Article
Multidisciplinary Sciences
Stefanie Van Offenwert, Veerle Cnudde, Marijn Boone, Tom Bultreys
Summary: The study found that pore-scale heterogeneity influences solute transport processes. Using X-ray micro-computed tomography, experiments were conducted to investigate the effect of heterogeneity on different porous materials.
Article
Environmental Sciences
Mohamed Elmorsy, Wael El-Dakhakhni, Benzhong Zhao
Summary: Subsurface processes play a crucial role in addressing major challenges such as sustainable extraction of hydrocarbons, carbon dioxide sequestration, and renewable energy storage. This study presents a novel analytical solution and physics-informed neural network models for accurate permeability prediction and upscaling of three-dimensional digital rock samples. The combination of these models showcases the potential of machine learning in rapid analysis of digital rocks at the core-scale.
WATER RESOURCES RESEARCH
(2023)
Article
Biochemistry & Molecular Biology
Lisa M. Duff, Andrew F. Scarsbrook, Nishant Ravikumar, Russell Frood, Gijs D. van Praagh, Sarah L. Mackie, Marc A. Bailey, Jason M. Tarkin, Justin C. Mason, Kornelis S. M. van der Geest, Riemer H. J. A. Slart, Ann W. Morgan, Charalampos Tsoumpas
Summary: The aim of this study was to develop and validate an automated pipeline for diagnosing active aortitis using radiomic imaging biomarkers from FDG PET-CT images. The aorta was automatically segmented using a CNN. Radiomic features were extracted, and three different radiomic fingerprints were constructed. The pipeline showed good diagnostic performance across multiple datasets, indicating its potential for generalizability and use in automated clinical decision-making.
Article
Engineering, Environmental
Agnese Marcato, Javier E. Santos, Gianluca Boccardo, Hari Viswanathan, Daniele Marchisio, Masa Prodanovic
Summary: The study of solute transport in porous media is important in chemical engineering systems. Neural networks can be trained with flow simulations to predict fields faster and with less computational resources. However, an effective description of the domain and operating conditions is crucial for accurate generalized models. This work trains a multi scale convolutional neural network (MSNet) with a diverse dataset to predict concentration fields in porous media images.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Teemu Makela, Olli Oman, Lasse Hokkinen, Ulla Wilppu, Eero Salli, Sauli Savolainen, Marko Kangasniemi
Summary: In this study, a convolutional neural network (CNN)-based algorithm was developed and evaluated for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. The results were compared to volumes reported by widely used CT perfusion-based RAPID software (IschemaView), showing a moderate level of accuracy.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Computer Science, Artificial Intelligence
Rachel Lea Draelos, Lawrence Carin
Summary: Understanding model predictions in healthcare is crucial, and this research introduces the challenging task of explainable multiple abnormality classification in volumetric medical images. A novel multiple instance learning convolutional neural network, AxialNet, and attention mechanism HiResCAM are proposed, along with a new approach to automatically obtain 3D allowed regions.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Florence M. Muller, Jens Maebe, Christian Vanhove, Stefaan Vandenberghe
Summary: This study investigated the potential of using convolutional neural networks (CNN) to restore high quality micro-CT images from low dose (noisy) images. The results showed that both CNN algorithms exhibited superior performance in noise suppression, structural preservation, and contrast enhancement compared to existing methods. The CNN-based denoising could offer a 2-4x dose reduction.
Article
Computer Science, Artificial Intelligence
Rachel Lea Draelos, David Dov, Maciej A. Mazurowski, Joseph Y. Lo, Ricardo Henao, Geoffrey D. Rubin, Lawrence Carin
Summary: This study utilized a large-scale chest CT dataset with high-quality abnormality labels, developed a method for extracting labels from radiology reports, and used a deep learning CNN model for multi-organ, multi-disease classification of CT volumes, demonstrating good classification performance.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hairui Wang, Yuchan Liu, Nan Xu, Yuanyuan Sun, Shihan Fu, Yunuo Wu, Chunhe Liu, Lei Cui, Zhaoyu Liu, Zhihui Chang, Shu Li, Kexue Deng, Jiangdian Song
Summary: Deep learning-based EfficientNetV2 model can evaluate the time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naive patients with intermediate-stage hepatocellular carcinoma (HCC). Patients with lower scores on the model have better prognosis in TACE treatment.
