Good Reconstruction Research Articles

Coefficient-Shuffled Variable Block Compressed Sensing for Medical Image Compression in Telemedicine Systems

Medical professionals primarily utilize medical images to detect anomalies within the interior structures and essential organs concealed by the skeletal and dermal layers. The primary purpose of medical imaging is to extract image features for the diagnosis of medical conditions. The processing of these images is indispensable for evaluating a patient’s health. However, when monitoring patients over extended periods using specific medical imaging technologies, a substantial volume of data accumulates daily. Consequently, there arises a necessity to compress these data in order to remove duplicates and speed up the process of acquiring data, making it appropriate for effective analysis and transmission. Compressed Sensing (CS) has recently gained widespread acceptance for rapidly compressing images with a reduced number of samples. Ensuring high-quality image reconstruction using conventional CS and block-based CS (BCS) poses a significant challenge since they rely on randomly selected samples. This challenge can be surmounted by adopting a variable BCS approach that selectively samples from diverse regions within an image. In this context, this paper introduces a novel CS method that uses an energy matrix, namely coefficient shuffling variable BCS (CSEM-VBCS), tailored for compressing a variety of medical images with balanced sparsity, thereby achieving a substantial compression ratio and good reconstruction quality. The results of experimental evaluations underscore a remarkable enhancement in the performance metrics of the proposed method when compared to contemporary state-of-the-art techniques. Unlike other approaches, CSEM-VBCS uses coefficient shuffling to prioritize regions of interest, allowing for more effective compression without compromising image quality. This strategy is especially useful in telemedicine, where bandwidth constraints often limit the transmission of high-resolution medical images. By ensuring faster data acquisition and reduced redundancy, CSEM-VBCS significantly enhances the efficiency of remote patient monitoring and diagnosis.

High dynamic range land wavefield reconstruction from randomized acquisition

Compressive sensing (CS) is an alternative to regular Shannon sampling that captures similar information from reduced measurements. It relies on randomized sampling patterns and a sparse data representation to reconstruct the regularly sampled object. CS is an important ingredient in affordable seismic acquisition, which can lead to improvements in the near-surface mapping and noise suppression for land data. However, the near surface traps most of the source-generated energy, resulting in data that are rich in high-wavenumber content and have amplitudes spanning several orders of magnitude. When dealing with such high dynamic range nonstationary data, the Fourier domain is not optimal for providing a sparse representation — a necessary condition for successful application of CS. In contrast, a discrete complex wavelet transform can localize high-energy features, has good directional selectivity, and is near-shift invariant. Combined, these properties allow complex wavelets to represent detail-rich wavefields in a compact form. To leverage these features and achieve good CS reconstructions, we develop a scale- and orientation-dependent iterative soft thresholding (IST) scheme for reconstructing high dynamic range wavefields. Our approach requires little parameterization, is easy to implement, and is robust to reconstructed wavefield sampling grid and dynamic range. We test IST on different wavefields with randomly missing traces and compare the data reconstructions with the spectral projected gradient solver and projection onto convex sets. We quantify the reconstructions by a direct comparison of Fourier coefficients between fully sampled and reconstructed wavefields. Taking log10 of Fourier coefficients prior to computing the quality metric deemphasizes the importance of magnitude match while highlighting Fourier coefficient support accuracy, which usually translates into good structural fidelity of reconstructed data. We find that IST performs consistently among all examples, yielding good phase match while performing gentle denoising.

