Complete Image Research Articles

The ALMA Interferometric Pipeline Heuristics

We describe the calibration and imaging heuristics developed and deployed in the Atacama Large Millimeter/submillimeter Array (ALMA) interferometric data processing pipeline, as of ALMA Cycle 9 operations. The pipeline software framework is written in Python, with each data reduction stage layered on top of tasks and toolkit functions provided by the Common Astronomy Software Applications package. This framework supports a variety of tasks for observatory operations, including science data quality assurance, observing mode commissioning, and user reprocessing. It supports ALMA and Very Large Array interferometric data along with ALMA and NRO 45 m single dish data, via different stages and heuristics. In addition to producing calibration tables, calibrated measurement sets, and cleaned images, the pipeline creates a WebLog which serves as the primary interface for verifying the quality assurance of the data by the observatory and for examining the contents of the data by the user. Following the adoption of the pipeline by ALMA Operations in 2014, the heuristics have been refined through annual prioritized development cycles, culminating in a new pipeline release aligned with the start of each ALMA Cycle of observations. Initial development focused on basic calibration and flagging heuristics (Cycles 2–3), followed by imaging heuristics (Cycles 4–5). Further refinement of the flagging and imaging heuristics, including the introduction of parallel processing, proceeded for Cycles 6–7. In the 2020 release, the algorithm to identify channels to use for continuum subtraction and imaging was substantially improved by the addition of a moment difference analysis. A spectral renormalization stage was added for the 2021 release (Cycle 8) to correct high spectral resolution visibility data acquired on targets exhibiting strong celestial line emission in their autocorrelation spectra. The calibration heuristics used in the low signal-to-noise regime were improved for the 2022 release (Cycle 9). In the two most recent Cycles, 97% of ALMA data sets were calibrated and imaged with the pipeline, ensuring long-term automated reproducibility of results. We conclude with a brief description of plans for future additions, including a self-calibration stage, support for multi-configuration imaging, and complete calibration and imaging of full polarization data.

Types of Carabidae (Insecta: Coleoptera) deposited at the entomological collection of Instituto- Fundación Miguel Lillo, Tucumán, Argentina.

The Carabidae (Insecta: Coleoptera) type species of Entomological Collection of the Entomology Institute housed in the Zoology Area of Miguel Lillo Foundation (FML), Tucumán, Argentina, were examined and listed. This collection is considered one of the most prestigious collections in Argentina and South America. The Carabidae family is represented by 545 species, with ten holotypes, one allotype, 12 cotypes, and 65 paratypes, which correspond to 35 species. Complete information and images for each species are given.

Comparison of diffusion weighted imaging b0 with T2*-weighted gradient echo or susceptibility weighted imaging for intracranial hemorrhage detection after reperfusion therapy for ischemic stroke

PurposeDiffusion-weighted imaging (DWI) b0 may be able to substitute T2*-weighted gradient echo (GRE) or susceptibility-weighted imaging (SWI) in case of comparable detection of intracranial hemorrhage (ICH), thereby reducing MRI examination time. We evaluated the diagnostic accuracy of DWI b0 compared to T2*GRE or SWI for detection of ICH after reperfusion therapy for ischemic stroke.MethodsWe pooled 300 follow-up MRI scans acquired within 1 week after reperfusion therapy. Six neuroradiologists each rated DWI images (b0 and b1000; b0 as index test) of 100 patients and, after a minimum of 4 weeks, T2*GRE or SWI images (reference standard) paired with DWI images of the same patients. Readers assessed the presence of ICH (yes/no) and type of ICH according to the Heidelberg Bleeding Classification. We determined the sensitivity and specificity of DWI b0 for detection of any ICH, and the sensitivity for detection of hemorrhagic infarction (HI1 & HI2) and parenchymal hematoma (PH1 & PH2).ResultsWe analyzed 277 scans of ischemic stroke patients with complete image series and sufficient image quality (median age 65 years [interquartile range, 54–75], 158 [57%] men). For detection of any ICH on DWI b0, the sensitivity was 62% (95% CI: 50–76) and specificity 96% (95% CI: 93–99). The sensitivity of DWI b0 was 52% (95% CI: 28–68) for detection of hemorrhagic infarction and 84% (95% CI: 70–92) for parenchymal hematoma.ConclusionDWI b0 is inferior for detection of ICH compared to T2*GRE/SWI, especially for smaller and more subtle hemorrhages. Follow-up MRI protocols should include T2*GRE/SWI for detection of ICH after reperfusion therapy.