EUROPEAN JOURNAL OF RADIOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Amish Kumar, Palash Ghosal, Soumya Snigdha Kundu, Amritendu Mukherjee, Debashis Nandi
Summary: This paper introduces a patch-based, residual, asymmetric, encoder-decoder CNN model for segmenting acute ischemic stroke lesions from CT and CT perfusion data. The model addresses the issues of class imbalance and vanishing gradients, achieving high performance.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Information Systems
Mamoona Humayun, Muhammad Ibrahim Khalil, Saleh Naif Almuayqil, N. Z. Jhanjhi
Summary: Breast cancer is a leading cause of mortality, and recent advancements in gene expression research and deep learning techniques have improved the accuracy of risk prediction, enabling tailored screening and prevention decisions.
Article
Water Resources
Ruotong Huang, Anna L. Herring, Adrian Sheppard
Summary: Understanding the mass transfer of CO2 into formation brine is crucial for the safety of geologic carbon sequestration. This study used quasi-dynamic X-ray micro-computed tomographic imaging to track the evolution of scCO2 clusters in sandstone during brine injection. The mass transfer coefficient of individual scCO2 clusters was found to range between 3.0x10-5 and 3.5x10-4 mm/s, with a macroscopic average of 1.4x10-4 mm/s. These values provide insight into the range of mass transfer coefficients expected for similar conditions. The study also highlighted the coupling of dissolution and mobilization processes, emphasizing the need to understand these dynamics for effective CO2 storage.
ADVANCES IN WATER RESOURCES
(2023)
Article
Chemistry, Multidisciplinary
Abdul Rahaman Wahab Sait
Summary: Lung cancer is the primary cause of cancer-related deaths worldwide. Deep learning-based medical image analysis plays a crucial role in detecting and diagnosing lung cancer. The author proposes a deep learning model using PET/CT images for lung cancer detection. By applying image preprocessing and augmentation techniques, as well as convolutional neural networks and deep autoencoders, along with optimization algorithms, the model achieves high accuracy and reduces the need for computational resources. The experimental results show that the proposed model has high accuracy and stability with fewer parameters.
APPLIED SCIENCES-BASEL
(2023)
Article
Environmental Sciences
Zitong Huang, Takeshi Kurotori, Ronny Pini, Sally M. Benson, Christopher Zahasky
Summary: This article presents a method for quantifying the heterogeneous multiscale permeability in geologic porous media using positron emission tomography (PET) imaging. By utilizing a trained convolutional neural network (CNN) based on experimental data, accurate and computationally efficient permeability inversion maps can be generated. The results demonstrate the effectiveness of this approach by comparing the network-predicted permeability maps with experimental data.
WATER RESOURCES RESEARCH
(2022)
Article
Biology
Shota Watanabe, Kenta Sakaguchi, Daisuke Murata, Kazunari Ishii
Summary: This study compared the accuracy of measuring HU values in the internal carotid artery using a DL-based method and the conventional ROI setting method. The results showed that the DL-based method can improve the accuracy of HU value measurements for ICA in BT images, especially in cases of patient involuntary movement.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Energy & Fuels
Fatimah Alzubaidi, Patrick Makuluni, Stuart R. Clark, Jan Erik Lie, Peyman Mostaghimi, Ryan T. Armstrong
Summary: This paper introduces a machine learning-based approach for automatic fracture recognition from unwrapped drill-core images. The method applies a state-of-the-art convolutional neural network for object identification and segmentation, and investigates the feasibility of using synthetic fracture images for training the learning model.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Chemical
Syeda Rubaiya Muin, Patrick T. Spicer, Kunning Tang, Yufu Niu, Maryam Hosseini, Peyman Mostaghimi, Ryan T. Armstrong
Summary: The article introduces a method of using micro-CT technology to conduct three-dimensional microstructural analysis of foam, and improves the image collection and analysis capabilities through deep learning. The study evaluates the stability mechanism of microfibrillar cellulose in foam, explains the effect of fiber entrapment on foam structure stability, and provides detailed analysis of the data and evaluation of deep learning methods.