Fast and Compact Partial Differential Equation (PDE)-Based Dynamic Reconstruction of Extended Position-Based Dynamics (XPBD) Deformation Simulation

Dynamic simulation is widely applied in the real-time and realistic physical simulation field. How to achieve natural dynamic simulation results in real-time with small data sizes is an important and long-standing topic. In this paper, we propose a dynamic reconstruction and interpolation method grounded in physical principles for simulating dynamic deformations. This method replaces the deformation forces of the widely used eXtended Position-Based Dynamics (XPBD), which are traditionally derived from the gradient of the energy potential defined by the constraint function, with the elastic beam bending forces to more accurately represent the underlying deformation physics. By doing so, it establishes a mathematical model based on dynamic partial differential equations (PDE) for reconstruction, which are the differential equations involving both the parametric variable u and the time variable t. This model also considers the inertia forces caused by acceleration. The analytical solution to this model is then integrated with the XPBD framework, built upon Newton’s equations of motion. This integration reduces the number of design variables and data sizes, enhances simulation efficiency, achieves good reconstruction accuracy, and makes deformation simulation more capable. The experiment carried out in this paper demonstrates that deformed shapes at about half of the keyframes simulated by XPBD can be reconstructed by the proposed PDE-based dynamic reconstruction algorithm quickly and accurately with a compact and analytical representation, which outperforms static B-spline-based representation and interpolation, greatly shortens the XPBD simulation time, and represents deformed shapes with much smaller data sizes while maintaining good accuracy. Furthermore, the proposed PDE-based dynamic reconstruction algorithm can generate continuous deformation shapes, which cannot be generated by XPBD, to raise the capacity of deformation simulation.

Inverse demand tracking in transportation networks

AbstractThis paper deals with the reconstruction of the desired demand in an optimal control problem, stated over a tree-shaped transportation network which is governed by a linear hyperbolic conservation law. As desired demands typically undergo fluctuations due to seasonality or unexpected events making short-term adjustments necessary, such an approach can exemplary be used for forecasting from past data. We suggest to model this problem as a so-called inverse optimal control problem, i.e., a hierarchical optimization problem whose inner problem is the optimal control problem and whose outer problem is the reconstruction problem. In order to guarantee the existence of solutions in the function space framework, the hyperbolic conservation law is interpreted in weak sense allowing for control functions in Lebesgue spaces. For the computational treatment of the model, we transfer the hierarchical problem into a nonsmooth single-level one by plugging the uniquely determined solution of the inner optimal control problem into the outer reconstruction problem before applying techniques from nonsmooth optimization. Some numerical experiments are presented to visualize various features of the model including different types of noise in the demand and strategies of how to observe the network in order to obtain good reconstructions of the desired demand.

A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs

A new model called the Transformer-Unet Generative Adversarial Network (TUGAN) is proposed for super-resolution reconstruction of digital elevation models (DEMs). Digital elevation models are used in many fields, including environmental science, geology and agriculture. The proposed model uses a self-similarity Transformer (SSTrans) as the generator and U-Net as the discriminator. SSTrans, a model that we previously proposed, can yield good reconstruction results in structurally complex areas but has little advantage when the surface is simple and smooth because too many additional details have been added to the data. To resolve this issue, we propose the novel TUGAN model, where U-Net is capable of multilayer jump connections, which enables the discriminator to consider both global and local information when making judgments. The experiments show that TUGAN achieves state-of-the-art results for all types of terrain details.

Dimensional effect of oxide-derived Cu electrocatalysts to reduce CO2 into multicarbon compounds

Oxide-derived Cu (OD-Cu) are the most promising catalysts for multicarbon formation via CO2 electroreduction (CO2ERR), yet facing structural/compositional reconstruction trends during CO2ERR. Nanocatalysts with specific mesoscopic morphology are more resistant to reconstruction, which would benefit systematical exploration of dimensional effects on CO2ERR. Herein, nanostructured OD-Cu catalysts of 1D nanowires (NWs), 2D nanosheets (NSs) and 3D nanoflowers (NFs) with good reconstruction resistance during CO2ERR are fabricated. Their well-preserved dimensional structures lead to altered Cu–O coordination, which is intensively investigated via X-ray absorption fine structure spectroscopy, resulting in distinctive CO2ERR performances. OD-Cu NWs catalyst with the highest Cu–O coordination possesses an impressive 77.7 % C2+ faradaic efficiency. Further in situ Fourier-transformed infrared spectroscopy and density functional theory calculations suggest more favorable asymmetric C–C coupling over the highest Cu–O coordination surface of OD-Cu NWs. This work provides new insights on developing reconstruction-resistant OD-Cu catalysts for value-added chemicals from advanced CO2 conversion.

