Imaging Mapping Research Articles

PEAKFORCE QUANTITATIVE NANOMECHANICAL MAPPING FOR SURFACE ENERGY CHARACTERIZATION ON THE NANOSCALE: A MINI-REVIEW

Surface energy characterization is important to design the fabrication process of reliable electronic devices. Surface energy is influenced by various factors such as surface functionality and morphology. Owing to the high surface-to-volume ratio, surface energy at the nanoscale can be different from that of the bulk. However, the conventional methods for characterization of surface energy such as a sessile drop or Washburn methods cannot be used for nanoscale samples, owing to the limited volume for characterization. Recently, surface energy characterization on the nanoscale using atomic force microscopy (AFM) with Peak Force-Quantitative Nanomechanical Mapping (PF-QNM) imaging mode has been proposed. The nanoscale AFM tips measure the adhesion forces at the nanoscale, which are converted into surface energy through pre-calibrated curves. Successful surface energy characterization of nanoscale metal samples using AFM with the PF-QNM method has been reported previously. This mini-review discusses the recent progress on surface energy characterization at the nanoscale using AFM with the PF-QNM method. The fundamentals of the PF-QNM mode are introduced, and the results of surface energy characterization are summarized. Consequently, the future research direction for surface energy characterization at the nanoscale is discussed.

A 76-m per pixel global color image dataset and map of Mars by Tianwen-1

A 76-m per pixel global color image dataset and map of Mars by Tianwen-1

Experimental determination of Fe–Mg interdiffusion in orthopyroxene as a function of Fe content

We have measured the dependence of the Fe–Mg interdiffusion coefficient, DFe-Mg, on the ferrosilite component in orthopyroxene, which so far has not been experimentally calibrated. Diffusion couples, consisting of approximately 1 µm thin-films were deposited by pulsed laser ablation on orthopyroxene crystals of En91Fs9 composition. Diffusion experiments were carried out at atmospheric pressure in vertical gas mixing furnaces (CO-CO2) at temperatures between 950 and 1100 °C at constant fO2 = 10–7 Pa. Using a focused ion beam-scanning electron microscope (FIB-SEM), FIB-foils were cut from diffusion couples before and after annealing. Diffusion profiles were extracted by using combined backscattered electron (BSE) imaging and energy dispersive X-ray (EDXS) mapping on FIB-foils which allowed to resolve concentration gradients within 20 nm. The microstructure of the diffusion experiments was investigated using transmission electron microscopy (TEM). Using this method, we obtained the first experimentally determined data on the dependence of DFe-Mg on the ferrosilite content in orthopyroxene at different temperatures, appearing to increase with increasing a temperature. For the temperature range (950 – 1100 °C), log fO2 = -7 Pa, along [001] in the composition Fs9, the log DFe-Mg yields the following Arrhenius equation:DFe-Mg[m2/s]=3.8·10-9exp[-261.07±24[kJ/mol]/(R/T[K])]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$D_{Fe - Mg} [m^2 /s] = 3.8 \\cdot 10^{ - 9} \\exp [ - 261.07 \\pm 24[kJ/mol]/(R/T[K])]$$\\end{document}The effect of XFe on DFe-Mg, given by D(XFe)=D(XFe=0.09)·10m(XFe-0.09),\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$D(X_{Fe} ) = D(X_{Fe} = 0.09) \\cdot {10}^{m (X_{Fe} - 0.09)},$$\\end{document} can be calculated by the following parameterization where m follows a linear regression of m on temperature: m=-2.711·104/T(K)+23.5408\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m = - 2.711 \\cdot 10^4 /T(K) + 23.5408$$\\end{document}By considering the DFe-Mg dependence on XFe, the timescales of natural processes obtained from modelling the compositional zoning of natural crystal may considerably differ from previously estimated timescales.

The value of magnetic resonance diffusion weighted imaging sequence and T2 mapping imaging technology in evaluating knee joint defects

The value of magnetic resonance diffusion weighted imaging sequence and T2 mapping imaging technology in evaluating knee joint defects

High-resolution intraoperative infrared thermography for the functional imaging and tumor resection maximization of a low-grade glioma: Technical case report

Currently, it is widely recognized the role of the extent of resection on overall and progression-free survival for gliomas surgery. Brain mapping and intraoperative image guidance are generally required to attain such an objective. Infrared Thermography (IRT) has a potential role in the identification of surgical margins, but it has not been consistently assessed in the literature. We aim to demonstrate the use of intraoperative IRT to detect superficial and deep tumoral margins of low-grade glioma, to identify adjacent functional motor cortex, and to maximize surgical resection in a 45-year-old lady with a long history of partial and secondary generalization seizures.

