Accurate Image Research Articles

Application of deep learning algorithms in classification and localization of implant cutout for the postoperative hip.

This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs. The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity. The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions. The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.

Artificial Intelligence and Privacy

Modern Artificial Intelligence (AI) technologies have a rapidly growing impact on a wide range of human activities. AI methods are being used in varied domains such as healthcare, material science, infrastructure engineering, social media, surveillance technologies, and even artistic expression. They have been used for the purposes of drug discovery via protein folding prediction, power usage optimization through reinforcement learning, and facial recognition by means of image segmentation. Their effectiveness and wide-scale, unregulated deployment within our societies pose significant risks to our fundamental rights. Multiple existing AI methods have the potential to profoundly undermine our ability to safeguard our privacy. The societal impact of such AI models can be investigated through six concentric Heuristic Zones of privacy. These AI models can perform inferences regarding highly sensitive, personal information such as race, gender, and intelligence from seemingly innocuous data sources beyond the capabilities of human experts. They are capable of generating increasingly accurate text and image recreations of our thoughts from non-invasive brain activity recordings such as magnetoencephalography and functional magnetic resonance imaging. Furthermore, prospective AI technologies pose concerns about the existential risk to our civilization which extend beyond the erosion of privacy and other fundamental human rights.

A Fluorescence Light-Up 3D DNA Walker Driven and Accelerated by Endogenous Adenosine-5'-triphosphate for Sensitive and Rapid Label-Free MicroRNA Detection and Imaging in Living Cells.

Herein, a fluorescence light-up 3D DNA walker (FLDW) was powered and accelerated by endogenous adenosine-5'-triphosphate (ATP) molecules to construct a biosensor for sensitive and rapid label-free detection and imaging of microRNA-221 (miRNA-221) in malignant tumor cells. Impressively, ATP as the driving force and accelerator for FLDW could significantly accelerate the operation rate of FLDW, reduce the likelihood of errors in signaling, and improve the sensitivity of detection and imaging. When FLDW was initiated by output DNA H1-op transformed by target miRNA-221, G-rich sequences in the S strand, anchored to AuNP, were exposed to form G-quadruplexes (G4s), and thioflavin T (ThT) embedded in the G4s emitted intense fluorescence to realize sensitive and rapid detection of target miRNA-221. Meanwhile, the specific binding of ThT to G4 with a weak background fluorescence response was utilized to enhance the signal-to-noise ratio of the label-free assay straightforwardly and cost-effectively. The proposed FLDW system could realize sensitive detection of the target miRNA-221 in the range of 1 pM to 10 nM with a detection limit of 0.19 pM by employing catalytic hairpin assembly (CHA) to improve the conversion of the target. Furthermore, by harnessing the abundant ATP present in the tumor microenvironment, FLDW achieved rapid and accurate imaging of miRNA-221 in cancer cells. This strategy provides an innovative and high-speed label-free approach for the detection and imaging of biomarkers in cancer cells and is expected to be a powerful tool for bioanalysis, diagnosis, and prognosis of human diseases.

Development and comprehensive evaluation of a dual-port textile UWB MIMO antenna for biomedical use

In this paper, a textile two-port multi-input multi-output (MIMO) half-circle antenna with corrugated borders is designed and fully analyzed for nearfield ultra-wideband biomedical diagnostic applications. The orthogonal polarization technique is introduced for mutual coupling reduction, signal quality, and imaging accuracy improvement. A low pass filter with significant out-of-band rejection is inserted between the two MIMO structural elements to improve the isolation from element to element. For simple system integration, A 50 ohms feed coplanar waveguide is suggested for each element. The entire antenna is constructed using textile materials, with the radiating element composed of conductor fabric. The substrate, on the other hand, is crafted from cotton material which possesses a relative permittivity of and a tan δ value of 0.025. The size of each antenna element has low-profile dimensions of 40 × 40 × 0.3 mm3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${mm}^{3}$$\\end{document}. The measured results obtained demonstrate an extremely broad bandwidth extends from 2.5 GHz to 12 GHz. Omnidirectional radiation pattern is obtained, with 6 dBi maximum gain and 96% efficiency. In order to ensure flexibility, a range of bending conditions were analyzed, and the durability was assessed through wash ability testing. Safety was confirmed by measuring the specific absorption rate. Furthermore, time domain and MIMO performance assessments were performed to ensure its suitability for imaging systems.

Rapid Ultrasonography for Shock and Hypotension Protocol Performed using Handheld Ultrasound Devices by Paramedics in a Moving Ambulance: Evaluation of Image Accuracy and Time in Motion.

