Resolution Computed Tomography Images Research Articles

Soil CT image quality enhancement via an improved super-resolution reconstruction method based on GAN

Computed tomography (CT) is an effective instrument to characterize the internal structure of soil. However, the resolution of soil CT images is often limited by the physical properties of the scanner and the imaging protocol, which can lead to difficulties in accurately characterizing fine-scale features of soil. At present, the super-resolution reconstruction results of CT images often have the problems of blurred feature boundaries and low image quality, causing inaccuracies in the analysis of soil structure. Therefore, this study developed an improved super-resolution reconstruction method based on generative adversarial networks (SRLGAN) to accurately reconstruct high-resolution CT images from low-resolution CT images and assist in digital soil descriptions. SRLGAN utilized a lightweight CNN super-resolution reconstruction module as the generator model, which can improve the reconstruction image quality while reducing the number of trainable parameters and inference time. Meanwhile, a closed-loop loss function was employed to reduce input image information loss and improve method generalization. Compared with traditional reconstruction methods and deep learning methods, the proposed SRLGAN method showed superior performance with a higher peak signal-to-noise ratio (PSNR) of 45.869 dB and a structural similarity index (SSIM) of 0.992. Particularly, the PSNR was 9.95% and 4.49% higher than the best-performing traditional method (Bicubic) and deep learning method (SRGAN), respectively. Furthermore, compared to the SRGAN, trainable parameters and inference time of the SRLGAN have decreased by 77.84% and 57.86%, respectively, which indicates the highest degree of lightweighting. This study demonstrated that SRLGAN can generate high-quality, high-resolution soil CT images that can be used for subsequent segmentation and 3D structural analysis, offering an intelligent approach to understanding the internal soil structure.

Super‐resolution reconstruction of vertebrate microfossil computed tomography images based on deep learning

AbstractMicropaleontologists use the fine structures of microfossils to extract evolutionary information. These structures could not be directly observed with the naked eye. Recently, paleontologists resort to computed tomography (CT) images to mine the information, and pursue higher resolution CT images with in‐depth research. Therefore, we propose a new model, weighted super‐resolution generative adversarial network (WSRGAN), for the super‐resolution reconstruction of CT images. The model proposed herein (WSRGAN) obtained higher LPIPS (0.0757) on the experimental dataset, compared with Bilinear (0.4289), Bicubic (0.4166), EDSR (0.2281), WDSR (0.2640), and SRGAN (0.0815). WSRGAN meets the requirements of paleontologists for reconstructing fish microfossils. We hope that more super‐resolution reconstruction methods based on deep learning could be applied to paleontology and achieve better performance.

Computed Tomography Chest Findings in COVID-19 Patients.

COVID-19 which has caused significant morbidity and mortality around the world has been declared by the World Health Organization to be a global health emergency. Our objective was to find out the lung parenchymal patterns commonly evident in high resolution Computed Tomography in patients with COVID-19 pneumonia. A retrospective cross-sectional study was conducted at a tertiary multi-specialty hospital in Kathmandu, Nepal. With ethical clearance from the institutional review board, a total of 235 patients with positive reverse transcriptase polymerase chain reaction for COVID-19 and having respiratory symptoms were included in the study. High Resolution Computed Tomography images of chest were retrieved from picture archiving and communication systems retrospectively and studied for the findings commonly attributed to COVID-19 pneumonia. The data was then analyzed using Stata version 14 (Stata Corp, College Station, TX, USA). Descriptive statistics were presented as mean and median while chi-square test was used to assess the association between socio-demographic characteristics and CT severity indices. Out of 235 patients, 174 (74.0%) were males and 61(26%) were females with a mean age of 54.8±14.5 years. The most commonly encountered pattern of pulmonary changes was bilateral involvement in 222 (94.5%) patients followed by ground-glass opacities in 218 (92.8%) patients and peripheral predominance of ground-glass opacities in 211 (89.8%) patients. Chest Computed Tomography abnormalities are common in COVID-19 positive patients with respiratory symptoms. These findings can guide in the assessment of the severity of the disease as well as patient management.

Neuroanatomy of the crocodylomorph Portugalosuchus azenhae from the late cretaceous of Portugal.