CHEMICAL ENGINEERING SCIENCE
(2022)
Article
Chemistry, Physical
Chenhao Sun, James McClure, Steffen Berg, Peyman Mostaghimi, Ryan T. Armstrong
Summary: This article proposes a universal description of wetting on multiscale surfaces through the combination of integral geometry and thermodynamic laws. The theoretical framework is presented and applied to different limiting cases. Simulations of fluid droplets on structurally rough and chemically heterogeneous surfaces are conducted to explore the wetting behavior. The findings reveal the origin of classical wetting models within the proposed framework.
JOURNAL OF COLLOID AND INTERFACE SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Kunning Tang, Quentin Meyer, Robin White, Ryan T. Armstrong, Peyman Mostaghimi, Ying Da Wang, Shiyang Liu, Chuan Zhao, Klaus Regenauer-Lieb, Patrick Kin Man Tung
Summary: This study demonstrates the benefits of using convolutional neural networks (CNNs) in accurately classifying different materials of proton exchange membrane fuel cells using X-ray micro-computed tomography. The study shows that a novel UResNet CNN can effectively segment the complete volume of the fuel cells with high accuracy. The CNN outperforms the manual segmentation, especially in separating carbon fibres and binder phase in the gas diffusion layer. Additionally, the CNN provides realistic permeability calculation results for the binder void space.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Engineering, Chemical
Kunning Tang, Ying Da Wang, Peyman Mostaghimi, Mark Knackstedt, Chad Hargrave, Ryan T. Armstrong
Summary: Mineral Liberation Analysis (MLA) is an automated system for identifying minerals in mineral samples. This study extends MLA to three dimensions using 3D X-ray microcomputed tomography and convolutional neural networks. Comparison between 2D and 3D analysis shows significant differences in the results.
MINERALS ENGINEERING
(2022)
Article
Engineering, Chemical
Naif J. Alqahtani, Yufu Niu, Ying Da Wang, Traiwit Chung, Zakhar Lanetc, Aleksandr Zhuravljov, Ryan T. Armstrong, Peyman Mostaghimi
Summary: Reliable quantitative analysis of digital rock images, especially in heterogeneous rocks like carbonates with complex pore size distributions, requires precise segmentation and identification. In recent years, deep learning algorithms have provided efficient and automated solutions for digital rock super-resolution and segmentation. This study presents a framework that uses convolutional neural networks (CNNs) to achieve super-resolved segmentations of carbonate rock images in order to identify sub-resolution porosity. Comparison of voxel-wise segmentation accuracy metrics, topological features, and effective properties confirms the accuracy of the trained model and highlights the value of integrating deep learning frameworks in digital rock analysis.
TRANSPORT IN POROUS MEDIA
(2022)
Article
Physics, Applied
Kunning Tang, Ying Da Wang, James McClure, Cheng Chen, Peyman Mostaghimi, Ryan T. Armstrong
Summary: Mitigating greenhouse gas emissions and finding solutions for future energy is essential. Understanding geochemical reactions and flow behavior at the interface is important for successful underground storage. This study introduces an image-processing workflow using synchrotron-based μCT and CNNs to extract quantitative data and analyze porous materials for energy applications. The results show the need for comprehensive assessment beyond pixel-wise accuracy.
PHYSICAL REVIEW APPLIED
(2022)
Article
Energy & Fuels
Zakhar Lanetc, Aleksandr Zhuravljov, Yu Jing, Ryan T. Armstrong, Peyman Mostaghimi
Summary: Modelling multiphase flow in fractured media is a challenging problem of great importance for oil production. The use of dynamic pore network models allows for capturing the transient nature of multiphase flow, overcoming the limitations of numerical oscillations and interface tracking. The proposed hybrid numerical method offers a promising approach for investigating transient immiscible multiphase flow phenomena in fractures.