Electrocatalytic CH4 production from CO2 by SiO2-induced amorphous CuOx

CH4, as an important clean fuel and hydrogen carrier, its production from CO2 driven by renewable energy could simultaneously achieve energy supply, CO2 emission reduction, and renewable energy consumption, which is of great significance. Electrocatalytic CH4 production relies on high-valence Cu species for realizing multielectron reduction, while facing inadequate performance stability due to the reconstruction tendency of Cu. Herein, an amorphous CuOx-SiO2 catalyst (amorCuOx-SiO2) is synthesized via an electrostatic interaction strategy to construct stable Cu2+ active sites for efficient electrocatalytic CH4 production. AmorCuOx-SiO2 shows a good reconstruction resistance during electroreduction, confirmed via X-ray absorption spectroscopy studies, endowing a 63.7% CH4 faradaic efficiency at 500 mA cm−2, which is 7.8 folds of crystalized CuOx-SiO2 (crysCuOx-SiO2). Theoretical calculations revealed that stable Cu2+ active sites enhanced *CO adsorption and were more susceptible for further hydrogenation, thus promoting the CO2-to-CH4 conversion. Our findings provide a feasible strategy for developing reconstruction-resistant electrocatalysts for advanced CH4 production performance.

Bristlecone Pine Maximum Latewood Density as a Superior Proxy for Millennium‐Length Temperature Reconstructions

AbstractBristlecone pine (Pinus longaeva) (PILO) trees exhibit exceptional longevity. Their tree‐ring width (TRW) series offer valuable insights into climatic variability. Maximum latewood density (MXD) typically correlates better with temperature variations than TRW, yet PILO MXD records are non‐existent due to methodological challenges related to tree‐ring structure. Here, we used an X‐ray Computed Tomography (X‐ray CT) toolchain on 51 PILO cores from the California White Mountains to build a chronology that correlates significantly (r = 0.66, p < 0.01) with warm‐season (March‐September) temperature over a large spatial extent. This led to the first X‐ray CT‐based temperature reconstruction (1625–2005 CE). Good reconstruction skill (RE = 0.51, CE = 0.32) shows that extending MXD records across the full length of the PILO archive could yield a robust warm‐season temperature proxy for the American Southwest over millennia. This breakthrough opens avenues for measuring MXD in other challenging conifers, increasing our understanding of past climate further, particularly in lower latitudes.

Compression and encryption for remote sensing image based on PSO-BP and 2D-MCCM

In response to the large size of remote sensing images and the limitations of existing image compression and encryption algorithms, this paper proposes a novel compression and encryption algorithm. The proposed algorithm utilizes a new type of memristive chaotic mapping in combination with PSO-BP neural networks and multi-threaded parallelism. Specifically, the proposed novel two-dimensional memristive chaotic mapping involves a combination of new memristors based on HP memristors and Cubic chaotic mapping. Compared to existing chaotic systems, this method exhibits stronger randomness and hyperchaotic characteristics. Additionally, to improve the reconstruction accuracy of compressed images, a traditional BP neural network with an added hidden layer is combined with the PSO algorithm for image compression and reconstruction. Furthermore, to enhance the encryption efficiency of remote sensing images, a multi-threaded parallel encryption method is employed, enabling simultaneous permutation within and among threads. Experimental results demonstrate that the proposed algorithm achieves good compression reconstruction accuracy, excellent encryption performance, and resistance to attacks.

