Optimal Reconstruction Research Articles

Optimal reconstruction methods after distal gastrectomy for gastric cancer: a protocol for a systematic review and network meta-analysis update

BackgroundDistal gastrectomy (DG) is a commonly used surgical procedure for gastric cancer (GC), with three reconstruction methods available: Billroth I, Billroth II, and Roux-en-Y. In 2018, our team published a systematic review to provide guidance for clinical practice on the optimal reconstruction method after DG for GC. However, since then, new evidence from several randomized controlled trials (RCTs) has emerged, prompting us to conduct an updated systematic review and network meta-analysis to provide the latest comparative estimates of the efficacy and safety of the three reconstruction methods after DG for GC.MethodThis systematic review and network meta-analysis update followed the PRISMA-P guidelines and will include a search of PubMed, Embase, and the Cochrane Library for RCTs comparing the outcomes of Billroth I, Billroth II, or Roux-en-Y reconstruction after DG for patients with GC. Two independent reviewers will screen the titles and abstracts based on predefined eligibility criteria, and two reviewers will assess the full texts of relevant studies. The Bayesian network meta-analysis will evaluate various outcomes, including quality of life after surgery, anastomotic leakage within 30 days after surgery, operation time, intraoperative blood loss, major postoperative complications within 30 days after surgery, incidence and severity of bile reflux, and loss of body weight from baseline.Ethics and disseminationThe review does not require ethical approval. The findings of the review will be disseminated through publication in an academic journal, presentations at conferences, and various media outlets.INPLASY registration numberINPLASY2021100060.

Cluster profiles from beyond-the-QE CMB lensing mass maps

Clusters of galaxies, being the largest collapsed structures in the universe, offer valuable insights into the nature of cosmic evolution. Precise calibration of the mass of clusters can be obtained by extracting their gravitational lensing signal on the Cosmic Microwave Background (CMB) fluctuations. We extend and test here the performance achieved on cluster scales by the parameter-free, maximum a posteriori (MAP) CMB lensing reconstruction method, which has been shown to be optimal in the broader context of CMB lensing mass map and power spectrum estimation. In the context of cluster lensing, the lensing signal of other large-scale structures acts as an additional source of noise. We show here that by delensing the CMB fluctuations around each and every cluster, this noise variance is reduced according to expectations. We also demonstrate that the well-known bias in the temperature quadratic estimator in this regime, sourced by the strong non-Gaussianity of the signal, is almost entirely mitigated without any scale cuts. Being statistically speaking an optimal and blind lensing mass map reconstruction, the MAP estimator is a promising tool for the calibration of the masses of clusters.

An Improved Imaging Method Based on Optimal Topographic Imaging Plane Reconstruction for Nonlinear Trajectory SAR

An Improved Imaging Method Based on Optimal Topographic Imaging Plane Reconstruction for Nonlinear Trajectory SAR

Parotid Metastasis from Squamous Cell Carcinoma: Surgical Resection and Microsurgery Reconstruction: a case report

Introduction and importance: Squamous cell carcinoma (SCC) accounts for approximately 20% of nonmelanoma skin cancers, with a significant proportion arising in the head and neck region. Up to 5% of these lesions’ present nodal metastases, particularly to the parotid gland, attributed to the extensive lymph nodes in and around the gland. Treatment typically involves surgical resection, including cervical lymph node dissection, with adjuvant radiation therapy recommended in select cases. Reconstruction using a microsurgery-assisted free anterolateral thigh flap is valuable in complex cases such as SCC with parotid metastasis, providing optimal reconstruction outcomes and improving patient quality of life. Case presentation: We presented a 68-year-old woman with squamous cell carcinoma (SCC) in the left parotid gland and metastasis. The patient had a history of SCC in the left zygomatic region. The parotid lesion exhibited rapid growth and was associated with various symptoms, including eye pain, hearing loss, tinnitus, and headache. Discussion: Treatment involved left extended total parotidectomy, ipsilateral supraomohyoid neck dissection, and reconstruction with a microsurgery-assisted free anterolateral thigh flap. Conclusion: Comprehensive care, from early diagnosis to skilled surgery, is vital for improving outcomes in squamous cell carcinoma, particularly with parotid metastases.

Esophagogastrostomy Versus Double Tract Reconstruction for Laparoscopic Proximal Gastrectomy: Short-term Outcomes Based on Nutritional Parameters and Skeletal Muscle Index.

