Reconstruction Approach Research Articles

Reconstruction of dense time series high spatial resolution NDVI data using a spatiotemporal optimal weighted combination estimation model based on Sentinel-2 and MODIS

A regular, dense time series of the normalized difference vegetation index (NDVI) is a crucial remote sensing parameter that provides insights into the growth status of plants across different temporal and spatial scales. However, the production of high-resolution, equal-interval, and dense-time-series NDVI products currently faces technical challenges, limiting their application in agriculture and forestry-related fields. This study proposes a new method for reconstructing a dense Sentinel-2 NDVI time series based on the spatiotemporal optimal weighted combination estimation model (SOWCEM). The SOWCEM is developed using state-of-the-art spatial and temporal reconstruction algorithms. This model considers the distribution characteristics of errors in spatiotemporal dimensions, allowing for adaptive determination of the optimal combination weight at the pixel level. Then, the reconstructed regular dense-time-series NDVI data covering the region for five days/time are evaluated through a comparative analysis. The experimental results demonstrate that the regional NDVI reconstruction image based on the SOWCEM algorithm has a determination coefficient (R2) of 0.9082, a root mean square error (RMSE) of 0.0403, and a comprehensive overall error (ERGAS) of 5.7726. Compared with single spatiotemporal dimension models, the SOWCEM algorithm shows an average improvement of 5.78% in R2, while the RMSE and ERGAS decrease by 6.5% and 6.86%, respectively. Moreover, the stability and robustness of the models are also enhanced. These results indicate that the SOWCEM algorithm can effectively achieve a high-quality fusion reconstruction of regional images, significantly enhancing the accuracy of high-spatial-resolution NDVI dense temporal spectral reconstruction. Furthermore, it exhibits clear superiority and good applicability. This algorithmic framework provides a feasible approach for the dense reconstruction of high-spatial-resolution regular time-series NDVI data, enabling more accurate and efficient high-resolution NDVI remote sensing monitoring services in related field applications. The core code of SOWCEM is available at https://github.com/GISerZkun/SOWCEM.

Joint k-ω Space Image Reconstruction and Data Fitting for Chemical Exchange Saturation Transfer Magnetic Resonance Imaging.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k-ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω, while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively.

Assessment of image quality and diagnostic accuracy for cervical spondylosis using T2w-STIR sequence with a deep learning-based reconstruction approach.

To investigate potential of enhancing image quality, maintaining interobserver consensus, and elevating disease diagnostic efficacy through the implementation of deep learning-based reconstruction (DLR) processing in 3.0T cervical spine fast magnetic resonance imaging (MRI) images, compared with conventional images. The 3.0T cervical spine MRI images of 71 volunteers were categorized into two groups: sagittal T2-weighted short T1 inversion recovery without DLR (Sag T2w-STIR) and with DLR (Sag T2w-STIR-DLR). The assessment covered artifacts, perceptual signal-to-noise ratio, clearness of tissue interfaces, fat suppression, overall image quality, and the delineation of spinal cord, vertebrae, discs, dopamine, and joints. Spanning canal stenosis, neural foraminal stenosis, herniated discs, annular fissures, hypertrophy of the ligamentum flavum or vertebral facet joints, and intervertebral disc degeneration were evaluated by three impartial readers. Sag T2w-STIR-DLR images exhibited markedly superior performance across quality indicators (median = 4 or 5) compared to Sag T2w-STIR sequences (median = 3 or 4) (p < 0.001). No statistically significant differences were observed between the two sequences in terms of diagnosis and grading (p > 0.05). The interobserver agreement for Sag T2w-STIR-DLR images (0.604-0.931) was higher than the other (0.545-0.853), Sag T2w-STIR-DLR (0.747-1.000) demonstrated increased concordance between reader 1 and reader 3 in comparison to Sag T2w-STIR (0.508-1.000). Acquisition time diminished from 364 to 197s through the DLR scheme. Our investigation establishes that 3.0T fast MRI images subjected to DLR processing present heightened image quality, bolstered diagnostic performance, and reduced scanning durations for cervical spine MRI compared with conventional sequences.

Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom.

