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Net‐zero carbon targets drive the development of new underground activities such as hydrogen storage and in situ critical mineral recovery, all of which involve geochemical reactions between minerals and fluid/ion transport. Understanding these processes is key to optimizing efficiency and minimizing environmental impacts. However, the fundamental mechanisms of ion transport, mineral dissolution, and secondary precipitation remain poorly understood, particularly at the pore scale. This gap partly arises from the challenges of characterizing samples at such a fine scale, where fluid/ion transport and reactions occur simultaneously. Herein, a core‐to‐pore‐scale experimental approach, combined with time‐lapse three‐dimensional (3D) imaging, is designed to characterize fluid/ion transport, dissolution, and precipitation processes. We implemented this workflow in an electrokinetic in situ recovery (EK‐ISR) system. Time‐lapse 3D micro‐computed tomography (micro‐CT) images were acquired during the experiment to observe dissolution and precipitation dynamics and to measure pore‐scale physical parameters. Findings indicate uniform reactive ion transport and mineral dissolution under EK conditions, with over 78% of the target mineral dissolved. Time‐lapse images reveal multiple dissolution and precipitation patterns that influence reactive transport processes. Geochemical modeling based on pore‐scale parameters demonstrates over 90% correlation with core‐scale experimental data. Our workflow demonstrates a promising capability for characterizing reactive transport processes across pore‐to‐core scales.

This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan–Meier curves (p = 0.0066) in the test dataset. This study’s findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.

Purpose To evaluate the optimal period of replanning to spare the rectal dose by investigating daily rectal movements during computed tomography (CT) image‐guided proton therapy for prostate cancer. Materials and methods To evaluate the optimum reference period for replanning, we analyzed 1483 sets of daily CT (dCT) images acquired from 40 prostate cancer patients and measured the daily rectal movement along the anterior–posterior direction based on the simulator CT (sCT) images and dCT images. We calculated daily dose distributions based on initial plans on the sCT images and replans on the dCT images for 13 representative patients, and evaluated daily dose volume histograms (DVHs) for the prostate, seminal vesicles, and rectum. Results The rectal anterior side on the dCT images around the seminal vesicles largely deviated toward the anterior side relative to the position on the reference sCT images, but the deviation decreased by referring to the dCT images and became nearly zero when we referred to the dCT images after 10‐day treatment. The daily DVH values for the prostate showed good dose coverage. For six patients showing rectal movement toward the anterior side, the daily rectal DVH (V77%) showed a 3.0 ± 1.7 cc excess from the initial plan and this excess was correlated with 9.9 ± 6.8 mm rectal movement. To identify the patients (37.5% in total) for whom the replanning on the 10th‐day and 20th‐day CT images reduced the V77% excess to 0.4 ± 1.5 cc and −0.2 ± 1.3 cc, respectively, we evaluated the accumulated mean doses with a 1.2 cc criterion. Conclusion Our data demonstrate that the daily movement of the rectal anterior side tends to move toward the anterior side, which results in a rectal overdose, and the mean of the movement gradually decreases with the passage of days. In such cases, replanning with the reference CT after 10 days is effective to spare the rectal dose.

Three-dimensional reconstruction plays a vital role in assisting doctors and surgeons in diagnosing the healing progress of bone defects. Common three-dimensional reconstruction methods include surface and volume rendering. As the focus is on the shape of the bone, this study omits the volume rendering methods. Many improvements have been made to surface rendering methods like Marching Cubes and Marching Tetrahedra, but not many on working towards real-time or near real-time surface rendering for large medical images and studying the effects of different parameter settings for the improvements. Hence, this study attempts near real-time surface rendering for large medical images. Different parameter values are experimented on to study their effect on reconstruction accuracy, reconstruction and rendering time, and the number of vertices and faces. The proposed improvement involving three-dimensional data smoothing with convolution kernel Gaussian size 5 and mesh simplification reduction factor of 0.1 is the best parameter value combination for achieving a good balance between high reconstruction accuracy, low total execution time, and a low number of vertices and faces. It has successfully increased reconstruction accuracy by 0.0235%, decreased the total execution time by 69.81%, and decreased the number of vertices and faces by 86.57% and 86.61%, respectively.