We present the first detailed braincase anatomical description and neuroanatomical study of Portugalosuchus azenhae, from the Cenomanian (Late Cretaceous) of Portugal. This eusuchian crocodylomorph was originally described as a putative Crocodylia and one of the oldest representatives of this clade; however, its phylogenetic position remains controversial. Based on new data obtained from high resolution Computed Tomography images (by micro-CT scan), this study aims to improve the original description of this taxon and also update the scarce neuroanatomical knowledge of Eusuchia and Crocodylia from this time interval, a key period to understand the origin and evolution of these clades. The resulting three-dimensional models from the CT data allowed a detailed description of its well-preserved neurocranium and internal cavities. Therefore, it was possible to reconstruct the cavities of the olfactory region, nasopharyngeal ducts, brain, nerves, carotid arteries, blood vessels, paratympanic sinus system and inner ear, which allowed to estimate some neurosensorial capabilities. By comparison with other crocodylomorphs, these analyses showed that Portugalosuchus, back in the Cenomanian, already displayed an olfactive acuity, sight, hearing and cognitive skills within the range of that observed in other basal eusuchians and crocodylians, including extant species. In addition, and in order to test its disputed phylogenetic position, these new anatomical data, which helped to correct and complete some of the original observations, were included in one of the most recent morphology-based phylogenies. The position of Portugalosuchus differs slightly from the original publication since it is now located as a "thoracosaurid" within Gavialoidea, but still as a crocodylian. Despite all this, to better contrast these results, additional phylogenetic analyses including this new morphological character coding together with DNA data should be performed.

Design and realization of a miniaturized high resolution computed tomography imaging spectrometer

The computed tomography imaging spectrometer (CTIS) is a relatively unknown snapshot hyperspectral camera. It utilizes computational imaging approaches to gain the hyperspectral image from a spatio-spectral smeared sensor image. We present a strongly miniaturized system with a dimension of only 36 × 40.5 × 52.8 mm and a diagonal field of view of 29°. We achieve this using a Galilean beam expander and a combination of off-the-shelf lenses, a highly aspherical imaging system from a commercial smartphone, and a 13 MP monochrome smartphone image sensor. The reconstructed hyperspectral image has a spatial resolution of 400 × 300 pixel with 39 spectral channels.

Texture transformer super-resolution for low-dose computed tomography

Computed tomography (CT) is widely used to diagnose many diseases. Low-dose CT has been actively pursued to lower the ionizing radiation risk. A relatively smoother kernel is typically used in low-dose CT to suppress image noise, which may sacrifice spatial resolution. In this work, we propose a texture transformer network to simultaneously reduce image noise and improve spatial resolution in CT images. This network, referred to as Texture Transformer for Super Resolution (TTSR), is a reference-based deep-learning image super-resolution method built upon a generative adversarial network (GAN). The noisy low-resolution CT (LRCT) image and the routine-dose high-resolution (HRCT) image are severed as the query and key in a transformer, respectively. Image translation is optimized through deep neural network (DNN) texture extraction, correlation embedding, and attention-based texture transfer and synthesis to achieve joint feature learning between LRCT and HRCT images for super-resolution CT (SRCT) images. To evaluate SRCT performance, we use the data from both simulations of the XCAT phantom program and the real patient data. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity (FSIM) index are used as quantitative metrics. For comparison of SRCT performance, the cubic spline interpolation, SRGAN (a GAN super-resolution with an additional content loss), and GAN-CIRCLE (a GAN super-resolution with cycle consistency) were used. Compared to the other two methods, TTSR can restore more details in SRCT images and achieve better PSNR, SSIM, and FSIM for both simulation and real-patient data. In addition, we show that TTSR can yield better image quality and demand much less computation time than high-resolution low-dose CT images denoised by block-matching and 3D filtering (BM3D) and GAN-CIRCLE. In summary, the proposed TTSR method based on texture transformer and attention mechanism provides an effective and efficient tool to improve spatial resolution and suppress noise of low-dose CT images.