Efficient infrared image processing and machine learning algorithm for breast cancer screening

ABSTRACT Breast cancer is a significant global health concern, and early detection is crucial for improving survival rates. The article reviews various studies that investigate different methods for detecting breast cancer using non-invasive imaging techniques with a focus on thermal imaging, including feature extraction, image segmentation, and machine learning. It concludes that developing efficient and accurate breast cancer screening software requires an integrated approach to data collection, image processing, and machine learning algorithms. The article presents a novel technique for developing breast cancer screening software using Rotational Thermographic Imaging, dynamic temperature-based data collection, Colour-based Infrared Image Processing, and a Machine Learning algorithm to provide a complete breast imaging in sitting position to reduce the chances of missing abnormalities. Image processing and machine learning techniques are utilised to extract a comprehensive relevant feature set from the captured images and used to train a machine learning model. The system was tested on an increasing patient population in a clinical setting deployed at a hospital. The algorithm’s performance was evaluated using several metrics, including sensitivity (82.14%), specificity (98.33%), and accuracy (93.27%). The results demonstrate that the proposed algorithm achieved high accuracy and sensitivity, making it a promising tool for breast cancer screening.

Can Artificial Intelligence Reconstruct Ancient Mosaics?

ABSTRACT A large number of ancient mosaics are no longer available to us because they have been destroyed by erosion, earthquakes, looting, or even used as materials in newer construction. To make things worse, among the small fraction of mosaics that have been recovered, many are damaged or incomplete. Therefore, it is of interest to explore how the mosaics may have appeared originally by using virtual reconstruction. This has traditionally been done manually and more recently using computer graphics programs but always by humans. In the last few years, artificial intelligence (AI) has made impressive progress in the generation of images from text descriptions and reference images. State-of-the-art AI tools such as DALL-E2 can generate high-quality images from text prompts and can take a reference image to guide the process. In August 2022, DALL-E2 launched a new feature called outpainting that takes as input an incomplete image and a text prompt and then generates a complete image filling in the missing parts. In this paper, we explore whether this innovative technology can be used to perform virtual reconstruction of mosaics with missing parts. Hence, a set of ancient mosaics have been selected and DALL-E2 has been used to create virtual reconstructions; the results are promising, showing that AI is able to interpret the key features of the mosaics and is able to produce virtual reconstructions that capture the essence of the scene. However, in some cases AI does not reproduce some details or geometric forms or introduces elements that are not consistent with the rest of the mosaic. This suggests that when AI image generation technology matures in the next few years, it could be a valuable tool to create virtual reconstructions of mosaics in the future.

A suggested universal protocol for dental examination in sports.

The athletes of any sport and level submit their bodies to constant exercise. Any given pathology can increase the risk of injury, illness, or even reduced performance. The medical examination is valuable in diagnosing existing health problems and preventing medical issues that might compromise the athlete's overall health when exercising. The stomatognathic system is not an exemption, as oral pathologies, including dental caries and periodontal diseases, are found in high incidence in sports. The need for accurate and detailed dental examination in sports leaded dentists from the European Association for Sports Dentistry and the Academy for Sports Dentistry to elaborate a universal dental examination in sports protocol that can record the overall oral health of the athlete, including the teeth, periodontium, and musculoskeletal screening, for all athletes. The outcome of this stomatognathic examination allows sports physicians and professionals other than dentists to have a complete image of the individual oral health condition of any given athlete, and it allows the dentists to efficiently screen and prevent pathologies, as well as to advise on the eligibility to practice sports from the oral health perspective.

Application of video image processing in sports action recognition based on particle swarm optimization algorithm

The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm; Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.