Article
Green & Sustainable Science & Technology
Elizabeth J. H. Kimbrel, Dorthe Wildenschild, Anna L. Herring, Ryan T. Armstrong
Summary: This article investigates the efficiency of CO2 trapping in geological storage. Through experiments on the imbibition and drainage processes of proxy fluids, it is found that the amount of trapped CO2 is dependent on the presence of the nonwetting phase and the initial injection method of supercritical CO2, factors that are not considered in current trapping models.
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
(2022)
Article
Mechanics
James E. McClure, Ming Fan, Steffen Berg, Ryan T. T. Armstrong, Carl Fredrik Berg, Zhe Li, Thomas Ramstad
Summary: Relative permeability is derived from conservation of energy and used to model fluid flow through porous materials. The study finds dynamic connectivity and explores the distribution of energy fluctuations during steady-state flow. It demonstrates the effectiveness of the conventional relative permeability relationship in simulating energy dissipation in systems with complex pore-scale dynamics.
Article
Engineering, Chemical
Yufu Niu, Samuel J. Jackson, Naif Alqahtani, Peyman Mostaghimi, Ryan T. Armstrong
Summary: This study compares two state-of-the-art super-resolution deep learning techniques, demonstrating that the unpaired GAN approach can accurately reconstruct super-resolution images as precise as the paired CNN method, with comparable training times and dataset requirements. This opens up new possibilities for micro-CT image enhancement using unpaired deep learning methods, eliminating the need for image registration.
TRANSPORT IN POROUS MEDIA
(2022)
Article
Environmental Sciences
Mohammad Ebadi, Ryan T. Armstrong, Peyman Mostaghimi, Ying Da Wang, Naif Alqahtani, Tammy Amirian, Lesley Anne James, Arvind Parmar, David Zahra, Hasar Hamze, Dmitry Koroteev
Summary: This paper proposes a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores and build predictive continuum-scale models. The workflow classifies rock types using image features and morphological properties, and evaluates petrophysical properties through pore-scale simulations. Experimental results demonstrate the high-fidelity characterization provided by this workflow.
WATER RESOURCES RESEARCH
(2022)
Article
Chemistry, Physical
Quentin Meyer, Shiyang Liu, Karin Ching, Ying Da Wang, Chuan Zhao
Summary: This study introduces a new approach to monitoring the evolutions of the triple-phase boundary in proton exchange membrane fuel cells. By simultaneously capturing the ohmic resistance and double-layer capacitance using high-frequency zero-phase impedance spectroscopy, the impact of fuel cell operations on the triple-phase boundary can be accurately analyzed. The results show that the triple-phase boundary area reduces throughout the operations, indicating changes in proton transport, electron transport, and oxygen diffusion.
JOURNAL OF POWER SOURCES
(2023)
Article
Engineering, Chemical
Kunning Tang, Ying Da Wang, Yufu Niu, Tom A. Honeyands, Damien O'Dea, Peyman Mostaghimi, Ryan T. Armstrong, Mark Knackstedt
Summary: The iron ore sintering process needs to be optimized to reduce energy consumption and carbon emissions, while producing high-quality sinter for low carbon blast furnace operations. 3D X-ray micro-computed tomography can provide particle-level information of iron ore mixtures, but algorithms are needed to identify and classify particles and establish the relationship between ore sources and sinter quality.
Article
Multidisciplinary Sciences
Ying Da Wang, Quentin Meyer, Kunning Tang, James E. McClure, Robin T. White, Stephen T. Kelly, Matthew M. Crawford, Francesco Iacoviello, Dan J. L. Brett, Paul R. Shearing, Peyman Mostaghimi, Chuan Zhao, Ryan T. Armstrong
Summary: The authors utilize X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multiphase simulation to simulate fuel cells and guide their design, addressing the challenge of accurate liquid water modelling.
NATURE COMMUNICATIONS
(2023)