Geometric Distortion Research Articles

Attentive Deep Image Quality Assessment for Omnidirectional Stitching

Omnidirectional images or videos are commonly generated via the stitching of multiple images or videos, and the quality of omnidirectional stitching strongly influences the quality of experience (QoE) of the generated scenes. Although there were many studies research the omnidirectional image quality assessment (IQA), the evaluation of the omnidirectional stitching quality has not been sufficiently explored. In this paper, we focus on the IQA for the omnidirectional stitching of dual fisheye images. We first establish an omnidirectional stitching image quality assessment (OSIQA) database, which includes 300 distorted images and 300 corresponding reference images generated from 12 raw scenes. The database contains a variety of distortion types caused by omnidirectional stitching, including color distortion, geometric distortion, blur distortion, and ghosting distortion, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> A subjective quality assessment study is conducted on the database and human opinion scores are collected for the distorted omnidirectional images. We then devise a deep learning based objective IQA metric termed Attentive Multi-channel IQA Net. In particular, we extend hyper-ResNet by developing a subnetwork for spatial attention and propose a spatial regularization item. Experimental results show that our proposed FR and NR models achieve the best performance compared with the state-of-the-art FR and NR IQA metrics on the OSIQA database. The OSIQA database as well as the proposed Attentive Multi-channel IQA Net will be released to facilitate future research.

BlendMR: A Computational Method to Create Ambient Mixed Reality Interfaces

Mixed Reality (MR) systems display content freely in space, and present nearly arbitrary amounts of information, enabling ubiquitous access to digital information. This approach, however, introduces clutter and distraction if too much virtual content is shown. We present BlendMR, an optimization-based MR system that blends virtual content onto the physical objects in users’ environments to serve as ambient information displays. Our approach takes existing 2D applications and meshes of physical objects as input. It analyses the geometry of the physical objects and identifies regions that are suitable hosts for virtual elements. Using a novel integer programming formulation, our approach then optimally maps selected contents of the 2D applications onto the object, optimizing for factors such as importance and hierarchy of information, viewing angle, and geometric distortion. We evaluate BlendMR by comparing it to a 2D window baseline. Study results show that BlendMR decreases clutter and distraction, and is preferred by users. We demonstrate the applicability of BlendMR in a series of results and usage scenarios.

PROTECTION OF POLIGRAPHIC PRODUCTS USING DIGITAL WATERMARKS

The article proposes algorithms for steganographic embedding and extraction of digital watermarks (DWM)&#x0D; embedded in the digital original of printed products in order to protect copyrights to them. The built-in digital&#x0D; image is resistant to distortions introduced into images during printing and scanning and is not visually noticeable.&#x0D; The resistance of the extraction algorithm to geometric distortions is achieved through the use of an algorithm&#x0D; for restoring the original geometry of the image by comparing the special points of the distorted copy of the container&#x0D; image and the original image. The results of testing the robustness of the algorithm both to simulated distortions&#x0D; of the printing and scanning channel, and to distortions introduced into images when using real printing and scanning&#x0D; devices are presented. The impact of the embedding process on image quality is also considered.

Atmospheric Turbulence Degraded Video Restoration with Recurrent GAN (ATVR-GAN).

Atmospheric turbulence (AT) can change the path and direction of light during video capturing of a target in space due to the random motion of the turbulent medium, a phenomenon that is most noticeable when shooting videos at long ranges, resulting in severe video dynamic distortion and blur. To mitigate geometric distortion and reduce spatially and temporally varying blur, we propose a novel Atmospheric Turbulence Video Restoration Generative Adversarial Network (ATVR-GAN) with a specialized Recurrent Neural Network (RNN) generator, which is trained to predict the scene's turbulent optical flow (OF) field and utilizes a recurrent structure to catch both spatial and temporal dependencies. The new architecture is trained using a newly combined loss function that counts for the spatiotemporal distortions, specifically tailored to the AT problem. Our network was tested on synthetic and real imaging data and compared against leading algorithms in the field of AT mitigation and image restoration. The proposed method outperformed these methods for both synthetic and real data examined.

