in agreement with the reduced effective echo spacing was that was attained with MUSE and RPG-MUSE. ConclusionFor imaging of the pediatric brain, MUSE and even more so RPG-MUSE offers both improved geometric fidelity and image quality compared to ssEPI.

Geometric Correction Analysis of Highly Distortion of Near Equatorial Satellite Images Using Remote Sensing and Digital Image Processing Techniques

The Near-equatorial orbit (NEqO) satellite represents a new generation of optical satellite images characterized by nonlinear distortion when captured. Conventional modeling techniques are insufficient to overcome the geometric distortion in these satellite images. This study proposes a new methodology for overcoming the geometric distortion of the NEqO images. The data used are obtained from RazakSAT and SPOT-5 satellite images in Malaysia. The method starts with applying the RI-SIFT algorithm to extract control points (CPs) automatically. These CPs are used to solve for the transformation parameters of the geometric correction model by applying spline transformations. The result is verified through statistical comparison: 1) geometric correction on the RazakSAT image is performed with Spot satellite image with using first-order polynomial trans-formation. 2) Then calculate the root mean square error (RMSE). 3) Compare the calculated RMSE with that obtained from the first step with that of the proposed method. The RMSE value of the geometric corrections using the proposed method was 7.08 × 10−9 m. The proposed method provides promising results.

COMPARISON OF 3D QUANTITATIVE OSTEOARTHRITIS IMAGING BIOMARKERS: A STUDY USING PAIRED CT AND MR IMAGES FROM THE IMI-APPROACH STUDY

MRI bone surface area and femoral bone shape (B-score) measures have been employed as quantitative endpoints in DMOAD clinical trials. The B-score (typical range -2 to +7) has been shown to be an objective, automated assessment of OA status, with clinical risk defined for current and future pain, functional limitation and total knee replacement. Computerized Tomography (CT) imaging is more commonly used for 3D visualization of bony anatomy due to its high bone-soft tissue contrast. Also, CT images are not subject to the geometric distortion in MR images due to field inhomogeneities, a problem addressed in DMOAD clinical trials by positioning the knee near the magnet isocenter. Measurement of 3D joint space width (3DJSW) has been previously reported using CT images but is possible from MR images. To test the robustness of the B-score, bone area and 3DJSW imaging endpoints to the choice of imaging modality, we compared the results of automated analysis of CT and MR image pairs acquired at baseline and 24-month timepoints. We used image data from the IMI-APPROACH exploratory, 2-year prospective cohort study. High resolution CT images were acquired at baseline and 24-months. These were matched with 3D T1w MR images with fat-sat or WE sequences. CT and MR images were acquired within a mean of 12 days of each other. Additional test-retest MR images with repositioning were available from 37 subjects. Kellgren-Lawrence Grades (KLG) were read centrally from screening weight-bearing x-rays. Femur and tibia bones were automatically segmented with sub-voxel accuracy from CT or MR images using active appearance models, a machine learning model trained to search images (Imorphics, Manchester, UK). Bone area measurement was produced for MF.tAB, LF.tAB, MT.tAB and LT.tAB regions. Average 3DJSW was measured in central regions of the medial or lateral tibial plateaus, normal to the tibial surface. Linear regression was used to test for correlation between measures. Limits of agreement and systematic bias were tested using Bland-Altman analysis. Smallest detectable difference (SDD) for MR B-score was computed from test-retest image pairs using Bland-Altman analysis. Images acquired from the same subject at both baseline and 24-month timepoints were treated as independent. Baseline and 24-month CT-MR pairs of the same knee were available from 231 and 203 subjects respectively. 338 were female knees (78%). KLG was KLG0 (19%); KLG1 (31%); KLG2 (30%); KLG3 (16%); KLG4 (3%) with 5 knees ungraded (1%). SDD of the B-score measure was 0.227. MRI B-score measures were highly correlated with CT (R2 = 0.97) and showed good agreement with a relatively small bias of 0.1. All 4 area measures were highly correlated (R2 = 0.96-0.97) and showed negative bias (MR smaller). Both 3DJSW measures showed a positive bias for MR (medial bias = 1.04mm, R2 = 0.58; lateral bias = 0.42mm, R2 = 0.47). The results suggest that B-score may be measured reliably using either modality. However, it is likely that the bone surface identified using MR and CT will be at slightly different positions within the bone/cartilage boundary. A consistent negative bias suggests the MR bone boundary is inside the CT boundary. This is consistent with the positive bias in favor of MR for 3DJSW. However, the MR-CT difference for medial 3DJSW is larger than for lateral 3DJSW, possibly due to a difference in knee pose during acquisition (fully extended for CT, flexed for MR) – the tibia is more externally rotated with respect to the femur in the fully extended knee. AB and MB are employees of Imorphics. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (APPROACH grant n° 115770). CORRESPONDENCE ADDRESS: alan.brett@stryker.com