Handheld ultrasound (US) devices have become increasingly popular since the early 2000s due to their portability and affordability compared to conventional devices. The Rapid Ultrasonography for Shock and Hypotension (RUSH) protocol, introduced in 2009, has shown promising accuracy rates when performed with handheld devices. However, there are limited data on the accuracy of such examinations performed in a moving ambulance. This study aimed to assess the feasibility and accuracy of the RUSH protocol performed by paramedics using handheld US devices in a moving ambulance. The study aimed to examine the performability of the RUSH protocol with handheld US devices in a moving ambulance and to evaluate the accuracy of diagnostic views obtained within an appropriate time frame. A prospective study was conducted with paramedics who underwent theoretical and practical training in the RUSH protocol. The participants performed the protocol using a handheld US device in both stationary and moving ambulances. Various cardiac and abdominal views were obtained and evaluated for accuracy. The duration of the protocol performance was recorded for each participant. Nine paramedics completed the study, with 18 performances each in both stationary and moving ambulance groups. The accuracy of diagnostic views obtained during the RUSH protocol did not significantly differ between the stationary and moving groups. However, the duration of protocol performance was significantly shorter in the moving group compared to the stationary group. Paramedics demonstrated the ability to perform the RUSH protocol effectively using handheld US devices in both stationary and moving ambulances following standard theoretical and practical training. The findings suggest that ambulance movement does not significantly affect the accuracy of diagnostic views obtained during the protocol. Further studies with larger sample sizes are warranted to validate these findings and explore the potential benefits of prehospital US in dynamic environments.

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image’s intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model’s capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks.

Objective&#xD;Fast and accurate deformable image registration (DIR) that include DIR uncertainty estimation are essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.&#xD;Approach&#xD;In this study, we introduce a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks (CNNs) to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo Drop-Out, and Monte Carlo B-splines) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.&#xD;Main results&#xD;The hyperparameter tuning of the models showed that there is a trade-off between the reliability of the estimated uncertainty and the accuracy of the deformation. In the optimal trade-off our model excelled in contour propagation and uncertainty estimation (p < 0.01) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.&#xD;Significance&#xD;By addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted paving the way for safe deployment in clinical environment.&#xD.

True lateral imaging during lumbar medial branch radiofrequency neurotomy: Interobserver reliability

BackgroundTrue lateral imaging (TLI), obtained by superimposing bilateral lumbar spine structures and aligning superior endplate cortical bone, requires deliberate rotational adjustments of the laterally positioned fluoroscope in both the axial and longitudinal planes. True lateral segmental imaging is necessary to depict true and accurate radiofrequency (RF) cannula positioning relative to bony anatomy during lumbar medial branch radiofrequency neurotomy (LMBRFN). ObjectiveTo determine the interobserver reliability of TLI during LMBRFN. MethodsThis was a retrospective review of a prospectively generated collection of lateral fluoroscopic images to determine the interobserver reliability of TLI during LMBRFN. Lateral fluoroscopic images were prospectively collected from 34 consecutive L4-5 and L5-S1 LMBRFN procedures during routine clinical practice. Employing International Pain and Spine Intervention Society (IPSIS) LMBRFN and TLI techniques, an RF cannula was positioned parallel to the L3 and L4 medial branches and the L5 dorsal rami. During the normal course of TLI, untrue and final true lateral segmental images were obtained and saved. An original data set of 100 pairs of true and untrue lateral images was reviewed to verify true laterality using established criteria; disagreement was resolved by consensus or discarding ambiguous cases. To measure interobserver reliability (Cohen's Kappa), two blinded expert reviewers independently reviewed the image set, identifying the true lateral image and the plane requiring correction. ResultsThe observers agreed upon 98/98 true lateral RF-segment images (Kappa score 1.0 [1.00,1.00]). The observers agreed upon 86/98 maneuvers to correct the untrue RF-segment image. The Kappa score for determining the most appropriate corrective maneuver was 0.76 (0.63,0.89), showing substantial interobserver agreement. ConclusionsThe true lateral image of the targeted RF segment during LMBRFN was reliably determined with perfect interobserver agreement. Interobserver agreement was substantial regarding the maneuver to achieve TLI.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

Illuminating Urban Near-Surface with Distributed Acoustic Sensing Multimodal Noise Surface-Wave Imaging

Abstract Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.