Role of Multislice CT Imaging in Predicting the Visibility of the Round Window during Cochlear Implantation

Aim: To envisage the visibility of round window nichein pediatric cochlear implantation by using computed tomography. Study design: Retrospective study Place and duration of study: Department of Radiology, Chandka Medical College Hospital, Larkana from 1st July 2021 to 31st March 2022. Methodology: Fifty five patients underwent the procedure of unilateral-cochlear implantation. Slices of round window niche and round window membrane were selected for visualization through HRCT imaging. The temporal bone imaging through high resolution computed tomography scans; before and after the operation were examined in detailed and scored. Each round window membrane and round window niche was then visualized for its visibility. The sensitivity as well as specificity of high resolution computed tomography imaging in prediction of round window membrane visuality were calculated. Results: The comparison of predictive values showed that the positive-predictive value of RWM was 73.1% and negative as 91.2%. The sensitivity as well as the specificity of HRCT identification in prediction difficulty in visualizing RWM was 79% and 87% respectively. Conclusion: Pre-operative high resolution computed tomography was proved helpful in visualization of detecting round window niche. Key words: CT scan; Cochlear implantation; Round window niche; Pediatrician

MDCT에서 ACR 팬텀의 구성 물질에 따른 공간분해능 연구

The modulation transfer function (MTF) is well known as a crucial parameter in quality assurance of computed tomography (CT) scanners that provides detailed information of both contrast and resolution of CT images. In this study, modulation transfer function (MTF) of multi-detector computed tomography (MDCT) was evaluated by changing the substance of phantom and the measurement parameters. The analysis of MTF gives following results: 1) Bone (the highest contrast material) and air (the lowest contrast material) exhibited variance by 0.030 lp/cm at MTF(50%). 2) In addition, slice thickness of 1.25-mm and 10.0-mm exhibited 0.110 lp/cm and 0.065 lp/cm at MTF(50%), respectively. 3) MTF (50%) showed 50.31% difference depending on the image reconstruction algorithm. Especially, bone algorithm showed higher MTF (50%) than standard algorithm by 26.69%. 4) In addition, the Full (360°) mode linear interpolation showed higher MTF (50%) than the Plus (540°) mode by 10.71%. For a accurate measurement of MTF while performing CT quality control, a phantom module which consists of diverse substance is required. Also, the optimal image which reflecting the size and shape of lesion can be obtained with a suitable scanning factor for diagnosis.

Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration.

Root and root‐derived biopore interactions in soils: A review

AbstractIn recent years, the investigation of root–biopore interactions has been reported in many studies, particularly with the application of X‐ray computed tomography (CT). To clarify our understanding of root–biopore interactions, we reviewed the effects of plant roots on soil structure and the biopore effects on plant growth and uptake of nutrients and water. Plant roots can penetrate the soil matrix and form biopores after decay. The biopores are cylindrical, continuous, and stable and facilitate the transport of air and water. The soil around biopores also affects plant growth by its rich nutrients; hence, the concept of a biopore sheath is proposed to indicate the soil affected by the roots and resulting biopores. The behavior of roots encountering biopores is impacted by soil strength, oxygen concentration, biopore characteristics, and crop species. The biopores provide many channels with the least mechanical resistance for crop roots to enter the subsoil, improving the absorption of water and nutrients by crops. However, the effect of biopores on crop growth depends on biopore size. The smaller biopores (<2 mm) promoted plant growth, but the larger biopores did not (≥2 mm), which was mainly related to the degree of contact between root and soil in biopores. However, quantification of root–biopore wall contact is difficult due to the low resolution of CT images, which limits the understanding of the effects of biopore characteristics on plant growth and nutrient uptake. Therefore, determining the contact area between roots and soil in the biopores and revealing the contribution of biopore characteristics to water and nutrient uptake are important to give full play to the functions of biopores in sustainable agriculture.