Semantic 3D-Aware Portrait Synthesis and Manipulation Based on Compositional Neural Radiance Field

Recently 3D-aware GAN methods with neural radiance field have developed rapidly. However, current methods model the whole image as an overall neural radiance field, which limits the partial semantic editability of synthetic results. Since NeRF renders an image pixel by pixel, it is possible to split NeRF in the spatial dimension. We propose a Compositional Neural Radiance Field (CNeRF) for semantic 3D-aware portrait synthesis and manipulation. CNeRF divides the image by semantic regions and learns an independent neural radiance field for each region, and finally fuses them and renders the complete image. Thus we can manipulate the synthesized semantic regions independently, while fixing the other parts unchanged. Furthermore, CNeRF is also designed to decouple shape and texture within each semantic region. Compared to state-of-the-art 3D-aware GAN methods, our approach enables fine-grained semantic region manipulation, while maintaining high-quality 3D-consistent synthesis. The ablation studies show the effectiveness of the structure and loss function used by our method. In addition real image inversion and cartoon portrait 3D editing experiments demonstrate the application potential of our method.

Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning

This paper presents a new adversarial training framework for image inpainting with segmentation confusion adversarial training (SCAT) and contrastive learning. SCAT plays an adversarial game between an inpainting generator and a segmentation network, which provides pixel-level local training signals and can adapt to images with free-form holes. By combining SCAT with standard global adversarial training, the new adversarial training framework exhibits the following three advantages simultaneously: (1) the global consistency of the repaired image, (2) the local fine texture details of the repaired image, and (3) the flexibility of handling images with free-form holes. Moreover, we propose the textural and semantic contrastive learning losses to stabilize and improve our inpainting model's training by exploiting the feature representation space of the discriminator, in which the inpainting images are pulled closer to the ground truth images but pushed farther from the corrupted images. The proposed contrastive losses better guide the repaired images to move from the corrupted image data points to the real image data points in the feature representation space, resulting in more realistic completed images. We conduct extensive experiments on two benchmark datasets, demonstrating our model's effectiveness and superiority both qualitatively and quantitatively.

Grade V renal trauma management: results from the multi-institutional genito-urinary trauma study.

To investigate management trends for American Association for the Surgery of Trauma (AAST) grade V renal trauma with focus on non-operative management. We used prospectively collected data as part of the Multi-institutional Genito-Urinary Trauma Study (MiGUTS). We included patients with grade V renal trauma according to the AAST Injury Scoring Scale 2018 update. All cases submitted by participating centers with radiology images available were independently reviewed to confirm renal trauma grade. Management was classified as expectant, conservative (minimally invasive, endoscopic or percutaneous procedures), or operative (renal-related surgery). Eighty patients were included, 25 of whom had complete imaging and had independent confirmation of AAST grade V renal trauma. Median age was 35years (Interquartile range (IQR) 25-50) and 23 (92%) had blunt trauma. Ten patients (40%) were managed operatively with nephrectomy. Conservative management was used in nine patients (36%) of which six received angioembolization and three had a stent or drainage tube placed. Expectant management was followed in six (24%) patients. Transfusion requirements were progressively higher with groups requiring more aggressive treatment, and injury characteristics differed significantly across management groups in terms of hematoma size and laceration size. Vascular contrast extravasation was more likely in operatively managed patients though a statistically significant association was not found. Successful use of nonoperative management for grade V injuries is used for a substantial subset of patients. Lower transfusion requirement and less severe injury radiologic phenotype appear to be important characteristics delineating this group.

Medical management and rehabilitation in a patient with avascular necrosis of the femoral head in the context of vitamin D deficiency and secondary hyperparathyroidism – case report

Vitamin D is essential in determining bone and muscle health. Vitamin D deficiency (VDD) produces secondary hyperparathyroidism, loss of bone quality and mass, mineral-ization effects and fractures, myopathy, and falls. The avascular necrosis (AVN) of the femoral head pathophysiology is still unclear and can be considered a vascular bone dis-ease. Our paper aimed to evaluate the etiology of AVN in a young male with bilateral fem-oral neck damage who required bilateral arthroplasty. Method: We present the case of a 46-year-old male patient with VDD and bilateral total hip arthroplasty for osteoarthritis secondary to AVN of the femoral head. After complete hormonal, biological, and imaging evaluations, the diagnosis of primary hyperparathyroidism was not supported; the im-provement of vitamin D deficiency after eight months of treatment clarified the diagnosis: hyperparathyroidism secondary to vitamin D deficiency. Results: Supplementation with 4000 IU of vitamin D improved the patient’s response to specific rehabilitation methods. Conclusions: Severe vitamin D deficiency can secondarily affect the structure of the femoral head through secondary hyperparathyroidism, producing AVN, but only osteopenia was obtained on DXA evaluation. VDD can be an important etiological factor in the occur-rence of AVN, and its correction can improve the evolution of the disease, improving the reabilitation of these patients. Keywords: avascular necrosis, secondary hyperparathyroidism, vitamin D deficiency, rehabilitation