Study on the application of 3D imaging based on the γ-photon silk thread method for visualisation and detection of flow fields

As a flow display technique, the silk thread method can clearly show the state of the flow field. However, displaying the state of the flow field in industrial closed metal cavities is impossible, and results of the traditional silk thread method fail to present depth information. To address these limitations, we propose a display method based on γ-photon silk thread 3D imaging. Firstly, a flow field visualization experimental platform applicable for industrial closed metal cavity was designed. Then, aiming at the shortcomings of insufficient γ-photon scanning data and limited imaging quality in flow field visualization, an adaptive sinogram interpolation-iterative filtering reconstruction algorithm was proposed to expand the data via the adaptive interpolation processing of the sinogram in image reconstruction. Beltrami filtering was also embedded in the iterative algorithm to effectively reduce noise and artifacts in the imaging image. Finally, the internal flow field inspection of the NACA0018 airfoil and the industrial confinement turbine were used as examples to visualise the wing upper surface flow and turbine blade passage secondary flow, respectively, and the results were consistent with those of the numerical simulation analysis. As the angle of attack of the wing increased, laminar flow separation occurred, and the silk thread structure state changed to the point where the aircraft stalled. After the imaging map analysis, the stall angle ranged between (15°,19°), which is consistent with the simulation results. In the turbine blade passage secondary flow detection, the imaging map formed a horseshoe vortex with airflow separation at the central saddle point and silk threads clearly presenting the structural state of the flow field along different directions.

Infrared and visible image fusion in a rolling guided filtering framework based on deep feature extraction

AbstractTo preserve rich detail information and high contrast, a novel image fusion algorithm is proposed based on rolling-guided filtering combined with deep feature extraction. Firstly, input images are filtered to acquire various scales decomposed images using rolling guided filtering. Subsequently, PCANet is introduced to extract weight maps to guide base layer fusion. For the others layer, saliency maps of input images are extracted by a saliency measure. Then, the saliency maps are optimized by guided filtering to guide the detail layer fusion. Finally, the final fusion result are reconstructed by all fusion layers. The experimental fusion results demonstrate that fusion algorithm in this study obtains following advantages of rich detail information, high contrast, and complete edge information preservation in the subjective evaluation and better results in the objective evaluation index. In particular, the proposed method is 16.9% ahead of the best comparison result in the SD objective evaluation index.

Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI

PurposeTo compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. Materials and methodsThis retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models. ResultsThe area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model. ConclusionThe bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Achieving High-Quality Text and Audio-to-Image Generation in a Single Step

Diffusion models have significantly advanced text-to-image generation by producing high-quality and imaginative results. However, their multi-step sampling process often proves slow, requiring extensive inference steps to achieve satisfactory outcomes. Despite attempts to improve sampling speed and computational efficiency through distillation, creating a functional one-step model has remained elusive. In this study, we investigate Rectified Flow, a recent method primarily applied to small datasets, as a potential solution. Central to Rectified Flow is its reflow procedure, which optimizes probability flow trajectories, refines noise-to- image mapping, and enables effective distillation with student models. We introduce a novel text-conditioned pipeline to convert Stable Diffusion (SD) into an ultra-fast one-step model. Our approach underscores the crucial role of reflow in enhancing noise-to-image assignments. Leveraging this pipeline, we develop the first one-step diffusion-based text-to- image generator capable of producing high-quality images comparable to those generated by SD. Additionally, we extend our methodology to include audio inputs, demonstrating its efficacy in generating images from audio cues with remarkable fidelity and speed. Key Words: Stable Diffusion, Text-to-Image Creation, Image Processing

Fusion of GaoFen-5 and Sentinel-2B data for lithological mapping using vision transformer dynamic graph convolutional network

Lithological identification and mapping using remote sensing (RS) imagery are challenging. Traditional lithological mapping relies mainly on multispectral data and machine learning methods. However, inadequate spectral information and inappropriate classification algorithms are major problems for RS geological applications. Moreover, satellite hyperspectral images (HSI) at low spatial resolution and convolutional neural network (CNN)-based methods with incomplete feature extraction remain challenging because of the limitations of sensor imaging and convolutional kernels for lithological mapping. To address the above issues, in this study, smoothing filter-based intensity modulation (SFIM) fusion technology is first employed to fuse GaoFen-5 hyperspectral images and Sentinel-2B multispectral images. This approach significantly improves spatial details and enriches spectral information. Subsequently, a novel Vision Transformer Dynamic Graph Convolutional Network (ViT-DGCN) is proposed for lithological mapping of the Cuonadong dome, Tibet, China. ViT-DGCN is a joint model consisting of a transformer and a dynamic graph convolution module that enhances feature extraction capabilities by exploring long-range interaction sequence features and dynamic graph structure information in a targeted manner. The proposed algorithm exhibits superior performance compared to the others, achieving an overall accuracy of 97% for the Cuonadong dome using only 1% of the available training samples.