The optimal reconstruction method for laparoscopic proximal gastrectomy (LPG) remains controversial. The present study aimed to compare short-term outcomes, including assessment of nutritional parameters and skeletal muscle, between two different methods, double-tract reconstruction (DTR) versus esophagogastrostomy (EG). Data from patients who underwent LPG for gastric tumor(s) between 2018 and 2021, were retrospectively analyzed. Patients were divided into two group: DTR (n=11) and EG (n=17). Since 2020, the authors have applied the modified side overlap with fundoplication by Yamashita (mSOFY) method as the EG technique. Compared with DTR, EG was associated with a shorter reconstruction time (p=0.003). Complications of grade ≥3 occurred only in the EG group [n=4 (23.5%)] and the incidence of abnormal endoscopic findings after surgery was numerically higher in the EG group (n=2 vs. n=9; p=0.047). Across virtually all data points on the line graph, the EG group exhibited greater changes in post-discharge nutritional parameters, with Skeletal Muscle Index also demonstrating significant superiority (0.83 vs. 0.89; p=0.045). Among reconstruction methods for LPG, EG demonstrated superiority over DTR in preserving nutritional parameters and skeletal muscle mass. However, further research, including larger cohorts and longer-term follow-up, is necessary to validate this finding.

Dynamical programming for off-the-grid dynamic inverse problems

In this work we consider off-the-grid algorithms for the reconstruction of sparse measures from time-varying data. In particular, the reconstruction is a finite collection of Dirac measures whose locations and masses vary continuously in time. Recent work showed that this decomposition was possible by minimising a convex variational model which combined a quadratic data fidelity with dynamical Optimal Transport. We generalise this framework and propose new numerical methods which leverage efficient classical algorithms for computing shortest paths on directed acyclic graphs. Our theoretical analysis confirms that these methods converge to globally optimal reconstructions. Numerically, we show new examples for unbalanced Optimal Transport penalties, and for balanced examples we are 100 times faster in comparison to the previously known method.

Multi-Channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction.

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of the iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from the intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Optimal Reconstruction of Probabilistic Boolean Networks.

In gene regulatory networks (GRNs), it is important to model gene regulation based on a priori information and experimental data. As a useful mathematical model, probabilistic Boolean networks (PBNs) have been widely applied in GRNs. This article addresses the optimal reconstruction problem of PBNs based on several priori Boolean functions and sampled data. When all candidate Boolean functions are known in advance, the optimal reconstruction problem is reformulated into an optimization problem. This problem can be well solved by a recurrent neural network approach which decreases the computational cost. When parts of candidate Boolean functions are known in advance, necessary and sufficient conditions are provided for the reconstruction of PBNs. In this case, two types of reconstruction problems are further proposed: one is aimed at minimizing the number of reconstructed Boolean functions, and the other one is aimed at maximizing the selection probability of the main dynamics under noises. At last, examples in GRNs are elaborated to demonstrate the effectiveness of the main results.

Front Cover

A novel accelerated model‐based iterative reconstruction strategy for sparse‐view PAT aided by multi‐channel autoencoder priors was proposed. A multi‐channel denoising autoencoder network was designed to learn prior information, which provides constraints for model‐based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The results show that the proposed method can achieve superior sparse‐view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition.For further details please visit the article by Xianlin Song, Wenhua Zhong, Zilong Li, Shuchong Peng, Hongyu Zhang, Guijun Wang, Jiaqing Dong, Xuan Liu, Xiaoling Xu, Qiegen Liu (e202300281)image

Dynamic Computed Tomography Angiography for capturing vessel wall motion: A phantom study for optimal image reconstruction.