Medical management and rehabilitation in a patient with avas-cular necrosis of the femoral head in the context of vitamin D deficiency and secondary hyperparathyroidism – case report

Vitamin D is essential in determining bone and muscle health. Vitamin D deficiency (VDD) produces secondary hyperparathyroidism, loss of bone quality and mass, mineral-ization effects and fractures, myopathy, and falls. The avascular necrosis (AVN) of the femoral head pathophysiology is still unclear and can be considered a vascular bone dis-ease. Our paper aimed to evaluate the etiology of AVN in a young male with bilateral fem-oral neck damage who required bilateral arthroplasty. Method: We present the case of a 46-year-old male patient with VDD and bilateral total hip arthroplasty for osteoarthritis secondary to AVN of the femoral head. After complete hormonal, biological, and imaging evaluations, the diagnosis of primary hyperparathyroidism was not supported; the im-provement of vitamin D deficiency after eight months of treatment clarified the diagnosis: hyperparathyroidism secondary to vitamin D deficiency. Results: Supplementation with 4000 IU of vitamin D improved the patient’s response to specific rehabilitation methods. Conclusions: Severe vitamin D deficiency can secondarily affect the structure of the femo-ral head through secondary hyperparathyroidism, producing AVN, but only osteopenia was obtained on DXA evaluation. VDD can be an important etiological factor in the occur-rence of AVN, and its correction can improve the evolution of the disease, improving the reabilitation of these patients.

Intravoxel incoherent motion diffusion-weighted MRI for predicting the efficacy of high-intensity focused ultrasound ablation for uterine fibroids.

To evaluate the significance of magnetic resonance (MR) intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) quantitative parameters in predicting early efficacy of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids before treatment. 64 patients with 89 uterine fibroids undergoing HIFU ablation (51 sufficient ablations and 38 insufficient ablations) were enrolled in the study and completed MR imaging and IVIM-DWI before treatment. The IVIM-DWI parameters, including D (diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction) and relative blood flow (rBF) were calculated. The logistic regression (LR) model was constructed to analyze the predictors of efficacy. The receiver operating characteristic (ROC) curve was drawn to assess the model's performance. A nomograph was constructed to visualize the model. The D value of the sufficient ablation group (931.0(851.5-987.4) × 10-6 mm2/s) was significantly lower than that of the insufficient ablation group (1052.7(1019.6-1158.7) × 10-6 mm2/s) (p<0.001). However, differences in D*, f, and rBF values between the groups were not significant (p>0.05). The LR model was constructed with D value, fibroid position, ventral skin distance, T2WI signal intensity, and contrast enhanced degree. The area under the ROC curve, specificity, and sensitivity of the model were 0.858 (95% confidence interval: 0.781, 0.935), 0.686, and 0.947. The nomogram and calibration curves confirmed that the model had excellent performance. The IVIM-DWI quantitative parameters can be used to predict early effects of HIFU ablation on uterine fibroids. A high D value before treatment may indicate that the treatment will be less effective in the early stages.