A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation

A novel zero-reference low-light image-enhancement approach based on noise estimation (ZLEN) is proposed to mitigate noise interference in image-enhancement processes, while the tenets of zero-reference and lightweight network architecture are maintained. ZLEN improves the high-order curve expression governing the mapping of low-light images to their enhanced counterparts, addressing image noise through a meticulously designed noise-estimation module and a zero-reference noise loss function. First, the higher-order curve expression with a noise term is defined, and then the noise map undergoes feature extraction through the semantic-aware attention module; following this, the resulting features are integrated with the low-light image. Ultimately, a lightweight convolutional neural network is adjusted to estimate higher-order curve parameters that link the low-light image to its enhanced version. Notably, ZLEN achieves luminance enhancement and noise reduction without paired or unpaired training data. Rigorous qualitative and quantitative evaluations were conducted on diverse benchmark datasets, demonstrating that ZLEN attained state-of-the-art (SOAT) status among existing zero-reference and unpaired-reference image-enhancement methodologies, while it exhibited comparable performance to full-reference image-enhancement methods. To confirm the practicality and robustness of ZLEN, the luminance enhancement was applied to mine images, which yielded satisfactory results.

Charge transfer dynamics in C3N4 encapsulated Cs3Bi2Br9 nanocrystals heterojunction for photocatalytic application

Perovskite materials have shown great application prospects in the field of photocatalysis due to their excellent photoelectric properties. However, the toxicity of lead and material instability limit its application. In this work, the heterojunction based on C3N4 thin layer encapsulated lead-free Cs3Bi2Br9 nanocrystals was constructed by electrostatic self-assembly method. This combination way does not disrupt the electronic, crystalline and structural properties of C3N4 and Cs3Bi2Br9. Fluorescence quenching in C3N4/Cs3Bi2Br9 composite is ascribed to the charge transfer process, which could be directly observed by spatially and temporally resolved fluorescence mapping image. The synergistic effect between C3N4 and Cs3Bi2Br9 can significantly improve the photocatalytic degradation efficiency of RhB, 98 % at 1 h. The stability of the composite was enhanced due to the special encapsulation effect. This work will provide a guideline for the construction of two-dimensional materials and perovskite heterojunction systems in the field of photocatalysis.

Alterations in subcortical magnetic susceptibility and disease-specific relationship with brain volume in major depressive disorder and schizophrenia

Quantitative susceptibility mapping is a magnetic resonance imaging technique that measures brain tissues’ magnetic susceptibility, including iron deposition and myelination. This study examines the relationship between subcortical volume and magnetic susceptibility and determines specific differences in these measures among patients with major depressive disorder (MDD), patients with schizophrenia, and healthy controls (HCs). This was a cross-sectional study. Sex- and age- matched patients with MDD (n = 49), patients with schizophrenia (n = 24), and HCs (n = 50) were included. Magnetic resonance imaging was conducted using quantitative susceptibility mapping and T1-weighted imaging to measure subcortical susceptibility and volume. The acquired brain measurements were compared among groups using analyses of variance and post hoc comparisons. Finally, a general linear model examined the susceptibility–volume relationship. Significant group-level differences were found in the magnetic susceptibility of the nucleus accumbens and amygdala (p = 0.045). Post-hoc analyses indicated that the magnetic susceptibility of the nucleus accumbens and amygdala for the MDD group was significantly higher than that for the HC group (p = 0.0054, p = 0.0065, respectively). However, no significant differences in subcortical volume were found between the groups. The general linear model indicated a significant interaction between group and volume for the nucleus accumbens in MDD group but not schizophrenia or HC groups. This study showed susceptibility alterations in the nucleus accumbens and amygdala in MDD patients. A significant relationship was observed between subcortical susceptibility and volume in the MDD group’s nucleus accumbens, which indicated abnormalities in myelination and the dopaminergic system related to iron deposition.

Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis

Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.