For reliable DCE MRI parameter estimation, k-space undersampling is essential to meet resolution, coverage, and signal-to-noise requirements. Pseudo-spiral (PS) sampling achieves this by sampling k-space on a Cartesian grid following a spiral trajectory. The goal was to optimize PS k-space sampling patterns for abdomin al DCE MRI. The optimal PS k-space sampling pattern was determined using an anthropomorphic digital phantom. Contrast agent inflow was simulated in the liver, spleen, pancreas, and pancreatic ductal adenocarcinoma (PDAC). A total of 704 variable sampling and reconstruction approaches were created using three algorithms using different parametrizations to control sampling density, halfscan and compressed sensing regularization. The sampling patterns were evaluated based on image quality scores and the accuracy and precision of the DCE pharmacokinetic parameters. The best and worst strategies were assessed in vivo in five healthy volunteers without contrast agent administration. The best strategy was tested in a DCE scan of a PDAC patient. The best PS reconstruction was found to be PS-diffuse based, with quadratic distribution of readouts on a spiral, without random shuffling, halfscan factor of 0.8, and total variation regularization of 0.05 in the spatial and temporal domains. The best scoring strategy showed sharper images with less prominent artifacts in healthy volunteers compared to the worst strategy. Our suggested DCE sampling strategy also showed high quality DCE images in the PDAC patient. Using an anthropomorphic digital phantom, we identified an optimal PS sampling strategy for abdominal DCE MRI, and demonstrated feasibility in a PDAC patient.

Automatic damage detection and data completion for operational bridge safety using convolutional echo state networks

Structural response data might be partially missing during acquisition and transmission by a structural health monitoring (SHM) system, affecting subsequent structural damage identification. Conventional deep learning methods require a substantial amount of data and have low training efficiency. With this in mind, this paper proposes a hybrid approach for data reconstruction and damage identification using an echo state network embedded with convolutional layers. The network has a training-free reservoir layer and focuses on historical information and multi-channel spatial features. As data redundancy occurs in most measured structural vibration responses, the extended Frobenius norm-principal component analysis method is applied to select valuable samples for updating the network, which greatly enhances the training efficiency. Experimental and numerical data from a physical bridge model is used to verify the data reconstruction capability and damage identification accuracy of the proposed method, and its performance is further compared with several existing deep learning methods.

Spectrum Reconstruction Model Based on Multispectral Electrochromic Devices.

Reconstructing the visible spectra of real objects is critical to the spectral camouflage from emerging spectral imaging. Electrochromic materials exhibit unique superiority for this goal due to their subtractive color-mixing model and structural diversity. Herein, a simulation model is proposed and a method is developed to fabricate electrochromic devices for dynamically reproducing the visible spectrum of the natural leaf. Over 20 kinds of pH-dependent leuco dyes have been synthesized/prepared through molecular engineering and offered available spectra/bands to reconstruct the spectrum of the natural leaf. More importantly, the spectral variance between the device and leaf is optimized from an initial 98.9 to an ideal 10.3 through the simulation model, which means, the similarity increased nearly nine-fold. As a promising spectrum reconstruction approach, it will promote the development of smart photoelectric materials in adaptive camouflage, spectral display, high-end encryption, and anti-counterfeiting.

Cardiorespiratory motion characteristics and their dosimetric impact on cardiac stereotactic body radiotherapy.