2OC: A General Automated Orientation and Orthorectification Method for Corona KH-4B Panoramic Imagery

Due to a lack of geographical reference information, complex panoramic camera models, and intricate distortions, including radiation, geometric, and land cover changes, it can be challenging to effectively apply the large number (800,000+) of high-resolution Corona KH-4B panoramic images from the 1960s and 1970s for surveying-related tasks. This limitation hampers their significant potential in the remote sensing of the environment, urban planning, and other applications. This study proposes a method called 2OC for the automatic and accurate orientation and orthorectification of Corona KH-4B images, which is based on generalized control information from reference images such as Google Earth orthophoto. (1) For the Corona KH-4B panoramic camera, we propose an adaptive focal length variation model that ensures accuracy and consistency. (2) We introduce a robust multi-source remote sensing image matching algorithm, which includes an accurate primary orientation estimation method, a multi-threshold matching enhancement strategy based on scale, orientation, and texture (MTE), and a model-guided matching strategy. These techniques are employed to extract high-accuracy generalized control information for Corona images with significant geometric distortions and numerous weak texture areas. (3) A time-iterative Corona panoramic digital differential correction method is proposed. The orientation and orthorectification results of KH-4B images from multiple regions, including the United States, Russia, Austria, Burkina Faso, Beijing, Chongqing, Gansu, and the Qinghai–Tibet Plateau in China, demonstrate that 2OC not only achieves automation but also attains a state-of-the-art level of generality and accuracy. Specifically, the standard deviation of the orientation is less than 2 pixels, the mosaic error of orthorectified images is approximately 1 pixel, and the standard deviation of ground checkpoints is better than 4 m. In addition, 2OC can provide a longer time series analysis of data from 1962 to 1972, benefiting various fields such as environmental remote sensing and archaeology.

Effect of choosing coordinate systems on computationally predicting nonradiative transition rates of flexible thermally activated delayed fluorescence molecules

AbstractUnderstanding nonradiative transition mechanisms is important in various situations. However, compared with radiative processes where temporal profiles of photon emission can be monitored in a straightforward manner, experimentally accessing the rate information may not be an easy task with nonradiative transitions. Hence, applying theoretical tools toward predicting the rates can be a useful tactic. Such predictions become very useful in designing optoelectronic materials as in the molecules adopted for constructing organic light‐emitting diodes (OLEDs). The correlation function formalism is a method that can fulfill the purpose of designing OLED materials. The formalism requires information regarding the vibrational normal modes of the two electronic states before and after the transition. Because the method is also based on harmonic oscillator approximation, it can actually fail to provide high reliability when there is a large geometric distortion between the initial and the final states. In fact, the harmonic normal mode picture is more prone to lose reliability in the Cartesian coordinates than in the internal ones even at a small distortion, and hence adopting internal coordinates may be more preferable for practical calculations. This is because normal mode mixing becomes less severe when molecular coordinates are described with internal degrees of freedom such as bond stretching, bending, and torsion. In this regard, how much more reliable the nonradiative rate predictions in OLED materials become with the use of internal coordinate system deserves a close inspection. In this account, we review on the derivation of the correlation function formalism and provide how it can be adapted toward the use of the internal coordinates. As a demonstration, we evaluate the intersystem crossing and the internal conversion rates of a series of thermally activated delayed fluorescence (TADF) molecules with both Cartesian and internal coordinate systems. Overall, handling transitions involving substantial structural changes is improved indeed with the internal coordinates. However, limitations are still apparent for the TADF systems with a flexible donor–acceptor type construct especially when the inevitable inter‐domain twisting takes place with the electronic transition. Future prospect for handling the issue is commented as a concluding remark.

Geometrically Deformed and Conformationally Rigid Phosphorus Trisamides Featuring an Unsymmetrical Backbone.