Reliably capturing sub-millimeter vessel wall motion over time, using dynamic Computed Tomography Angiography (4D CTA), might provide insight in biomechanical properties of these vessels. This may improve diagnosis, prognosis, and treatment decision making in vascular pathologies. The aim of this study is to determine the most suitable image reconstruction method for 4D CTA to accurately assess harmonic diameter changes of vessels. An elastic tube (inner diameter 6 mm, wall thickness 2 mm) was exposed to sinusoidal pressure waves with a frequency of 70 beats-per-minute. Five flow amplitudes were set, resulting in increasing sinusoidal diameter changes of the elastic tube, measured during three simulated pulsation cycles, using ECG-gated 4D CTA on a 320-detector row CT system. Tomographic images were reconstructed using one of the following three reconstruction methods: hybrid iterative (Hybrid-IR), model-based iterative (MBIR) and deep-learning based (DLR) reconstruction. The three reconstruction methods where based on 180 degrees (half reconstruction mode) and 360 degrees (full reconstruction mode) raw data. The diameter change, captured by 4D CTA, was computed based on image registration. As a reference metric for diameter change measurement, a 9 MHz linear ultrasound transducer was used. The sum of relative absolute differences (SRAD) between the ultrasound and 4D CTA measurements was calculated for each reconstruction method. The standard deviation was computed across the three pulsation cycles. MBIR and DLR resulted in a decreased SRAD and standard deviation compared to Hybrid-IR. Full reconstruction mode resulted in a decreased SRAD and standard deviations, compared to half reconstruction mode. 4D CTA can capture a diameter change pattern comparable to the pattern captured by US. DLR and MBIR algorithms show more accurate results than Hybrid-IR. Reconstruction with DLR is >3 times faster, compared to reconstruction with MBIR. Full reconstruction mode is more accurate than half reconstruction mode.

Optimized quantitative mapping of cardiopulmonary oscillations using hyperpolarized 129 Xe gas exchange MRI: Digital phantoms and clinical evaluation in CTEPH.

The interaction between 129 Xe atoms and pulmonary capillary red blood cells provides cardiogenic signal oscillations that display sensitivity to precapillary and postcapillary pulmonary hypertension. Recently, such oscillations have been spatially mapped, but little is known about optimal reconstruction or sensitivity to artifacts. In this study, we use digital phantom simulations to specifically optimize keyhole reconstruction for oscillation imaging. We then use this optimized method to re-establish healthy reference values and quantitatively evaluate microvascular flow changes in patients with chronic thromboembolic pulmonary hypertension (CTEPH) before and after pulmonary thromboendarterectomy (PTE). A six-zone digital lung phantom was designed to investigate the effects of radial views, key radius, and SNR. One-point Dixon 129 Xe gas exchange MRI images were acquired in a healthy cohort (n = 17) to generate a reference distribution and thresholds for mapping red blood cell oscillations. These thresholds were applied to 10 CTEPH participants, with 6 rescanned following PTE. For undersampled acquisitions, a key radius of was found to optimally resolve oscillation defects while minimizing excessive heterogeneity. CTEPH participants at baseline showed higher oscillation defect + low (32 ± 14%) compared with healthy volunteers (18 ± 12%, p < 0.001). For those scanned both before and after PTE, oscillation defect + low decreased from 37 ± 13% to 23 ± 14% (p = 0.03). Digital phantom simulations have informed an optimized keyhole reconstruction technique for gas exchange images acquired with standard 1-point Dixon parameters. Our proposed methodology enables more robust quantitative mapping of cardiogenic oscillations, potentially facilitating effective regional quantification of microvascular flow impairment in patients with pulmonary vascular diseases such asCTEPH.

Modified Lip Lift Technique for Pediatric Columellar Reconstruction: A Case Report

The nasal columella presents one of the most intricate challenges in facial reconstruction due to its unique contours and limited availability of adjacent tissue. Several surgical techniques exist for successful columellar reconstruction, and the choice of approach depends on factors such as the size of the defect, surgical expertise, and overall esthetic goals. In cases involving substantial columellar defects, or deformities affecting multiple nasal subunits, local or regional flaps are often necessary to achieve optimal reconstruction. In this report, the authors present a case of pediatric columellar reconstruction utilizing a modified local flap technique derived from the upper lip lift used in esthetic surgery for facial rejuvenation and lip enhancement. This method distinguishes itself from previous adaptations by remaining entirely subnasal, without extending beyond the width of the alar base, disrupting the philtrum, or disturbing the orbicularis oris muscle. This technique offers notable advantages for pediatric patients, such as its ability to minimize scarring, prevent additional deformities, and be performed in a single stage. Level of Evidence: Level IV, case study.

Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors.