Cardiac stereotactic body radiotherapy (CSBRT) is an emerging and promising noninvasive technique for treating refractory arrhythmias utilizing highly precise, single or limited-fraction high-dose irradiations. This method promises to revolutionize the treatment of cardiac conditions by delivering targeted therapy with minimal exposure to surrounding healthy tissues. However, the dynamic nature of cardiorespiratory motion poses significant challenges to the precise delivery of dose in CSBRT, introducing potential variabilities that can impact treatment efficacy. The complexities of the influence of cardiorespiratory motion on dose distribution are compounded by interplay and blurring effects, introducing additional layers of dose uncertainty. These effects, critical to the understanding and improvement of the accuracy of CSBRT, remain unexplored, presenting a gap in current clinical literature. To investigate the cardiorespiratory motion characteristics in arrhythmia patients and the dosimetric impact of interplay and blurring effects induced by cardiorespiratory motion on CSBRT plan quality. The position and volume variations in the substrate target and cardiac substructures were evaluated in 12 arrhythmia patients using displacement maximum (DMX) and volume metrics. Moreover, a four-dimensional (4D) dose reconstruction approach was employed to examine the dose uncertainty of the cardiorespiratory motion. Cardiac pulsation induced lower DMX than respiratory motion but increased the coefficient of variation and relative range in cardiac substructure volumes. The mean DMX of the substrate target was 0.52cm (range: 0.26-0.80cm) for cardiac pulsation and 0.82cm (range: 0.32-2.05cm) for respiratory motion. The mean DMX of the cardiac structure ranged from 0.15 to 1.56cm during cardiac pulsation and from 0.35 to 1.89cm during respiratory motion. Cardiac pulsation resulted in an average deviation of -0.73% (range: -4.01%-4.47%) in V25 between the 3D and 4D doses. The mean deviations in the homogeneity index (HI) and gradient index (GI) were 1.70% (range: -3.10%-4.36%) and 0.03 (range: -0.14-0.11), respectively. For cardiac substructures, the deviations in D50 due to cardiac pulsation ranged from -1.88% to 1.44%, whereas the deviations in Dmax ranged from -2.96% to 0.88% of the prescription dose. By contrast, the respiratory motion led to a mean deviation of -1.50% (range: -10.73%-4.23%) in V25. The mean deviations in HI and GI due to respiratory motion were 4.43% (range: -3.89%-13.98%) and 0.18 (range: -0.01-0.47) (p<0.05), respectively. Furthermore, the deviations in D50 and Dmax in cardiac substructures for the respiratory motion ranged from -0.28% to 4.24% and -4.12% to 1.16%, respectively. Cardiorespiratory motion characteristics vary among patients, with the respiratory motion being more significant. The intricate cardiorespiratory motion characteristics and CSBRT plan complexity can induce substantial dose uncertainty. Therefore, assessing individual motion characteristics and 4D dose reconstruction techniques is critical for implementing CSBRT without compromising efficacy and safety.

Iliac transposition technique for reconstruction of aortic bifurcation in pediatric blunt abdominal aortic injury

Blunt abdominal aortic injuries, especially in pediatric patients, represents a rare and critical challenge. We report a unique case of a 10-year-old boy who presented after a high-speed motor vehicle collision resulting in injury to the aortic bifurcation, alongside other intra-abdominal trauma. A novel surgical approach for aortoiliac reconstruction in a contaminated field was used. This technique consisted of the mobilization of the distal aorta and common iliac arteries, ligation of right internal iliac artery, and creation of aortoiliac and common to internal iliac artery anastomoses. This method demonstrates a potentially lifesaving technique in select patients.

Testing Bell inequality through at CEPC* *Tong Li is Supported by the National Natural Science Foundation of China (12375096, 12035008, 11975129), and "the Fundamental Research Funds for the Central Universities", Nankai University (63196013). Kai Ma was supported by the Natural Science Basic Research Program of Shaanxi Province, China (2023-JC-YB-041) and the Innovation Capability Support Program of Shaanxi Province, China (2021KJXX-47)

The decay of Higgs boson into two spin-1/2 particles provides an ideal system to reveal quantum entanglement and Bell-nonlocality. Future colliders can improve the measurement accuracy of the spin correlation of tau lepton pairs from Higgs boson decay. We show the testability of Bell inequality through at Circular Electron Positron Collider (CEPC). Two realistic methods of testing Bell inequality are investigated, i.e., Törnqvist's method and Clauser-Home-Shimony-Holt (CHSH) inequality. In the simulation, we consider the detector effects of CEPC including uncertainties for tracks and jets from Z boson in the production of . Necessary reconstruction approaches are described to measure quantum entanglement between and . Finally, we show the sensitivity of CEPC to Bell inequality violation for the two methods.

Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings

AbstractAccurate wind pressure analysis on high‐rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework for optimal sensor placement on high‐rise buildings. The framework leverages the strengths of multiresolution dynamic mode decomposition (mrDMD) for feature extraction and incorporates a novel regularization term within an existing sensor placement algorithm under constraints. This innovative term enables the algorithm to consider real‐world system constraints during sensor selection, leading to a more practical and efficient solution for wind pressure analysis. mrDMD effectively analyzes the multiscale features of wind pressure data. The extracted mrDMD modes, combined with the enhanced constrained QR decomposition technique, guide the selection of informative sensor locations. This approach minimizes the required number of sensors while ensuring accurate pressure field reconstruction and adhering to real‐world placement constraints. The effectiveness of this method is validated using data from a scaled building model tested in a wind tunnel. This approach has the potential to revolutionize wind pressure analysis for high‐rise buildings, paving the way for advancements in digital twins, real‐time monitoring, and risk assessment of wind loads.

The Satisfaction and Quality of Life of Patients After Breast Reconstruction: A Cross-Sectional Multicenter Study Comparing Immediate, Delayed, and Nonreconstructive Outcomes.