Nonclassical P(III) centers have attracted much attention in recent years. Incorporating a P(III) center in a rigid bicyclic platform offers a particularly attractive way to invoke significant geometric distortion of the phosphorus atom that may in turn induce unusual reactivity. Although still relatively scarcely explored, phosphorus centers enforced in a non-C3 symmetry have gained significant traction lately. However, the current scaffolds are based on a relatively limited set of design principles and ligand platforms associated therewith. This work is focussed on the synthesis as well as versatile oxidation, addition and coordination chemistry of a geometrically distorted P(III) species featuring a synthetically modular, nonsymmetric trisamine platform derived from 2-(methylamino)-N-(2-(methylamino)phenyl)benzenesulfonamide.

Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.

Design Optimization of Components for Additive Manufacturing-Repair: An Exploration of Artificial Neural Network Requirements and Application

The integration of artificial intelligence (AI) in additive manufacturing (AM) technology is currently a promising and leading area of research for component repair and restoration. The Issues of high cost and time consumption for AM repair have been a subject of discussion among researchers in this field of study. Moreover, the potential challenges in dealing with complex components for repair and restoration in the (AM) domain require the establishment of a critical technical platform based on hybrid (AI). At this point, the proposed optimization method must cover all important parameters for the complex configuration of structural components under restoration. For the purpose of this study, a design optimization framework was developed using a MATLAB-SIMULINK mathematical model for AM solution purposes by improving the functionality and integration of monitoring. This improvement is based on facilitating the real-time identification of failures with accuracy and giving a clear monitoring vision according to the intended targets like geometric distortions, residual stresses evaluation, and defect characterization. The improvement involves overcoming a number of challenges such as the pre-fabrication stage by expanding the data repository besides offering a theoretical set of algorithmic with some options that improve the current procedure. Also, this study will conclude and suggest a further framework and new knowledge for restoration and product life cycle extension. This developed ANN can be used at the real pace of modeling the MATLAB-Simulink system and merged with another suitable algorithm to form a hybrid ANN. This model development using a neural network has attained a good manipulation of AM. The predicted data from ANN model that was determined and achieved in this study can be used to facilitate and enhance any further study as base knowledge in merging the ANN with another AI to form a hybrid algorithm.

Estimation of effective b-value for a diffusion-weighted double-echo steady-state sequence with bipolar gradients

Diffusion-weighted double-echo steady-state (dwDESS) MRI with bipolar diffusion gradients is a promising candidate to obtain diffusion weighted images (DWI) free of geometric distortions and with low motion sensitivity. However, a wider clinical application of dwDESS is currently hindered as no method is reported to explicitly calculate the effective b-value of the obtained DWI from the diffusion-gradients applied in the sequence. To this end, a previously described signal model was adapted for dwDESS with bipolar diffusion gradients, which allows to estimate an effective b-value, dubbed b’. Evaluation in phantom examinations was performed on a clinical 1.5 T MR system. Experimental results were compared with theoretical predictions, including the apparent diffusion coefficient (ADC) based on b-values from a standard EPI-DWI sequence and ADC’ based on the effective b’ from the dwDESS sequence. The adapted signal model was able to describe the experimental results, and the obtained values of ADC’ were in line with conventional ADC measurements.

Image quality in whole-body MRI using the MY-RADS protocol in a prospective multi-centre multiple myeloma study