Spectroscopy Detection and Imaging System Based on Line Array Single Photon Detectors

Abstract. Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole

Abstract. Because of its special significance, exploration missions and research at the Lunar South Pole (LSP) are of considerable importance. The mapping products of the LSP such as Digital Ortho Maps (DOMs) and Digital Elevation Models (DEMs) are essential to support exploration missions. However, in the LSP region, the available data are relatively limited. The large amounts of shadows and the special environment of the LSP has an important impact on both image matching and generation of mapping products. In this paper, we propose a photogrammetric processing method to improve the accuracy of the LRO NAC images by using LOLA DEM as auxiliary control data. The LOLA DEM was first rendered using the same illumination conditions as the corresponding LRO NAC raw images, and then matched to the LRO NAC DOMs. Next, the corresponding elevations of the tie points between LOLA DEMs and LRO NAC images were extracted and automatically added to the control network for bundle adjustment. In order to verify the feasibility of the method, we selected 17 LRO NAC images near the LSP at 85° south latitude. The results show that the proposed method can efficiently acquire uniformly distributed elevation control points for bundle adjustment and the generated DEM has a good consistency with the LOLA DEM.

Quantitative assessment of delamination in composites using multiple local-defect-resonance modes

Accurately visualizing the geometry and dimensions of internal delamination in fiber-reinforced polymer (CFRP) composites is a challenging endeavor. In this paper, we develop a method to identify and characterize delamination in composites by the integration of multiple local defect resonance (LDR) modes. The proposed method was validated by using T300/M914 CFRP plates containing three distinct defect geometries, employing both numerical simulations and experimental measurements. The approach commences with the application of a frequency-domain finite element method for LDR frequency determination, emphasizing improved computational efficiency. This technique is further corroborated through the use of the defect-to-background ratio (DBR) method, which identifies LDR frequencies in both numerical simulations and experimental testing. The integration of multiple LDR modes (IMLDR) at various frequencies enables precise imaging of delamination defects of diverse geometries and sizes. Experimental results closely align with the numerical findings, confirming that the integrated imaging results accurately correspond to artificial defects. This method furnishes precise descriptions of damage location, geometry, and dimensions. The obtained results indicate that the proposed IMLDR-based technique significantly enhances the accuracy and reliability of delamination imaging in composites. Consequently, it offers an effective approach for quantitatively assessing delamination damage in composites.

SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.

To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully sampled reference data. We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to ). Our results also highlight the importance of different modules of SPICER-including the DMBA, the CSM estimation, and the SPICER training loss-on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully sampled ground-truth data.

THE TEN-YEAR RADIOLOGICAL RESULTS OF THE UNCEMENTED OXFORD MEDIAL COMPARTMENT KNEE ARTHROPLASTY

IntroductionThe Oxford Unicompartmental Knee Replacement (OUKA) is the most popular unicompartmental knee replacement (UKR) in the New Zealand Joint Registry with the majority utilising cementless fixation. We report the 10-year radiological outcomes.MethodsThis is a prospective observational study. All patients undergoing a cementless OUKA between May 2005 and April 2011 were enrolled. There were no exclusions due to age, gender, body mass index or reduced bone density.All knees underwent fluoroscopic screening achieving true anteroposterior (AP) and lateral images for radiographic assessment. AP assessment for the presence of radiolucent lines and coronal alignment of the tibial and femoral components used Inteliviewer radiographic software. The lateral view was assessed for lucencies as well as sagittal alignment.Results687 OUKAs were performed in 641 patients. Mean age at surgery was 66 years (39–90yrs), 382 in males and 194 right sided. 413 radiographs were available for analysis; 92 patients had died, 30 UKRs had been revised and 19 radiographs were too rotated to be analysed the remainder were lost to follow-up. Mean radiograph to surgery interval was 10.2 years (7.1–16.2yrs).RLLs were identified in zone 1 (3 knees), zone 2 (2 knees), zone 3 (3 knees), zone 5 (3 knees), zone 6 (2 knees) and zone 7 (42 knees). No RLL had progressed, and no case had any osteolysis or prosthesis subsidence.Alignment in the coronal plane: mean 2.90° varus (9.30° varus - 4.49° valgus) of the tibial component to the tibial anatomic axis and the femoral component in mean 4.57° varus (17.02° varus - 9.3° valgus). Sagittal plane posterior tibial slope was a mean 6.30° (0.44° -13.60° degrees) and mean femoral component flexion of 8.11° (23.70° flexion – 16.43° extension).ConclusionThe cementless OUKA demonstrates stable fixation with low revision rates at our centre supporting results earlier published by the design centre.

Single-shot pump-probe technique by the combination of an echelon and a grating with a time window of 109 ps

In this study, using only a single pulse, pump-probe measurement with a large time window of more than 100 ps is implemented. A commercial grating is used to encode a time window of ∼56 ps in a single pulse; therefore, there is no need for machining customization. In addition, in this technique, the grating surface is accurately imaged, eliminating the image blur problem caused by phase differences in previous echelon-based techniques. Moreover, to make full use of the grating surface and obtain a larger time window, a simple reflection echelon is combined that matches the grating in the time window. This combination encoding strategy results in a total time window of ∼109 ps and maintains accurate imaging of the grating surface. This time window is an order of magnitude greater than the maximum reported values of the echelon encoding strategy and the angle beam encoding strategy. To demonstrate this single-shot pump-probe technique, the two-photon absorption process of ZnSe and the excited-state absorption process of a symmetrical phenoxazinium bromine salt were studied. The possibility of further improving the experimental setup is also discussed.