Objective To assess the accuracy of a virtual stenting tool based on coronary CT angiography (CCTA) and fractional flow reserve (FFR) derived from CCTA (FFR CT Planner) across different levels of image quality. Materials and methods Prospective, multicenter, single-arm study of patients with chronic coronary syndromes and lesions with FFR ≤ 0.80. All patients underwent CCTA performed with recent-generation scanners. CCTA image quality was adjudicated using the four-point Likert scale at a per-vessel level by an independent committee blinded to the FFR CT Planner. Patient- and technical-related factors that could affect the FFR CT Planner accuracy were evaluated. The FFR CT Planner was applied mirroring percutaneous coronary intervention (PCI) to determine the agreement with invasively measured post-PCI FFR. Results Overall, 120 patients (123 vessels) were included. Invasive post-PCI FFR was 0.88 ± 0.06 and Planner FFR CT was 0.86 ± 0.06 (mean difference 0.02 FFR units, the lower limit of agreement (LLA) − 0.12, upper limit of agreement (ULA) 0.15). CCTA image quality was assessed as excellent (Likert score 4) in 48.3%, good (Likert score 3) in 45%, and sufficient (Likert score 2) in 6.7% of patients. The FFR CT Planner was accurate across different levels of image quality with a mean difference between FFR CT Planner and invasive post-PCI FFR of 0.02 ± 0.07 in Likert score 4, 0.02 ± 0.07 in Likert score 3 and 0.03 ± 0.08 in Likert score 2, p  = 0.695. Nitrate dose ≥ 0.8mg was the only independent factor associated with the accuracy of the FFR CT Planner (95%CI − 0.06 to − 0.001, p  = 0.040). Conclusion The FFR CT Planner was accurate in predicting post-PCI FFR independent of CCTA image quality. Clinical relevance statement Being accurate in predicting post-PCI FFR across a wide spectrum of CT image quality, the FFR CT Planner could potentially enhance and guide the invasive treatment. Adequate vasodilation during CT acquisition is relevant to improve the accuracy of the FFR CT Planner. Key Points • The fractional flow reserve derived from coronary CT angiography (FFR CT ) Planner is a novel tool able to accurately predict fractional flow reserve after percutaneous coronary intervention. • The accuracy of the FFR CT Planner was confirmed across a wide spectrum of CT image quality. Nitrates dose at CT acquisition was the only independent predictor of its accuracy. • The FFR CT Planner could potentially enhance and guide the invasive treatment. Graphical abstract

Craniosynostosis is a congenital malformation of the infant skull typically treated via corrective surgery. To accurately quantify the extent of deformation and identify the optimal correction strategy, the patient-specific skull model extracted from a pre-surgical computed tomography (CT) image needs to be registered to an atlas of head CT images representative of normal subjects. Here, the authors present a robust multi-stage, multi-resolution registration pipeline to map a patient-specific CT image to the atlas space of normal CT images. The proposed registration pipeline first performs an initial optimisation at very low resolution to yield a good initial alignment that is subsequently refined at high resolution. They demonstrate the robustness of the proposed method by evaluating its performance on 560 head CT images of 320 normal subjects and 240 craniosynostosis patients and show a success rate of 92.8 and 94.2%, respectively. Their method achieved a mean surface-to-surface distance between the patient and template skull of <2.5 mm in the targeted skull region across both the normal subjects and patients.

A critical step in detection of primary intracerebral haemorrhage (ICH) is an accurate assessment of computed tomography (CT) brain images. The correct diagnosis relies on imaging modality and quality of acquired images. The authors present an enhancement algorithm which can improve the clarity of edges on CT images. About 40 samples of CT brain images with final diagnosis of primary ICH were obtained from the UKM Medical Centre in Digital Imaging and Communication in Medicine format. The images resized from 512 × 512 to 256 × 256 pixel resolution to reduce processing time. This Letter comprises of two main sections; the first is denoising using Wiener filter, non-local means and wavelet; the second section focuses on image enhancement using a modified unsharp masking (UM) algorithm to improve the visualisation of ICH. The combined approach of Wiener filter and modified UM algorithm outperforms other combinations with average values of mean square error, peak signal-to-noise ratio, variance and structural similarity index of 2.89, 31.72, 0.12 and 0.98, respectively. The reliability of proposed algorithm was evaluated by three blinded assessors which achieved a median score of 65%. This approach provides reliable validation for the proposed algorithm which has potential in improving image analysis.

The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The numerous algorithms had been proposed earlier by many researchers in the past, but, the accuracy of prediction is always a challenging task. In this work, an artificial neural network based methodology is proposed to find the irregular growth of lung tissues. Higher probability of detection is taken as a goal to get an automated tool, with great accuracy. The finest feature sets derived from Haralick Gray level co occurrence Matrix and used as the dimension reduction way for feeding neural network. In this work, a binary Binary classifier neural network has been proposed to identify the normal images out of all the images. The capability of the proposed neural network has been quantitatively computed using confusion matrix and found in terms of classification accuracy.

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset

Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).