False lumen flow dynamics and partial thrombosis in patients with chronic aortic dissection of the descending aorta: a 4D flow CMR study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Spanish Ministry of Science, Innovation, and Universities Background Partial thrombosis of the false lumen (FL) in patients with chronic aortic dissection (AD) of the descending aorta has recently been associated with poor outcomes (1). However, four-dimensional phase-contrast cardiovascular magnetic resonance (4D flow CMR) studies analyzing flow dynamics and biomechanics in the FL and their relationship with partial thrombosis are lacking. Purpose This study aimed to compare FL flow dynamics and biomechanics between patients with non- and partially-thrombosed FL. Methods Patients with a chronic (&amp;gt;3 months after the acute event), non-thrombosed or partially-thrombosed FL in the descending aorta after an AD underwent an imaging follow-up including a magnetic resonance angiography (MRA) and a 4D flow CMR study. FL thrombosis was quantified as the ratio of thrombus volume and FL volume on MRA. Forward systolic and diastolic flow, and wall shear stress (WSS) were calculated at 8 equidistant planes covering the FL descending aorta from the pulmonary bifurcation to the diaphragmatic level, and averaged values were used (1,2). Maximum kinetic energy (KE), maximum acceleration and flow stasis were quantified in a volume covering the same region of the descending aorta (1). Aortic stiffness in the FL was assessed in terms of pulse wave velocity (PWV), which was calculated from the third supraortic trunk to the diaphragmatic level (3). Results Sixty-five patients with a complete imaging protocol were included in the study. The FL was non thrombosed in 34 patients and partially thrombosed in 31 patients, which presented with the 21 ± 13 % of the FL volume occupied by thrombus. Demographics and clinical data, and flow dynamic and biomechanical parameters of these patients are shown in Table 1. Partial thrombosis of the false lumen was associated with a reduction in the amount and energy of flow in the FL (reduced forward systolic flow, KE and acceleration (Figure 1B-D, respectively)), and a more stagnated flow in the FL (increased flow stasis (Figure 1E)). Axial WSS showed a tendency to be lower in the partial-thrombosis group compared to the no-thrombosis group (Figure 1F), while PWV were similar in both of them. Conclusion In patients with chronic AD of the descending aorta, false lumen axial wall shear stress, kinetic energy, and flow acceleration and stasis proved to be the main discriminators between no and partial thrombosis of the false lumen.

3D reconstruction of unlimited-size real-world porous media by combining a BicycleGAN-based multimodal dictionary and super-dimension reconstruction

Three-dimensional reconstruction from a single two-dimensional image is important in the field of numerical simulation of porous media (including sandstones, composite, and alloys). Owing to limitations of computer storage space, the algorithm itself, and GPU memory, traditional algorithms and increasingly popular deep-learning algorithms cannot reconstruct complete real-size core images. To solve this problem, we propose a generative adversarial network dictionary-based super-dimension (GANDB-SD) algorithm that uses a BicycleGAN model to establish eight multimodal mappings of small-size structures. These mapping relations are used as the mapping dictionary when the super-dimension reconstruction framework is used to reconstruct the real core images without size limitation. Based on this algorithm, we further propose a three-dimensional co-occurrence matrix function as the loss function for the first time and propose a diversity preservation strategy to solve the pattern repetition problem during reconstruction. We performed an experiment to reconstruct a 5123 image, in which the morphological parameters and reconstruction time of different algorithms were compared. Results show that the algorithm has advantages in reconstruction accuracy and time. We further reconstructed 10003 real cores, and the statistical and morphological similarities between the reconstructed and target structures proved that the proposed algorithm is competent in the task of reconstructing real-world cores with unlimited size. We remark that our approach opens the door to a new methodology that incorporating machine learning/deep learning techniques into conventional super-dimension methods to address 3D reconstruction of unlimited-size real-world porous media issues in a wide range of related fields.

Water Quality Evaluation and Prediction Using Irrigation Indices, Artificial Neural Networks, and Partial Least Square Regression Models for the Nile River, Egypt