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.

Strong Correlation Between Femoral Condyle Radius of Curvature and Anterior-Posterior Length

Background: The radius of curvature (ROC) of the femoral condyle is a factor in potential cartilage incongruities following osteochondral allograft (OCA) transplantation. Accurate restoration of the chondral surface may be achievable by using “best-fit” donor-recipient matching based on linear femorotibial dimensions, such as the femoral condyle anterior-posterior length (APL), femoral condyle width (lateral-medial length, LML), femoral hemicondyle width (HCW), and tibial plateau width (TPW), particularly if they correlate well with the ROC. This study aimed to investigate the correlative relationship between femorotibial dimensions and the ROC. Methods: Computed tomography (CT) scans from 49 patients (31 men 28 ± 10 years old and 18 women 27 ± 6 years old) were analyzed. Axial images were used for APL and LML measurements, while coronal images were used for HCW and TPW. True sagittal images of the medial femoral condyle (MFC) and lateral femoral condyle (LFC) were used to calculate their individual ROCs by determining the best-fit circles along the condylar surface. Linear regression models were used to determine the relationship between the femorotibial dimensions and ROC. Measurements were repeated for a randomly selected subset of the data, and intraclass correlation coefficients (ICCs) were calculated to investigate intra- and interobserver reliability. Results: All femorotibial dimensions showed significant correlations with the MFC and LFC ROCs (p &lt; 0.01). The ROC correlations with femorotibial dimensions were found to be in the following descending order: APL (R2 ≥ 0.83), LML (R2 ≥ 0.52), TPW (R2 ≥ 0.36), and HCW (R2 ≥ 0.27). The intra- and interobserver reliabilities for the APL (ICC &gt; 0.98) and ROC (ICC &gt; 0.94) were excellent. Conclusions: The ROC was strongly correlated with the APL of the MFC and LFC. Donor-recipient APL matching in OCA transplantation may provide a level of matching similar to that achievable by direct ROC measurements. Clinical Relevance: Determining the most predictive femorotibial dimension for ROC restoration in the OCA matching process may improve clinical outcomes, particularly for patients with large osteochondral lesions.

Determining Riverine Surface Roughness at Fluvial Mesohabitat Level and Its Influence on UAV-Based Thermal Imaging Accuracy

Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we developed the SR Measurement Device (SRMD). The SRMD uses the concept of in situ quantification of wave frequency and wave amplitude. Data of nine installed SRMDs in four different fluvial mesohabitat classes presented a range of 0 to 47 waves per 30 s and an amplitude range of 0 to 6 cm. Even subtle differences between mesohabitat classes run, riffle, and no-/low-flow still and pool areas could be detected with the SRMD. However, SR revealed no significant influence on the accuracy of thermal infrared (TIR) imagery data in our study case. Overall, the presented device expands existing methods of riverine habitat assessments and has the potential to produce highly relevant data of SR for various ecological and technical applications, ranging from remote sensing of surface water and habitat quality characterizations to bank stability and erosion risk assessments.

High-accuracy ultrasonic imaging of the defects in the complex-surface components by the search-vector imaging condition

Complex-surface components play more and more significant roles in engineering practice because of the development of manufacturing technology and requirements from various industries. Void and crack defects inside the components greatly deteriorate the mechanical properties, and ultrasonic full-matrix imaging is a promising method to detect the defects. However, the research on high-quality full-matrix imaging for complex-surface components is limited. In this paper, a full-matrix imaging method based on the search-vector imaging condition is proposed to detect the defects in complex-surface components with high accuracy. Herein, the search-vector imaging condition is established based on waveform inversion, which employs the first-order derivative vector and approximated Hessian matrix to obtain the high-accuracy images of defects. The proposed imaging condition does not make assumptions about the wavefield and is more rigorous than the conventional excitation-time imaging condition. Furthermore, the proposed method can highlight defects throughout the measurement area, so it is very convenient for locating defects in large measurement structures. In the simulation and experiments, defects in complex sound speed models are the measurement targets, and the proposed method has significantly improved the imaging accuracy, resolution and signal-to-noise ratio (SNR) in all cases compared with the conventional methods, where all the void and crack defects are highlighted clearly in the whole image. In addition, the proposed method is not sensitive to the influencing factors and has strong robustness. Therefore, the proposed method has great potential in defect detection for complex-surface components in various fields.