Visually secure traffic image encryption scheme using new two-dimensional Sigmoid-type memristive chaotic map and Laguerre transform embedding

In intelligent transportation system, unprotected bare data transmission faces serious security threats and challenges. To this end, this paper proposes a visually secure traffic image encryption scheme that combines a newly designed two-dimensional Sigmoid-type memristive chaotic map (2D-SMCM) with two-dimensional compressive sensing (2D-CS) and Laguerre transform (LT) embedding to provide services for secure transmission of private images. Specifically, first, the 2D-SMCM is used to generate pseudo-random sequences for subsequent compression, encryption and hiding operations. Second, the 2D-CS is utilized to compress the plain image to reduce the amount of data transmission. Then, encryption is completed by modifying the data values and their positions through index permutation and bidirectional diffusion. Finally, the encrypted data is embedded in the LT-processed public carrier medium for covert transmission. Experiments and performance analysis illustrate that the proposed scheme has good security, imperceptibility and reconstruction performance, with the average PSNRs of the cipher images and decrypted secret images up to 45.90 dB and 34.85 dB, respectively, using 500 grayscale images from the database BOWS2.

Reconstruction of inhomogeneous media by an iteration algorithm with a learned projector

This paper is concerned with the inverse problem of reconstructing an inhomogeneous medium from the acoustic far-field data at a fixed frequency in two dimensions. This inverse problem is severely ill-posed (and also strongly nonlinear), and certain regularization strategy is thus needed. However, it is difficult to select an appropriate regularization strategy which should enforce some a priori information of the unknown scatterer. To address this issue, we plan to use a deep learning approach to learn some a priori information of the unknown scatterer from certain ground truth data, which is then combined with a traditional iteration method to solve the inverse problem. Specifically, we propose a deep learning-based iterative reconstruction algorithm for the inverse problem, based on a repeated application of a deep neural network and the iteratively regularized Gauss–Newton method (IRGNM). Our deep neural network (called the learned projector in this paper) mainly focuses on learning the a priori information of the shape of the unknown contrast with a normalization technique in the training processes and is trained to act like a projector which is helpful for projecting the solution into some feasible region. Extensive numerical experiments show that our reconstruction algorithm provides good reconstruction results even for the high contrast case and has a satisfactory generalization ability.

A novel intervention for wound bed preparation in severe extremity trauma: Highly concentrated carbon dioxide bathing

IntroductionIn severe extremity trauma involving large tissue defects, early closure (e.g., free-flap surgery) of the defects is an essential step for good functional reconstruction; however, in some cases, early closure may be difficult. Highly concentrated carbon dioxide bathing, used to improve blood flow in ischemic limbs and skin ulcers, can also be applied in wound bed preparation for severe limb trauma. Patients and MethodsThe three cases in this study required an average of 13 weeks of highly concentrated carbonated bathing, which led to significantly better wound bed preparation, even in the exposed bone and tendon regions. ResultsWe successfully achieved good functional limb reconstruction in patients with deep burns and severe open fractures by reducing wound infection and facilitating good wound bed preparation. ConclusionsHighly concentrated carbon dioxide bathing was sufficient to prevent frequent wound infections, even in severe extremity trauma involving large soft-tissue defects such as deep crush burns and Gustilo Anderson classification ≥3b open fractures of the extremities. To our knowledge, such interventions have not been reported in the past and are valuable as new procedures for wound bed preparation in severe extremity trauma from both cost and wound infection control perspectives.

Identifiability of spatiotemporal tissue perfusion models

Objective. Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model. Approach. For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth. Main results. Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters. Significance. These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.

Scapholunate Ligament Reconstruction With Internal Brace Augmentation Techniques for Chronic Scapholunate Dissociation: A Clinical Follow-up Study.