BackgroundThe Myeloma Response Assessment and Diagnosis System (MY-RADS) guidelines establish a standardised acquisition and analysis pipeline for whole-body MRI (WB-MRI) in patients with myeloma. This is the first study to assess image quality in a multi-centre prospective trial using MY-RADS.MethodsThe cohort consisted of 121 examinations acquired across ten sites with a range of prior WB-MRI experience, three scanner manufacturers and two field strengths. Image quality was evaluated qualitatively by a radiologist and quantitatively using a semi-automated pipeline to quantify common artefacts and image quality issues. The intra- and inter-rater repeatability of qualitative and quantitative scoring was also assessed.ResultsQualitative radiological scoring found that the image quality was generally good, with 94% of examinations rated as good or excellent and only one examination rated as non-diagnostic. There was a significant correlation between radiological and quantitative scoring for most measures, and intra- and inter-rater repeatability were generally good.When the quality of an overall examination was low, this was often due to low quality diffusion-weighted imaging (DWI), where signal to noise ratio (SNR), anterior thoracic signal loss and brain geometric distortion were found as significant predictors of examination quality.ConclusionsIt is possible to successfully deliver a multi-centre WB-MRI study using the MY-RADS protocol involving scanners with a range of manufacturers, models and field strengths. Quantitative measures of image quality were developed and shown to be significantly correlated with radiological assessment. The SNR of DW images was identified as a significant factor affecting overall examination quality.Trial registrationClinicalTrials.gov, NCT03188172, Registered on 15 June 2017.Critical relevance statementGood overall image quality, assessed both qualitatively and quantitatively, can be achieved in a multi-centre whole-body MRI study using the MY-RADS guidelines.Key points• A prospective multi-centre WB-MRI study using MY-RADS can be successfully delivered.• Quantitative image quality metrics were developed and correlated with radiological assessment.• SNR in DWI was identified as a significant predictor of quality, allowing for rapid quality adjustment.Graphical

Fabrication of thin-walled hat-shaped beams from ultrahigh strength steel by laser-assisted robotic roller forming

Laser-assisted robotic roller forming (LRRF) can be applied to forming ultrahigh strength sheets to products with high geometrical accuracy. In this study, the geometrical profiles and three-point bending performance of thin-walled hat-shaped beams formed by LRRF and robotic roller forming (RRF, i.e., without laser heating) were experimentally investigated. It is found that the geometrical distortion resulted from the accumulation of longitudinal tension during multi-pass roller forming is significantly alleviated by LRRF. In addition, the bending performance of LRRF beam assembly is ameliorated at the initial deformation stage of three-point bending. The improvement is ascribed to a smaller bending radius and the alter of mechanical properties at the laser heat-affected zone. The lower geometrical precision of beams formed without laser heating results in early failure of spot welds of the beam assembly during three-point bending. LRRF beam assembly exhibits larger displacements at failure, and the failure mode is cracking at the edge of the laser-irradiated region. The pronounced microstructure differences on both sides of the crack suggest that the obvious laser-induced mechanical property gradients are responsible for the failure of LRRF beam assembly during three-point bending.

Reliable Feature Matching for Spherical Images via Local Geometric Rectification and Learned Descriptor

Spherical images have the advantage of recording full scenes using only one camera exposure and have been becoming an important data source for 3D reconstruction. However, geometric distortions inevitably exist due to the spherical camera imaging model. Thus, this study proposes a reliable feature matching algorithm for spherical images via the combination of local geometric rectification and CNN (convolutional neural network) learned descriptor. First, image patches around keypoints are reprojected to their corresponding tangent planes based on a spherical camera imaging model, which uses scale and orientation data from the keypoints to achieve both rotation and scale invariance. Second, feature descriptors are then calculated from the rectified image patches by using a pre-trained separate detector and descriptor learning network, which improves the discriminability by exploiting the high representation learning ability of the CNN. Finally, after classical feature matching with the ratio test and cross check, refined matches are obtained based on an essential matrix-based epipolar geometry constraint for outlier removal. By using three real spherical images and an incremental structure from motion (SfM) engine, the proposed algorithm is verified and compared in terms of feature matching and image orientation. The experiment results demonstrate that the geometric distortions can be efficiently reduced from rectified image patches, and the increased ratio of the match numbers ranges from 26.8% to 73.9%. For SfM-based spherical image orientation, the proposed algorithm provides reliable feature matches to achieve complete reconstruction with comparative accuracy.

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.