Water quality is identically important as quantity in terms of meeting basic human needs. Therefore, evaluating the surface-water quality and the associated hydrochemical characteristics is essential for managing water resources in arid and semi-arid environments. Therefore, the present research was conducted to evaluate and predict water quality for agricultural purposes across the Nile River, Egypt. For that, several irrigation water quality indices (IWQIs) were used, along with an artificial neural network (ANN), partial least square regression (PLSR) models, and geographic information system (GIS) tools. The physicochemical parameters, such as T °C, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, were measured at 51 surface-water locations. As a result, the ions contents were the following: Ca2+ &gt; Na+ &gt; Mg2+ &gt; K+ and HCO3− &gt; Cl− &gt; SO42− &gt; NO3− &gt; CO32−, reflecting Ca-HCO3 and mixed Ca-Mg-Cl-SO4 water types. The irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), permeability index (PI), and magnesium hazard (MH) had mean values of 92.30, 1.01, 35.85, 31.75, 72.30, and 43.95, respectively. For instance, the IWQI readings revealed that approximately 98% of the samples were inside the no restriction category, while approximately 2% of the samples fell within the low restriction area for irrigation. The ANN-IWQI-6 model’s six indices, with R2 values of 0.999 for calibration (Cal.) and 0.945 for validation (Val.) datasets, are crucial for predicting IWQI. The rest of the models behaved admirably in terms of predicting SAR, Na%, SSP, PI, and MR with R2 values for the Cal. and validation Val. of 0.999. The findings revealed that ANN and PLSR models are effective methods for predicting irrigation water quality to assist decision plans. To summarize, integrating physicochemical features, WQIs, ANN, PLSR, models, and GIS tools to evaluate surface-water suitability for irrigation offers a complete image of water quality for sustainable development.

Head and neck MRI-based T stage and [18F]FDG PET/CT-based N/M stage improved prognostic stratification in primary nasopharyngeal carcinoma.

To evaluate whether MRI-based T stage (TMRI), [18F]FDG PET/CT-based N (NPET/CT), and M stage (MPET/CT) are superior in NPC patients' prognostic stratification based on long-term survival evidences, and whether TNM staging method involving TMRI + NPET/CT + MPET/CT could improve NPC patients' prognostic stratification. From April 2007 to December 2013, 1013 consecutive untreated NPC patients with complete imaging data were enrolled. All patients' initial stages were repeated based on (1) the NCCN guideline recommended "TMRI + NMRI + MPET/CT" ("MMP") staging method; (2) the traditional "TMRI + NMRI + Mconventional work-up (CWU)" ("MMC") staging method; (3) the single-step "TPET/CT + NPET/CT + MPET/CT" ("PPP") staging method; or (4) the "TMRI + NPET/CT + MPET/CT" ("MPP") staging method recommended in present research. Survival curve, ROC curve, and net reclassification improvement (NRI) analysis were used to evaluate the prognosis predicting ability of different staging methods. [18F]FDG PET/CT performed worse on T stage (NRI = - 0.174, p < 0.001) but better on N (NRI = 0.135, p = 0.004) and M stage (NRI = 0.126, p = 0.001). The patients whose N stage upgraded by [18F]FDG PET/CT had worse survival (p = 0.011). The "TMRI + NPET/CT + MPET/CT" ("MPP") method performed better on survival prediction when compared with "MMP" (NRI = 0.079, p = 0.007), "MMC" (NRI = 0.190, p < 0.001), or "PPP" method (NRI = 0.107, p < 0.001). The "TMRI + NPET/CT + MPET/CT" ("MPP") method could reclassify patients' TNM stage to a more appropriate stage. The improvement is significant in patients with more than 2.5-years follow-up according to the time-dependent NRI values. The MRI is superior to [18F]FDG PET/CT in T stage, and [18F]FDG PET/CT is superior to CWU in N/M stage. The "TMRI + NPET/CT + MPET/CT" ("MPP") staging method could significantly improve NPC patients' long-term prognostic stratification. The present research provided long-term follow-up evidence for benefits of MRI and [18F]FDG PET/CT in TNM staging for nasopharyngeal carcinoma, and proposes a new imaging procedure for TNM staging incorporating MRI-based T stage and [18F]FDG PET/CT-based N and M stage, which significantly improves long-term prognostic stratification for patients with NPC. • The long-term follow-up evidence of a large-scale cohort was provided to evaluate the advantages of MRI, [18F]FDG PET/CT, and CWU in the TNM staging of nasopharyngeal carcinoma. • A new imaging procedure for TNM stage of nasopharyngeal carcinoma was proposed.