SA²VP: Spatially Aligned-and-Adapted Visual Prompt

As a prominent parameter-efficient fine-tuning technique in NLP, prompt tuning is being explored its potential in computer vision. Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming from NLP, which represents an input image as a flattened sequence of token embeddings and then learns a set of unordered parameterized tokens prefixed to the sequence representation as the visual prompts for task adaptation of large vision models. While such sequential modeling paradigm of visual prompt has shown great promise, there are two potential limitations. First, the learned visual prompts cannot model the underlying spatial relations in the input image, which is crucial for image encoding. Second, since all prompt tokens play the same role of prompting for all image tokens without distinction, it lacks the fine-grained prompting capability, i.e., individual prompting for different image tokens. In this work, we propose the Spatially Aligned-and-Adapted Visual Prompt model (SA^2VP), which learns a two-dimensional prompt token map with equal (or scaled) size to the image token map, thereby being able to spatially align with the image map. Each prompt token is designated to prompt knowledge only for the spatially corresponding image tokens. As a result, our model can conduct individual prompting for different image tokens in a fine-grained manner. Moreover, benefiting from the capability of preserving the spatial structure by the learned prompt token map, our SA^2VP is able to model the spatial relations in the input image, leading to more effective prompting. Extensive experiments on three challenging benchmarks for image classification demonstrate the superiority of our model over other state-of-the-art methods for visual prompt tuning. Code is available at https://github.com/tommy-xq/SA2VP.

FD3D: Exploiting Foreground Depth Map for Feature-Supervised Monocular 3D Object Detection

Monocular 3D object detection usually adopts direct or hierarchical label supervision. Recently, the distillation supervision transfers the spatial knowledge from LiDAR- or stereo-based teacher networks to monocular detectors, but remaining the domain gap. To mitigate this issue and pursue adequate label manipulation, we exploit Foreground Depth map for feature-supervised monocular 3D object detection named FD3D, which develops the high-quality instructive intermediate features to conduct desirable auxiliary feature supervision with only the original image and annotation foreground object-wise depth map (AFOD) as input. Furthermore, we build up our instructive feature generation network to create instructive spatial features based on the sufficient correlation between image features and pre-processed AFOD, where AFOD provides the attention focus only on foreground objects to achieve clearer guidance in the detection task. Moreover, we apply the auxiliary feature supervision from the pixel and distribution level to achieve comprehensive spatial knowledge guidance. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both the KITTI and nuScenes datasets, with no external data and no extra inference computational cost. We also conduct quantitative and qualitative studies to reveal the effectiveness of our designs.

Reliable differentiation of necrosis and active metabolically contours of glioblastoma multiforme using susceptibility-based imaging

PurposeGadolinium-enhancing necrosis in glioblastoma multiforme (GBM), as an occasionally occurring false positive in contrast enhancement (CE) imaging, leads to trouble for segmentation of GBM and treatment. Therefore, the investigation of complementary detection way to identify the metabolically active volume of the tumor with high reliability is very worth to be addressed. Here, we reported on a case of GBM with gadolinium-enhancing necrosis in an experimental CE imaging study in mice and evaluated the discrimination of the necrosis and metabolically active parts of the GBM using conventional and state-of-the-art susceptibility-based MRI. MethodsIn this study, following 5-aminolevulinic acid (ALA) and iron supplements (FAC, 6 h after ALA, intra-tumoral injection) to animal, T2*-W imaging and quantitative susceptibility mapping (QSM) were performed, and compared with CE imaging. ResultsThe signal intensity (SI) of the active and necrosis areas of the case in the CE image demonstrated no significant difference while the SI on the T2*-W images and susceptibility value in QSM changed 24 and 150%, respectively. ConclusionThe preclinical case report provides valuable insights into the potential of susceptibility-based MRI using ALA + FAC to apply as a robust discriminator between necrotic and viable tumors.

Context-I2W: Mapping Images to Context-Dependent Words for Accurate Zero-Shot Composed Image Retrieval

Different from the Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to learn a more accurate image representation that has adaptive attention to the reference image for various manipulation descriptions. In this paper, we propose a novel context-dependent mapping network, named Context-I2W, for adaptively converting description-relevant Image information into a pseudo-word token composed of the description for accurate ZS-CIR. Specifically, an Intent View Selector first dynamically learns a rotation rule to map the identical image to a task-specific manipulation view. Then a Visual Target Extractor further captures local information covering the main targets in ZS-CIR tasks under the guidance of multiple learnable queries. The two complementary modules work together to map an image to a context-dependent pseudo-word token without extra supervision. Our model shows strong generalization ability on four ZS-CIR tasks, including domain conversion, object composition, object manipulation, and attribute manipulation. It obtains consistent and significant performance boosts ranging from 1.88% to 3.60% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://anonymous.4open.science/r/Context-I2W-4224/.