Pattern and prevalence of different findings in high resolution computed tomography images in patients with coronavirus disease and kidney injury; a pilot study

Introduction: Since December 2019, an outbreak of pneumonia caused by a new coronavirus has emerged. The standard diagnostic method for COVID-19 infection is the real-time reverse transcriptase-polymerase chain reaction (RT-PCR). High-resolution computed tomography (HRCT) has been proven as a sensitive, feasible and accessible test in Iran. In addition to respiratory system, other organs could also be involved in this disease. The exact mechanism of renal involvement is unknown; however, acute kidney injury (AKI) occurs in almost 5-15% of cases. Different HRCT patterns might be associated with AKI presence and severity of the disease. Objectives: To investigate patterns and prevalence of different HRCT findings in COVID-19 patients with concurrent AKI. Patients and Methods: In this retrospective study, we reviewed all hospitalized patients with COVID-19 infection, from February to April 2020 in Razi hospital, Rasht. Twenty-two cases who had AKI were enrolled. The HRCT findings of the patients were reviewed independently by two radiologists. Percentage and prevalence of HRCT findings were analyzed in SPSS 21 software. Results: All 22 cases had multifocal distribution on HRCT. Around 95.5% had peripheral involvement, 86.4% had central zones opacity while 72.2% of cases had peribronchovascular pattern. Bilateral lung involvement was found in 90.9%, but only 9.1% had unilateral involvement. There was 59.1% of lower lobe predominance for COVID-19 involvement since sub-pleural regions were spared in 18.2% of individuals. All the patients’ HRCTs showed ground glass opacity. Reticular pattern (81.8%), consolidation (77.3%), vascular enlargement in involved zones (68.2%) and airway changes (68.2%) were the next more prevalent findings. Half of the subjects showed crazy paving, 45.5% had pleural effusion and 13.6% had also lymphadenopathy. We found 40.9% of the patients had arcade-like sign. Less frequent findings were nodular opacities (13.6%), halo sign (9.1%) and reverse-halo sign (9.1%), respectively. Conclusion: This study demonstrated that atypical patterns are likely to be more common in COVID-19 patients with kidney injury.

A two-stage deep generative adversarial quality enhancement network for real-world 3D CT images

High-quality (HQ) three-dimensional (3D) images are the premise of analyzing the properties of porous media such as rocks. X-ray computed tomography (CT) is one of the most widely used imaging tools to capture the 3D images of rock samples. Nevertheless, the quality (e.g., resolution, sharpness, and the signal-to-noise ratio) of the collected rock CT images may not meet the needs of practical applications in some cases due to the limitations of imaging systems, leading to inaccurate results of property analysis. In this paper, aiming at improving the quality of rock CT images as well as the accuracy of property analysis, we develop a two-stage deep generative adversarial quality enhancement network for real-world 3D CT images, namely the CTQENet. More specifically, the proposed CTQENet consists of a two-dimensional (2D) reconstruction module (2DRM) and a 3D fusion module (3DFM), which enhance the quality of 3D CT images from the perspective of 2D slices and 3D volumes, respectively. In order to remove artifacts and enhance the resolution of real-world CT images, the 2DRM takes the cycle-consistent generative adversarial network as the backbone to learn the mapping from low-quality (LQ) 2D slices to HQ ones without one-to-one paired training data. Then, the 3D CT volumes stacked by the reconstructed HQ slices along the x/y/z-axis are adaptively fused in the generative adversarial network-based 3DFM, to achieve more reliable 3D morphological structures. Qualitative and quantitative comparisons show the effectiveness of the proposed CTQENet for real-world 3D CT images of rock samples. In particular, the reconstructed HQ 3D CT images by CTQENet show similar morphological characteristics and statistical properties with HQ targets. This study makes it possible to obtain higher quality 3D CT images that partly exceed the limitations of CT imaging systems for better visual experience and more accurate property analysis.

Super Resolution CT Imaging Using Higher Order Total Variation (HOTV) Technique

The present study focuses on the performance upgradation of a table-top computed tomography (CT) scanning instrument. The size restriction of the detectors of table-top CT scanner imposes limitation on the allowable size of the object to be scanned. The object with increasing size has to be put in a far end from the X-ray source to be fit into the scan field of view of the scanner. This situation affects the pixel resolution of the tomography images. The limited resolution capacity restricts the CT image zoomed-in view facility which is very often an essential requirement for scientific measurement and analysis. The hardware-based solution involving larger detectors will significantly increase the monetary cost, and thus a cost-effective solution is very much required. The new super resolution (SR) or resolution up-scaling technique based on higher order total variation minimization has been proposed to overcome the above-mentioned problem associated with a table-top tomography instrument. The proposed technique is capable of producing SR image from a single low-resolution image. The experimental results have been compared with some of the existing SR techniques, in terms of various image quality parameters (mean squared error, correlation coefficient, structural similarity index, and feature similarity index) to show betterment over other existing SR techniques. The experiments are carried out with different objects to validate the universality of the proposed scheme. The pixel-enhanced tomographic images are further checked with Sobolev space error analysis to ensure the quality of the images from tomography perspective. It can be inferred that the presented technique can properly up-scale the resolution of CT images, and therefore it presents a cost-effective possible solution to the resolution problem caused by the size constraint of the detectors of table-top CT scanning instruments.