Breast reconstruction following mastectomy can be performed through various surgical techniques that prioritize the patient's safety and quality of life. Plastic surgeons are trained to choose the most appropriate surgical approach based on the individual patient's needs and medical history. The safety of the patient is always the primary concern, followed by considerations such as aesthetic outcomes and long-term health implications. The aim of this study was to assess and document patients' satisfaction and quality of life after breast reconstruction across Saudi Arabia. This is a cross-sectional multicenter study among female patients who underwent mastectomy with or without breast reconstruction between 2015 and 2022. Two hundred eighty patients participated in this study through a call-based Arabic version of the BREAST-Q questionnaire to analyze the quality of their lives and satisfaction. Our results showed that patients who underwent delayed reconstruction had lower satisfaction than those who underwent immediate reconstruction. The average BREAST-Q score was lower in patients who used tissue expanders than those with implant-based reconstruction, autologous reconstruction, or combined approaches. Patients who underwent simple mastectomy had lower satisfaction (M = 66.1) than those who had a skin-sparing mastectomy (M = 71.1) and/or nipple-sparing mastectomy (M = 72.6). This retrospective multicenter study observed a significant association between the time of the reconstructive surgery and patient's satisfaction; patients who underwent immediate reconstruction had higher satisfaction rate. Lower satisfaction rate was associated with tissue expander breast reconstruction. There is a significant association between satisfaction rate and smoking history.

Efficacy of Narrative Meaning Reconstruction Interview for Pet Loss

Objectives: With this study, I sought to resolve a primary question regarding narrative therapy and its application to bereavement: Is the meaning reconstruction interview (MRI) an effective means to initiate pet loss processing?Methods: Seven participants took part in a 3-phase narrative therapy sequence: an in-office visit; a homework assignment; and a 30-minute phone session. Each phase included a different section of the MRI. Participants were assessed at intake and 15 minutes post-exercise during each phase using the Grief Severity Index (GSI).Results: On average, GSI scores improved during each phase. In addition, all seven participants showed at least some numerical improvement after each phase.Conclusions: My findings suggest the MRI does have a positive impact on pet loss and may serve as an enticing first step in any narrative therapy, experiential counseling, and/or meaning reconstruction approach to pet loss.

Compound 3D building modeling with structure-aware partition and primitive assembly from airborne laser scanning point clouds

ABSTRACT Urban digital twins and realistic three-dimensional (3D) scenes have further enhanced the ever-increasing demand for building models. Airborne Laser Scanning (ALS) point clouds are the predominant data source for 3D building reconstruction owing to their highly detailed spatial features. However, information on the reconstruction of compound buildings (i.e. buildings with diverse plane structures or various roof structures) is lacking. Existing methods often produce building models that suffer from topological inconsistencies and/or geometric errors. To address these issues, this study proposes an automatic building reconstruction approach based on ALS point cloud that offers both modeling flexibility and geometric consistency. Specifically, a structure-aware partitioning algorithm that partitions a complex building roof into several regular roof patches is incorporated. Subsequently, a primitive model library comprising representative primitives suitable for building reconstruction is created. Suitable primitive model selection and reconstruction optimization are employed to ensure the accuracy and compactness in the final model. To comprehensively evaluate the performance, two typical ALS point cloud datasets are tested. Experiments demonstrate the generated models can achieve a boundary consistency of 92% while ensuring Root Mean Square Error below 0.09 m. Comparative experiments with state-of-the-art methods further validate the effectiveness of the proposed approach in compound building reconstruction.

Asymmetric Synthesis and Biological Evaluation of Platensilin, Platensimycin, Platencin, and Their Analogs via a Bioinspired Skeletal Reconstruction Approach.

Platensilin, platensimycin, and platencin are potent inhibitors of β-ketoacyl-acyl carrier protein synthase (FabF) in the bacterial and mammalian fatty acid synthesis system, presenting promising drug leads for both antibacterial and antidiabetic therapies. Herein, a bioinspired skeleton reconstruction approach is reported, which enables the unified synthesis of these three natural FabF inhibitors and their skeletally diverse analogs, all stemming from a common ent-pimarane core. The synthesis features a diastereoselective biocatalytic reduction and an intermolecular Diels-Alder reaction to prepare the common ent-pimarane core. From this intermediate, stereoselective Mn-catalyzed hydrogen atom-transfer hydrogenation and subsequent Cu-catalyzed carbenoid C-H insertion afford platensilin. Furthermore, the intramolecular Diels-Alder reaction succeeded by regioselective ring opening of the newly formed cyclopropane enables the construction of the bicyclo[3.2.1]-octane and bicyclo[2.2.2]-octane ring systems of platensimycin and platencin, respectively. This skeletal reconstruction approach of the ent-pimarane core facilitates the preparation of analogs bearing different polycyclic scaffolds. Among these analogs, the previously unexplored cyclopropyl analog 47 exhibits improved antibacterial activity (MIC80 = 0.0625 μg/mL) against S. aureus compared to platensimycin.