Positive Progesterone Receptor Expression in Meningioma May Increase the Transverse Relaxation: First Prospective Clinical Trial Using Single-Shot Ultrafast T2 Mapping

Rationale and ObjectivesThis project aims to investigate the diagnostic performance of multiple overlapping-echo detachment imaging (MOLED) technique-derived transverse relaxation time (T2) maps in predicting progesterone receptor (PR) and S100 expression in meningiomas. Materials and Methods63 meningioma patients were enrolled from October 2021 to August 2022, who underwent a complete routine magnetic resonance imaging and T2 MOLED, which can characterize the whole brain transverse relaxation time within 32 seconds in a single scan. After the surgical resection of meningiomas, the expression levels of PR and S100 were determined by an experienced pathologist using immunohistochemistry techniques. Histogram analysis was performed in tumor parenchyma based on the parametric maps. Independent t test and Mann-Whitney U test were applied for the comparison of histogram parameters between different groups, with a significance level of P < .05. Logistic regression and receiver operating characteristic (ROC) analysis with 95% confidence interval were conducted for the diagnostic efficiency evaluation. ResultsPR-positive group had significantly elevated T2 histogram parameters (P = .001-.049) compared to the PR-negative group. The multivariate logistic regression model with T2 showed the highest area under the ROC curve (AUC) for predicting PR expression (AUC=0.818). Additionally, the multivariate model also had the best diagnostic performance for predicting meningioma S100 expression (AUC=0.768). ConclusionThe MOLED technique-derived T2 maps can distinguish PR and S100 status in meningiomas preoperatively.

Low incidence of occult colorectal carcinoma in a cohort of 150 consecutive patients with diverticular strictures

BackgroundThe symptoms of stricturing diverticulitis can overlap with those of colorectal cancer. Additionally, the stricture itself may mask a “hidden” colorectal cancer. We aimed to describe the demographics, operative details and outcomes, including occult colorectal cancer, in a cohort of consecutive resections for presumed diverticular strictures. MethodsIn this single-center, retrospective cohort study, all patients undergoing resection for a presumed diverticular stricture between January 2010 and December 2015 were included. Preoperative imaging and colonoscopies were individually reviewed. Only patients with radiographically, endoscopically and/or intraoperatively benign-appearing strictures were included. ResultsOne hundred fifty patients (72.7% female, mean age = 70.4 ± 11.8 years, 62.7% elective) were included. Only 34 (22.7%) had a complete preoperative colonoscopy. In 95 (63.6% of cohort) patients, the stricture was non-traversable colonoscopically. Overall, 47 (31.3%) patients did not have complete preoperative imaging or a colonoscopy.In total, 53.3% were open procedures and 62% had non-diverted primary anastomosis. Eleven (14.7%) underwent resection of adjacent organs (5 appendixes/5 right colons/7 fallopian tubes ± ovaries/3 small bowel resections/2 partial cystectomies/1 spleen). The median length of stay was 7 (5, 12.5) days. Only 2 cancers (1.3% of patients) involving the stricture (1 invasive moderately differentiated sigmoid adenocarcinoma/1 lymphoma) were found. Three additional cancers were found in organs involved in the inflammatory process (20% of concomitantly resected organs, 1 ovarian carcinoma/1 leukemia in a lymph node/1 appendiceal tumor). ConclusionDespite approximately one-third of the cohort not having undergone successful preoperative colonoscopy or imaging, the rate of neoplasia involving diverticular strictures was only 1.3%. A relatively high cancer rate was found in concomitantly resected organs involved in the stricturing process.

Saphenous vein aneurysms characterization and treatment: A 36-year single center experience.

Superficial vein aneurysms (SVA) are rare vascular pathologies associated with trauma, chronic venous disease, and venous malformation. We retrospectively reviewed cases of SVA treated from 1986 to 2022. Out of 2463 venous procedures, 16 patients were found with 19 GSV and 1 SSV aneurysms, with 88% presenting with a palpable mass. Varicose veins were noted in 94% of patients, 81% had concomitant reflux, 15% had thrombus within the aneurysm sac, and 19% demonstrated multiple aneurysms. All patients underwent ligation and excision. Post-procedure, 55% of patients received anticoagulants, and 85% received compression. Mean follow-up was 19.4months, with no aneurysm recurrence. We propose a modification to the current SVA classification. The prevalence of multiple aneurysms suggests the need for complete limb imaging in affected patients. Surgical management of SVA was effective in preventing SVA recurrence, while the proposed classification modification will aid in future SVA management.