Design and realization of a miniaturized high resolution computed tomography imaging spectrometer

The computed tomography imaging spectrometer (CTIS) is a relatively unknown snapshot hyperspectral camera. It utilizes computational imaging approaches to gain the hyperspectral image from a spatiospectral smeared sensor image. We present a strongly miniaturized system with a dimension of only 36mm x 40.5mm x 52.8mm and a diagonal field of view of 29°. We achieve this using a Galilean beam expander and a combination of off-the-shelf lenses, a highly aspherical imaging system from a commercial smartphone and a 13 MP monochrome smartphone image sensor. The reconstructed hyperspectral image has a spatial resolution of 400 x 300 pixel with 39 spectral channels.

DESPITE THEIR UTILITY IN IDIOPATHIC PULMONARY FIBROSIS, SERUM IGA AND MONOCYTE COUNT ARE NOT PREDICTIVE OF BASELINE PULMONARY FUNCTION, MORTALITY, OR INTERSTITIAL LUNG DISEASE PATTERN IN RHEUMATOID ARTHRITIS-ASSOCIATED INTERSTITIAL LUNG DISEASE

TOPIC: Diffuse Lung Disease TYPE: Original Investigations PURPOSE: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease that confers a poor prognosis. Previously, immunoglobulin A (IgA) level has been used as a surrogate marker of transforming growth factor (TGF)-beta, an important mediator of pulmonary fibrosis, and increased IgA level was associated with worse outcomes in IPF [1]. Recent studies have also described associations between serum monocyte count and clinical outcomes in IPF [2]. Rheumatoid arthritis related interstitial lung disease (RA-ILD) has many similarities with IPF, including similar genetic risk, survival, and ILD pattern. We hypothesized that elevated IgA and monocyte count would correlate with clinical outcomes and ILD patterns in RA-ILD. METHODS: We performed a single-center, retrospective chart review of patients diagnosed with RA-ILD between 2010 and 2021 using an ICD9 and ICD10 based search. Of 256 patients who met our search criteria, 77 were included in the final analysis. Patients were included if they had serum IgA and monocyte count in the chart, a confirmed diagnosis of RA based on ACR/EULAR criteria, and ILD based on chart diagnosis and confirmatory imaging. Patients were excluded if they received rituximab prior to their blood draw. High resolution computed tomography images closest to the date of the blood draw were analyzed by consensus reads by two blinded thoracic radiologists using the 2018 ATS/ERS/JRS/ALAT guidelines. IgA level and monocyte count were compared to pulmonary function at time of blood draw, time to death or lung transplant, UIP pattern as well as sex, ethnicity and age. RESULTS: The mean and median IgA levels were 342.8 mg/dl and 329.0 mg/dl, respectively. There was no statistically significant association between IgA level and the UIP pattern (p = 0.689). IgA level was not associated with baseline FVC% predicted or baseline DLCO% predicted (p = 0.218 and p = 0.639). The effect of IgA level on overall survival is not significant in the simple Cox regression model [hazard ratio (HR) = 1.002, 95% confidence interval (CI) 0.999-1.004, p = 0.203].The mean and median absolute monocyte counts were 6.5×108 cells/liter and 6.0×108 cells/liter, respectively. There was no association between monocyte count and the UIP pattern (p = 0.762). Monocyte count was not associated baseline with FVC% predicted (p = 0.963) or baseline DLCO% predicted (p = 0.882). The effect of monocyte count on overall survival is not significant in the simple Cox regression model (HR = 1.099, 95% CI 0.449-2.690, p = 0.836). CONCLUSIONS: Serum IgA and monocyte concentration were not associated with important clinical outcomes in this RA-ILD cohort. This was a retrospective analysis, it may be that prospective, longitudinal analysis of IgA and monocyte count in RA-ILD could yield different results. CLINICAL IMPLICATIONS: N/A DISCLOSURES: No relevant relationships by Joe Johnstone, source=Web Response Consultant relationship with Boehringer Ingelheim Please note: $1001 - $5000 by Joyce Lee, source=Web Response, value=Consulting fee Consultant relationship with Genentech Please note: $1001 - $5000 by Joyce Lee, source=Web Response, value=Consulting fee Consultant relationship with Celgene Please note: $1001 - $5000 by Joyce Lee, source=Web Response, value=Consulting fee Consultant relationship with Galapagos Please note: $1001 - $5000 by Joyce Lee, source=Web Response, value=Consulting fee Consultant relationship with Eleven P15 Please note: $1001 - $5000 by Joyce Lee, source=Web Response, value=Consulting fee No relevant relationships by Scott Matson, source=Web Response No relevant relationships by Sahil Pandya, source=Web Response No relevant relationships by Kristen Pope, source=Web Response No relevant relationships by Xiaosong Shi, source=Web Response No relevant relationships by Christopher Walker, source=Web Response

Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

In this paper, automated detection of interstitial lung disease patterns in high resolution computed tomography images is achieved by developing a faster region-based convolutional network based detector with GoogLeNet as a backbone. GoogLeNet is simplified by removing few inception models and used as the backbone of the detector network. The proposed framework is developed to detect several interstitial lung disease patterns without doing lung field segmentation. The proposed method is able to detect the five most prevalent interstitial lung disease patterns: fibrosis, emphysema, consolidation, micronodules and ground-glass opacity, as well as normal. Five-fold cross-validation has been used to avoid bias and reduce over-fitting. The proposed framework performance is measured in terms of F-score on the publicly available MedGIFT database. It outperforms state-of-the-art techniques. The detection is performed at slice level and could be used for screening and differential diagnosis of interstitial lung disease patterns using high resolution computed tomography images.

Adoption of Computed Tomography Images under Iterative Reconstruction Algorithm in Diagnosis of Gastric Cancer

Objective. This work aimed to study the application of iterative reconstruction algorithm-based computed tomography (CT) imaging in the diagnosis of gastric cancer (GC). Methods. 40 cases of GC patients diagnosed by gastroscopy biopsy and pathology in hospital were retrospectively analyzed. Scanning images of the upper abdomen were obtained after plain scanning and double-phase enhanced scanning. Then, the image was reconstructed by the iterative reconstruction algorithm, and the CT value under the algorithm was analyzed statistically. Results. It was revealed that the detection rate of both spiral CT and iterative reconstruction algorithm-based CT was 100%. After the iterative reconstruction algorithm, the image quality, image information, and image mean square error (MSE) were notably improved. The degree of tumor invasion (T) staging accuracy was 82.6%, lymph node metastasis (N) staging accuracy was 73.2%, and tumor node metastasis (TNM) staging accuracy was 79.1%. The accuracy of the iterative reconstruction algorithm-based CT was 90% for T staging, 83% for N staging, and 85.5% for TNM staging. Conclusion. Iterative reconstruction algorithm can effectively improve the spatial resolution of CT images in GC diagnosis, with high accuracy. It can provide reliable and objective imaging data for the diagnosis of GC clinically, which was worthy of further application in clinical practice.

An update on interstitial lung disease.

Interstitial lung diseases are a complex group of conditions that cause inflammation and scarring of the lung interstitium. This article discusses the diagnosis and management of common interstitial lung diseases including idiopathic pulmonary fibrosis, hypersensitivity pneumonitis, connective tissue disease associated-interstitial lung disease, sarcoidosis and drug-induced interstitial lung disease. A multidisciplinary approach to diagnosis of interstitial lung disease is the gold standard; key history and examination features, blood panel, pulmonary function tests, high resolution computed tomography imaging, and when required bronchoalveolar lavage and lung biopsy results are discussed to reach a multidisciplinary consensus diagnosis. Advances, including the development of the disease-modifying anti-fibrotic medications nintedanib and pirfenidone, continue to shape the future management of interstitial lung disease. A holistic approach to the care of patients with interstitial lung disease is paramount, as they often have a high symptom burden and considerable palliative care needs.