Photoacoustic tomography (PAT) commonly works in sparse view due to data acquisition limitations. However, reconstruction suffers from serious deterioration (e.g., severe artifacts) using traditional algorithms under sparse view. Here, a novel accelerated model-based iterative reconstruction strategy for sparse-view PAT aided by multi-channel autoencoder priors was proposed. A multi-channel denoising autoencoder network was designed to learn prior information, which provides constraints for model-based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The performance of the proposed method was evaluated using blood vessel simulation data and experimental data. The results show that the proposed method can achieve superior sparse-view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition (e.g., 32 projections) compared with the U-Net method, with an improvement of 48% in PSNR and 12% in SSIM for in vivo experimental data.

Point cloud enhancement optimization and high-fidelity texture reconstruction methods for air material via fusion of 3D scanning and neural rendering

In order to realize the digital management and manufacturing of air material, the methods of 3D point cloud enhancement optimization and high-fidelity texture reconstruction in entity digitization of air material based on the fusion of 3D scanning and neural rendering are proposed. In this paper, an automatic data acquisition system is designed and built, and the acquired training images are enhanced. Then the Yolov8 segmentation model and CascadePSP boundary refinement model are combined for training to realize foreground segmentation and background removal of air material images. In this paper, a 3D point cloud enhancement optimization method is proposed, which combines the point cloud obtained by binocular structured light scanner and the point cloud reconstructed by Colmap algorithm for registration and fusion. By combining the coarse registration of PointNetLK network with the fine registration of ICP algorithm, a more complete and high-quality dense point cloud is generated, and the effects of illumination conditions and image quantity on point cloud reconstruction is studied experimentally. Using the optimized point cloud model and random perspective enhancement (RPE) method, the neural rendering model NeuS is improved, which improves the 3D surface texture reconstruction quality and the sight extrapolation effect. In order to verify the effectiveness of the proposed method, comparison and ablation experiments are carried out in this paper. The experimental results show that point cloud enhancement optimization and RPE have a good effect on improving the model, and the model performance has been greatly improved.

Breast reconstruction after mastectomy in patients with obesity: a narrative review.

Breast reconstruction in patients with obesity presents numerous challenges, both in terms of surgical technique and post-operative complication management. As breast reconstruction techniques continue to evolve, the armamentarium of reconstructive options for patients with obesity has vastly expanded. Options now include immediate or delayed, implant-based, autologous, or hybrid reconstruction. Determining the optimal breast reconstruction in this complex population requires nuanced and experienced decision-making. A literature search was conducted to identify studies assessing breast reconstruction considerations in patients with obesity. The search was performed on PubMed and was limited to English language studies published between 1990 and 2023. Primary studies, case reports, chart reviews, and qualitative studies were included. Additional articles were identified for inclusion based on a review of references, as well as a web-based search, to identify additional studies that were not captured with the primary search strategy. This narrative review article summarizes the current literature available to guide surgeons in breast reconstruction in patients with obesity. The advancements in oncologic surgery and breast reconstruction techniques have expanded available surgical options, including immediate or delayed implant-based, autologous, or hybrid breast reconstruction. Each approach has its unique advantages, disadvantages, and surgical considerations. Despite the challenges, patients with obesity can achieve favorable aesthetic outcomes through careful assessment of comorbidities and expectation management.

An Active Detection Strategy Based on Dimensionality Reduction for False Data Injection Attacks in Cyber-Physical Systems

In recent years, different intelligent and undetectable cyber-attacks against networked control systems (e.g., replay and covert attacks) have been investigated. In this paper, we design a novel control architecture that prevents their existence. In particular, first, we propose to encode the sensor outputs into a randomly changing lower-dimensional space obtained by means of principal component analysis. Such a transformation ensures optimal information reconstruction on the controller's side, and it prevents the attacker from accessing the original sensor measurements, nullifying the possibility of perfect stealthy attacks. Then, on the controller's side, we design a passive Gaussian anomaly detector that leverages the output of an unknown input observer ad-hoc designed to estimate the system's states and unknown inputs simultaneously. It is formally shown that the proposed detection strategy is able to discover the presence of replay and covert attacks. Simulation results obtained considering a quadruple water tank system confirm the capability of the developed architecture.