The scapholunate ligament is the most important stabilizer of the scapholunate articulation. The management of chronic irreversible injuries of this ligament in the absence of preexisting arthritis of the wrist joint remains controversial. Recently, surgeons introduced a novel surgical technique using an internal brace (IB). Several biomechanical studies on this technique have been conducted using cadavers; however, very few studies have discussed the results in detail in actual clinical practice. Therefore, herein, we investigated the radiological and functional results of patients who underwent IB augmentation as a treatment for chronic scapholunate dissociation. This retrospective study was conducted from April 2018 to May 2022. Twenty-two patients with chronic scapholunate dissociation were treated using the IB augmentation technique, of whom 17 were followed-up for at least 1 year. Radiological results, including scapholunate distance, scapholunate angle, and radioscaphoid angle, were collected. Furthermore, clinical parameters, such as the visual analog scale (preoperative and at final follow-up), the Disabilities of the Arm, Shoulder, and Hand scores (preoperatively and at 3, 6, and 12 months postoperatively), and Mayo wrist scores (preoperative and at final follow-up), were measured. The scapholunate distance increased significantly in the affected wrist compared to the unaffected wrist, which improved after reconstruction in all wrist positions ( P < 0.05). Compared to the unaffected wrist, the scapholunate angle increased significantly in all positions ( P < 0.05) except for extension ( P = 0.535) and improved after reconstruction in all wrist positions. The radioscaphoid angle significantly increased compared to the angle of the unaffected wrist in all positions ( P < 0.05) except for extension ( P = 0.602) and clenched fist ( P = 0.556). This angle improved after reconstruction in all wrist positions except for extension ( P = 0.900). The visual analog scale score (7-2, preoperatively and at final follow-up) and Mayo wrist score (53-82, preoperatively and at final follow-up) improved after surgery. The Disabilities of the Arm, Shoulder, and Hand scores also improved after surgery (68, 53, 30, 7, preoperatively and at 3, 6, and 12 months postoperatively). This study revealed that scapholunate ligament reconstruction using an autologous tendon and suture tape is a good reconstruction technique that can improve clinical symptoms and radiographic parameters with a shorter operation time and fewer complications than other reconstruction methods.

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural networks (CNN), however, most of the models cannot effectively combine global and local information and are also easy to ignore the correlation between different hierarchical feature information. To address these problems, this study proposes a multi-level feature interactive image super-resolution network, which is constructed by the convolutional units inspired by nonlinear spiking mechanism in nonlinear spiking neural P systems, including shallow feature processing, deep feature extraction and fusion, and reconstruction modules. The different omni domain self-attention blocks are introduced to extract global information in the deep feature extraction and fusion stage and formed a feature enhancement module having a Transformer structure using a novel convolutional unit for extracting local information. Furthermore, to adaptively fuse features between different hierarchies, we design a multi-level feature fusion module, which not only can adaptively fuse features between different hierarchies, but also can better interact with contextual information. The proposed model is compared with 16 state-of-the-art or baseline models on five benchmark datasets. The experimental results show that the proposed model not only achieves good reconstruction performance, but also strikes a good balance between model parameters and performance.

Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions.

Fluorescence molecular tomography based on an online maximum a posteriori estimation algorithm

Fluorescence molecular tomography (FMT) is a non-invasive, radiation-free, and highly sensitive optical molecular imaging technique for early tumor detection. However, inadequate measurement information along with significant scattering of near-infrared light within the tissue leads to high ill-posedness in the inverse problem of FMT. To improve the quality and efficiency of FMT reconstruction, we build a reconstruction model based on log-sum regularization and introduce an online maximum a posteriori estimation (OPE) algorithm to solve the non-convex optimization problem. The OPE algorithm approximates a stationary point by evaluating the gradient of the objective function at each iteration, and its notable strength lies in the remarkable speed of convergence. The results of simulations and experiments demonstrate that the OPE algorithm ensures good reconstruction quality and exhibits outstanding performance in terms of reconstruction efficiency.