The Distribution of Skull Score and Skull Density Ratio in Tremor Patients for MR-Guided Focused Ultrasound Thalamotomy.

ObjectiveMagnetic resonance-guided focused ultrasound (MRgFUS) is a minimum-invasive surgical approach to non-incisionally cause the thermos-coagulation inside the human brain. The skull score (SS) has already been approved as one of the most dominant factors related to a successful MRgFUS treatment. In this study, we first reveal the SS distribution of the tremor patients, and correlate the SS with the image feature from customized skull density ratio (cSDR). This correlation might give a direction to future clinical studies for improving the SS.MethodsTwo hundred and forty-six patients received a computed tomography (CT) scan of the brain, and a bone-enhanced filter was applied and reconstructed to a high spatial resolution CT images. The SS of all patients would be estimated by the MRgFUS system after importing the reconstructed CT images into the MRgFUS system. The histogram and the cumulative distribution of the SS from all the patients were calculated to show the percentage of the patients whose SS lower than 0.3 and 0.4. The same CT images of all patients were utilized to calculated the cSDR by first segmented the trabecular bone and the cortical bone from the CT images and divided the average trabecular bone intensity (aTBI) by the average cortical bone intensity (aCBI). The Pearson’s correlations between the SS and the cSDR, aTBI, and the aCBI were calculated, respectively.ResultsThere were 19.19 and 50% of the patient who had the SS lower than the empirical threshold 0.3 and 0.4, respectively. The Pearson’s correlation between the SS and the cSDR, aCBI, and the aTBI were R = 0.8145, 0.5723, and 0.8842.ConclusionHalf of the patients were eligible for the MRgFUS thalamotomy based on the SS, and nearly 20% of patients were empirically difficult to achieve a therapeutic temperature during MRgFUS. The SS and our cSDR are highly correlated, and the SS had a higher correlation with aTBI than with aCBI. This is the first report to explicitly reveal the SS population and indicate a potential way to increase the chance to achieve a therapeutic temperature for those who originally have low SS.

Oxygen diffusivity mapping of fruit and vegetables based on X-ray CT

Oxygen (O 2 ) diffusion affects respiration of fruit and vegetables and, thus, their behavior under controlled or modified atmosphere storage conditions. Effective gas diffusivity expresses, at a macroscopic level, the overall ability of the tissue to exchange gasses and is determined by the porous structure of the tissue. Variations of the tissue microstructure across the fruit and vegetable organs may result in an effective O 2 diffusivity that is position dependent. A method was developed to create three-dimensional (3-D) effective O 2 diffusivity maps in fruit and vegetables based on low resolution X-ray computed tomography (CT) images. The effective tissue diffusivity was first calculated for representative small tissue samples of eggplant, turnip, apple, and pear fruit, using microscale diffusion simulations with a finite volume model on high resolution CT images, which served as ground truth. Then the dependence of the effective O 2 diffusivity on the total and open porosity and tortuosity of the samples was investigated. The regression model of the diffusivity with respect to total porosity had an RMSE = 6.90 × 10 −7 m 2 s −1 and R 2 = 0.92, while that with respect to open porosity had an RMSE = 5.41 × 10 −7 m 2 s −1 and R 2 = 0.95, for all samples and organs. The incorporation of the pore network tortuosity did not improve the goodness of fit of the correlation. The correlation based on total porosity was found sufficient for eggplant and turnip that has a large porosity, while diffusivity profiles in apple and pear fruit correlated better with the open porosity due to the presence of disconnected (closed) pores in these fruits that contribute less to the effective diffusion. Thereto, open porosity was estimated from a correlation model between total porosity and open porosity with RMSE = 5.76% for apple and RMSE = 0.93% for pear. Porosity profiles of organs computed from low resolution CT grayscale images were translated to O 2 diffusivity profiles using the correlation model and tested against the high resolution ground truth values. Finally, effective diffusivity maps were created for intact organs, which for the first time provides insight in the spatial variation in gas exchange capability of different plant species. • Effective O 2 diffusivity in an intact product was mapped based on X-ray CT. • Diffusivity models were developed based on total and open porosity, and tortuosity. • O 2 diffusivity of fruit and vegetables was found heterogeneous.