Early microvascular coronary endothelial dysfunction precedes pembrolizumab-induced cardiotoxicity. Preventive role of high dose of atorvastatin.

Immune checkpoint inhibitors (ICIs) exhibit remarkable antitumor activity and immune-related cardiotoxicity of unknown pathomechanism. The aim of the study was to investigate the ICI class-dependent cardiotoxicity in vitro and pembrolizumab's (Pem's) cardiotoxicity in vivo, seeking for translational prevention means. Cytotoxicity was investigated in primary cardiomyocytes and splenocytes, incubated with ipilimumab, Pem and avelumab. Pem's cross-reactivity was assessed by circular dichroism (CD) on biotechnologically produced human and murine PD-1 and in silico. C57BL6/J male mice received IgG4 or Pem for 2 and 5weeks. Echocardiography, histology, and molecular analyses were performed. Coronary blood flow velocity mapping and cardiac magnetic resonance imaging were conducted at 2weeks. Human EA.hy926 endothelial cells were incubated with Pem-conditioned media from human mononuclear cells, in presence and absence of statins and viability and molecular signaling were assessed. Atorvastatin (20mg/kg, daily) was administered in vivo, as prophylaxis. Only Pem exerted immune-related cytotoxicity in vitro. Pem's cross-reactivity with the murine PD-1 was confirmed by CD and docking. In vivo, Pem initiated coronary endothelial and diastolic dysfunction at 2weeks and systolic dysfunction at 5weeks. At 2weeks, Pem induced ICAM-1 and iNOS expression and intracardiac leukocyte infiltration. At 5weeks, Pem exacerbated endothelial activation and triggered cardiac inflammation. Pem led to immune-related cytotoxicity in EA.hy926 cells, which was prevented by atorvastatin. Atorvastatin mitigated functional deficits, by inhibiting endothelial dysfunction in vivo. We established for the first time an in vivo model of Pem-induced cardiotoxicity. Coronary endothelial dysfunction precedes Pem-induced cardiotoxicity, whereas atorvastatin emerges as a novel prophylactic therapy.

Can MRI Accurately Diagnose and Stage Endometrial Adenocarcinoma?

Background and Objectives: Endometrial carcinoma is one of the most common gynecological cancers, and benign lesions such as endometrial hyperplasia, polyps, adenomyosis and leiomyomas should be included in the differential diagnosis. Magnetic resonance imaging has an important role in evaluating endometrial cancer and assessing the depth of myometrial invasion, and it closely correlates with the prognosis of the patient. The purpose of this study is to evaluate the MRI semiology of the endometrial carcinomas that mimic benign lesions, the main factors that may affect the correct diagnosis and the feasibility of magnetic resonance imaging to evaluate the depth of the myometrial invasion of endometrial cancer. Materials and Methods: This is a retrospective analysis of 45 patients that underwent MRI examinations and the lesions were pathologically diagnosed as endometrial carcinoma after surgical resection. This study evaluated the staging accuracy of T2-weighted imaging, diffusion-weighted imaging (DWI), ADC mapping and T1-weighted imaging with fat saturation before and after gadolinium injection. Results: In 36 of the 45 cases, the MRI of the lesion showed the characteristics of endometrial cancer and the diagnosis was certain. Nine lesions (20%) were described as unequivocal and had unspecific MR appearance. In eight of the nine cases (89%), the histopathologic report revealed the presence of leiomyomas and two of these cases (22%) were also associated with adenomyosis. The cause of underestimation in these patients was coexisting lesions exhibiting heterogenous intensity and contrast enhancement, which made it difficult to detect the margins of the lesions. The depth of the myometrial invasion was underestimated in nine cases and overestimated in three cases. The staging accuracy with MRI was 74%. There was a significant correlation between MR imaging and histopathologic finding in the assessment of myometrial invasion (p < 0.001). Cervical extension was noted in eight cases (18%), but was missed on MR imaging in two patients and overstaged in none. Six of them were associated with myometrial invasion in more than 50% of the thickness. There was a significant correlation between MR imaging and histopathologic finding in the assessment of cervical extension (p < 0.001). Conclusions: Our data confirm the high accuracy of MRI in the diagnosis and local staging of endometrial carcinoma. The information provided by MRI has an important role in planning the treatment and the prognosis of the patients.