Optimization of the Reconstruction Settings for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lungs

Photon-counting detector computed tomography (PCD-CT) yields improved spatial resolution. The combined use of PCD-CT and a modern iterative reconstruction method, known as quantum iterative reconstruction (QIR), has the potential to significantly improve the quality of lung CT images. In this study, we aimed to analyze the impacts of different slice thicknesses and QIR levels on low-dose ultra-high-resolution (UHR) PCD-CT imaging of the lungs. Our study included 51 patients with different lung diseases who underwent unenhanced UHR-PCD-CT scans. Images were reconstructed using three different slice thicknesses (0.2, 0.4, and 1.0 mm) and three QIR levels (2–4). Noise levels were determined in all reconstructions. Three raters evaluated the delineation of anatomical structures and conspicuity of various pulmonary pathologies in the images compared to the clinical reference reconstruction (1.0 mm, QIR-3). The highest QIR level (QIR-4) yielded the best image quality. Reducing the slice thickness to 0.4 mm improved the delineation and conspicuity of pathologies. The 0.2 mm reconstructions exhibited lower image quality due to high image noise. In conclusion, the optimal reconstruction protocol for low-dose UHR-PCD-CT of the lungs includes a slice thickness of 0.4 mm, with the highest QIR level. This optimized protocol might improve the diagnostic accuracy and confidence of lung imaging.

Unveiling and utilizing the reconstructing dynamics on nanoporous Ag-Bi for CO2 electroreduction

The reconstruction of catalysts during electrochemical reactions has become an unavoidable issue for higher catalytic activity and the intrinsic mechanism. Herein the reconstruction that amorphous Bi shell forming onto nanoporous AgBi during CO2ER is regulated by potential, and the optimal reconstruction potential is confirmed to be − 1.05 V. The surface reconfiguration and catalytic property conversion along with the reconstruction are identified via in-situ electrochemical and spectroscopic characterizations. The reconstructed catalyst achieves a Faradaic efficiency of 94 %, partial current density of 19.75 mA•cm−2, and wonderful durability, thanks to the special nanoporous core-shell structure with abundant defects and a efficient electron transport. Furthermore, computational simulation manifests that Bi atoms on the ligament surface are more capable to activating CO2 than Ag atoms. This investigation introduces one method to elucidate the real state of catalysts and proposes a novel strategy for designing electrochemical catalysts with high activity and durability by atomic scale reconstruction.

Effective selection of shears in variable lateral shearing holography.

The efficiency of reconstruction of complex wavefields in digital holography through shear interferometry has a direct correlation with the shears selected for image acquisition. Although studies to investigate the effect of shears have shown correlations between the selected shear set and the spatial and frequency contents of the reconstructed complex wavefield, to our best knowledge, not much information is available to provide a guide on how to select these shears optimally and what factors to be considered during this selection procedure. In this paper, we study the effect of shear parameters on the phase error through a series of simulations using a synthetic object wavefield and provide a range of shear parameters for optimal reconstruction. Further, we correlated the data by comparing the results with corresponding frequency information density maps.

Technical Strategy for Pancreatic Body Cancers: A Raison d'etre of Distal Pancreatectomy with Portal Resection.

Advancements in multiagent chemotherapy have expanded the surgical indications for pancreatic cancer. Although pancreaticoduodenectomy (PD) with portal vein resection (PVR) has become widely adopted, distal pancreatectomy (DP) with PVR remains rarely performed because of its technical complexity. This study was designed to assess the feasibility of DP-PVR compared with PD-PVR for pancreatic body cancers, with a focus on PV complications and providing optimal reconstruction techniques when DP-PVR is necessary. A retrospective review was conducted on consecutive pancreatic body cancer patients who underwent pancreatectomy with PVR between 2005 and 2020.An algorithm based on the anatomical relationship between the arteries and PV was used for optimal surgical selection. Among 119 patients, 32 underwent DP-PVR and 87 underwent PD-PVR. Various reconstruction techniques were employed in DP-PVR cases, including patch reconstruction, graft interposition, and wedge resection. The majority of PD-PVR cases involved end-to-end anastomosis. The length of PVR was shorter in DP-PVR (25 vs. 40 mm; p < 0.001). Although Clavien-Dindo ≥3a was higher in DP-PVR (p = 0.002), inpatient mortality and R0 status were similar. Complete PV occlusion occurred more frequently in DP-PVR than in PD-PVR (21.9% vs. 1.1%; p < 0.001). A cutoff value of 30 mm for PVR length was determined to be predictive of nonrecurrence-related PV occlusion after DP-PVR. The two groups did not differ significantly in recurrence or overall survival. DP-PVR had higher occlusion and postoperative complication rates than PD-PVR. These findings support the proposed algorithm and emphasize the importance of meticulous surgical manipulation when DP-PVR is deemed necessary.