A hybrid approach for reconstruction of transonic buffet aerodynamic noise: Integrating random forest and compressive sensing algorithm

Experimental measurements and numerical simulation methods for obtaining aerodynamic noise face issues such as high costs and long periods. A single machine learning method for predicting aerodynamic noise also requires a sufficient amount of data. According to this, this paper proposes a hybrid method integrating Random Forest and Compressive Sensing (RF_CS) to accurately reconstruct transonic buffet aerodynamic noise from sparse data. First, the RF algorithm, known for its strong nonlinear feature extraction capabilities, is used to obtain the basis function. Then, the basis coefficients are calculated using the L1 optimization algorithm based on limited sensor data and basis functions. Finally, a linear combination of basis functions and basis coefficients is used to reconstruct aerodynamic noise, including power spectral density, sound pressure level, and flow modes. Compared to the Compressive Sensing based on Proper Orthogonal Decomposition (POD_CS), the proposed algorithm can effectively reduce error by approximately 2–20 times and decrease the absolute error of modes by about 2–3 orders of magnitude. Specifically, the RF_CS method ensures that the reconstruction errors for power spectral density across various flow conditions are all below 3E-3, achieving generalization from one flow condition to the entire sample space. Additionally, this approach can utilize approximately 10 sensors to reconstruct accurate sound pressure level and modes, with errors within 5E-3 and 5E-5, respectively. This allows for generalization across the entire Mach number space based on a single Mach number condition.

Acoustic camera-based super-resolution reconstruction approach for underwater perception in low-visibility marine environments

In low-visibility environments, the underwater perception range of optical cameras is severely restricted, and perception operations in ocean engineering often rely on sonar. Acoustic cameras are a type of forward-looking sonar that have attracted considerable attention because of their ability to produce images similar to those of optical cameras. However, owing to the unique imaging mechanism employed by acoustic cameras, the resulting images suffer from insufficient resolution and a loss of feature details. This issue considerably diminishes the precision of downstream visual tasks, limiting the application of acoustic cameras. In this study, we propose a deep-learning-based super-resolution reconstruction approach for acoustic cameras, where the reconstruction process relies only on images, without prior assumptions regarding the detection scenes. We verified the effectiveness of the proposed method for two practical applications: marine debris detection and marine structure inspection. The experimental results show that our proposed method can robustly reconstruct high-resolution sonar images, and the obtained images have superior feature details, which improved the precision of downstream vision tasks. In this study, we aim to provide better solutions for underwater perception in low-visibility marine environments, while exploring the application of acoustic cameras in marine debris detection and structure inspection.

Semihierarchical reconstruction and weak‐area revisiting for robotic visual seafloor mapping

AbstractDespite impressive results achieved by many on‐land visual mapping algorithms in the recent decades, transferring these methods from land to the deep sea remains a challenge due to harsh environmental conditions. Images captured by autonomous underwater vehicles, equipped with high‐resolution cameras and artificial illumination systems, often suffer from heterogeneous illumination and quality degradation caused by attenuation and scattering, on top of refraction of light rays. These challenges often result in the failure of on‐land Simultaneous Localization and Mapping (SLAM) approaches when applied underwater or cause Structure‐from‐Motion (SfM) approaches to exhibit drifting or omit challenging images. Consequently, this leads to gaps, jumps, or weakly reconstructed areas. In this work, we present a navigation‐aided hierarchical reconstruction approach to facilitate the automated robotic three‐dimensional reconstruction of hectares of seafloor. Our hierarchical approach combines the advantages of SLAM and global SfM that are much more efficient than incremental SfM, while ensuring the completeness and consistency of the global map. This is achieved through identifying and revisiting problematic or weakly reconstructed areas, avoiding to omit images and making better use of limited dive time. The proposed system has been extensively tested and evaluated during several research cruises, demonstrating its robustness and practicality in real‐world conditions.

Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging

ObjectivesThis study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-phase sequence. Materials and methodsIn this retrospective study, 29 patients who underwent lumbar spine MRI, including an MR bone imaging sequence between January and April 2023, were analyzed. Images were reconstructed with and without SR-DLR (Matrix sizes: 960 × 960 and 320 × 320, respectively). The signal-to-noise ratio (SNR) of the vertebral body and spinal canal and the contrast and contrast-to-noise ratio (CNR) between the vertebral body and spinal canal were quantitatively evaluated. Furthermore, the slope at half-peak points of the profile curve drawn across the posterior border of the vertebral body was calculated. Two radiologists independently assessed image noise, contrast, artifacts, sharpness, and overall image quality of both image types using a 4-point scale. Interobserver agreement was evaluated using weighted kappa coefficients, and quantitative and qualitative scores were compared via the Wilcoxon signed-rank test. ResultsSNRs of the vertebral body and spinal canal were notably improved in images with SR-DLR (p < 0.001). Contrast and CNR were significantly enhanced with SR-DLR compared to those without SR-DLR (p = 0.023 and p = 0.022, respectively). The slope of the profile curve at half-peak points across the posterior border of the vertebral body and spinal canal was markedly higher with SR-DLR (p < 0.001). Qualitative scores (noise: p < 0.001, contrast: p < 0.001, artifact p = 0.042, sharpness: p < 0.001, overall image quality: p < 0.001) were superior in images with SR-DLR compared to those without. Kappa analysis indicated moderate to good agreement (noise: κ = 0.56, contrast: κ = 0.51, artifact: κ = 0.46, sharpness: κ = 0.76, overall image quality: κ = 0.44). ConclusionSR-DLR, which is based on k-space data, has the potential to enhance the image quality of lumbar spine MR bone imaging utilizing a 3D gradient echo in-phase sequence.Clinical relevance statement: The application of SR-DLR can lead to improvements in lumbar spine MR bone imaging quality.

Liver autotransplantation and atrial reconstruction on a patient with multiorgan alveolar echinococcosis: a case report

BackgroundAlveolar echinococcosis (AE) primarily affects the liver and potentially spreads to other organs. Managing recurrent AE poses significant challenges, especially when it involves critical structures and multiple major organs.Case presentationWe present a case of a 59-year-old female with recurrent AE affecting the liver, heart, and lungs following two previous hepatectomies, the hepatic lesions persisted, adhering to major veins, and imaging revealed additional diaphragmatic, cardiac, and pulmonary involvement. The ex vivo liver resection and autotransplantation (ELRA), first in human combined with right atrium (RA) reconstruction were performed utilizing cardiopulmonary bypass, and repairs of the pericardium and diaphragm. This approach aimed to offer a potentially curative solution for lesions previously considered inoperable without requiring a donor organ or immunosuppressants. The patient encountered multiple serious complications, including atrial fibrillation, deteriorated liver function, severe pulmonary infection, respiratory failure, and acute kidney injury (AKI). These complications necessitated intensive intraoperative and postoperative care, emphasizing the need for a comprehensive management strategy in such complicated high-risk surgeries.ConclusionsThe multidisciplinary collaboration in this case proved effective and yielded significant therapeutic outcomes for a rare case of advanced hepatic, cardiac, and pulmonary AE. The combined approach of ELRA and RA reconstruction under extracorporeal circulation demonstrated distinct advantages of ELRA in treating complex HAE. Meanwhile, assessing diaphragm function during the perioperative period, especially in patients at high risk of developing pulmonary complications and undergoing diaphragmectomy is vital to promote optimal postoperative recovery. For multi-resistant infection, it is imperative to take all possible measures to mitigate the risk of AKI if vancomycin administration is deemed necessary.

A simple method improving acoustic mode identification capability based on genetic algorithms

This letter develops a simple approach of duct mode identification and reconstruction based on genetic algorithms, which can extend the azimuthal mode order range compared to the conventional method based on the (spatial) discrete Fourier transform. The underlying principle is reconstructing the dominant mode from the modal identification forward model through optimization by exploiting the sparsity of the mode amplitude vector. The performance is experimentally demonstrated for detections of one and two azimuthal modes under noisy conditions with nondominant modes. Overall, the proposed genetic-algorithm-based framework for solving acoustic inverse problems is beneficial to duct acoustic testing, particularly design evaluations of fan blades and acoustic liners for aeroengines.