Multimedia healthcare cloud personal archives security system based on compressed sensing and multi-image encryption

Cloud services in the medical process have become commonplace, and the semi-trusted nature of the healthcare cloud makes security in the communication process particularly important. This paper summarizes the four major pain points in the process of collecting, storing in the cloud, and communicating information in the medical process, and proposes a multimedia medical cloud information security system. The pressure of channel transmission is reduced by compressing two-dimensional information images with compressed sensing (CS) technology. The multimedia information type conversion technology can convert multimedia information into 2D images, and the converted 2D images are merged into a multi-image cube, which realizes the “one-person-one-cube” medical information archives management mode. A chaotic multi-image encryption (MIE) based on Identity Mutual Recognition Keys (IMRKs) is designed to encrypt the cube by transmitting it to the edge cloud, which ensures security and solves the problem of unauthorized use of medical information. The 3D Positioning Fifth Generation Message-Digest (3DPMD5) tamper determination algorithm is utilized to generate 32-bit hexadecimal numbers that are transmitted back to the medical terminal to achieve cloud tamper resistance. It is able to be demonstrated from the simulation results that the system has good reconstruction results, relatively safe performance, and high encryption and decryption efficiency.

IoT-enabled image captioning with deep learning for healthcare domain

&lt;p&gt;The ever-increasing volume of medical images greatly strains clinicians who are in the process of reviewing it and writing reports. It would be more efficient and cost-effective if an image captioning model could automatically create report drafts from matching photos, thereby relieving physicians from this tedious work. The Internet of things (IoT) has switched its emphasis from its initial binary concept to that of the Internet of multimedia things (IoMT) because of the explosive rise of multilingual-on-demand data in various sound, footage, picture forms. This work proposed a deep learning-based image caption network (DL-ICN) for healthcare domain. The work originality is shown using DL to identify various class labels of the patient X-ray and ECG images. With the help of bilateral encoder representations from transformers (BERT) method for captioning pictures, a detailed written summary of a person’s medical picture may be generated automatically. Results of simulations showed that the proposed model achieved good compression performance, good quality reconstruction and good classification results for image captioning.&lt;/p&gt;

Modified mini-SLET for treatment of complex cases in pterygium surgery

The major problem associated with the benign but destructive growing pterygium is the high recurrence rate. Anew surgical technique to lower recurrence rates is minor ipsilateral simple limbal epithelial transplantation (mini-SLET), where the regeneration potential of limbal stem cells is used in combination with amniotic membrane transplantation (AMT) for surgical reconstruction. The aim of this study is to assess the surgical outcome of the mini-SLET technique with tenonectomy, mitomycinC, and AMT as used in the authors' hospital. Atotal of 16eyes from 15patients undergoing mini-SLET after surgical pterygium removal with tenonectomy, mitomycinC, and AMT were analyzed retrospectively. Two different groups of pterygia were enrolled: group1 included recurrent pterygia (n = 10) and group2 comprised primary large pterygia such as double-head pterygia (n = 6). In addition to assessment of best corrected visual acuity and compete ophthalmological examination, preoperative slip-lamp examination with photo documentation served to calculate the corneal size of the pterygium head using VISUPAC software (Zeiss, Oberkochen, Germany). Postoperatively, best corrected visual acuity and slit-lamp examination were routinely evaluated. The surgical outcome was defined by the postoperatively achieved best corrected visual acuity, restoration of the ocular surface, recurrence rate, and rate of postoperative complications. Median follow-up in all patients was 27months; in groups1 and 2 it was 30.7and 25.3months, respectively. No recurrence developed in 15eyes (93.75%). Only one group1 patient (6.25%) suffered arecurrent lesion after 10months. Postoperatively, logMAR visual acuity did not change significantly. During follow-up, complications were limited to one case of early wound dehiscence. Mini-SLET in combination with tenonectomy, mitomycinC, and AMT enables good surgical reconstruction of the ocular surface, and almost complete healing in the sense of restitutio ad integrum is possible. The results of the present study have shown the technique's effectiveness for recurrence prevention.