Medical Image Processing and Numerical Simulation for Digital Hepatic Parenchymal Blood Flow

Lèbre, Marie-Ange; Arrouk, Khaled; Văn, Anh-Khoa Võ; Leborgne, Aurélie; Grand-Brochier, Manuel; Beaurepaire, Pierre; Vacavant, Antoine; Magnin, Benoît; Abergel, Armand; Chabrot, P.

doi:10.1007/978-3-319-68127-6_11

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…MRI, NMR, radio detection and range, helium electron sprinkling, and other applications have matured the Quaternion transform metrics theoretician of the provided approach. Because it has matured [14]. It uses transformational methodologies.…”

Section: Related Workmentioning

confidence: 99%

Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction

Kumar¹,

Venkatesan²,

S.Ravichandran³

2023

IJSMIEN

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Deep learning compresses medical image processing in IoMT. CS-MRI acquires quickly. It has various medicinal uses due to its advantages. This lowers motion artifacts and contrast washout. Reduces patient pressure and scanning costs. CS-MRI avoids the Nyquist-Shannon sampling barrier. Parallel imagingbased fast MRI uses many coils to reconstruct MRI images with less raw data. Parallel imaging enables rapid MRI. This research developed a deep learning-based method for reconstructing CS-MRI images that bridges the gap between typical non-learning algorithms that employ data from a single image and enormous training datasets. Conventional approaches only reconstruct CS-MRI data from one picture. Reconstructing CS-MRI images. CS-GAN is recommended for CS-MRI reconstruction. For success. Refinement learning stabilizes our C-GAN-based generator, which eliminates aliasing artifacts. This improved newly produced data. Product quality increased. Adversarial and information loss recreated the picture. We should protect the image’s texture and edges. Picture and frequency domain data establish consistency. We want frequency and picture domain information to match. It offers visual domain data. Traditional CS-MRI reconstruction and deep learning were used in our broad comparison research. C-GAN enhances reconstruction while conserving perceptual visual information. MRI image reconstruction takes 5 milliseconds, allowing real-time analysis.

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Section: Related Workmentioning

confidence: 99%

Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction

Kumar¹,

Venkatesan²,

S.Ravichandran³

2023

IJSMIEN

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“…Liver segmentation from MRI images is important in the process of tumor detection. In this work, we have applied the segmentation model developed by [7,13]. The method consists of developing an automatic model for both modalities MRI and CT scans.…”

Section: Liver Segmentationmentioning

confidence: 99%

“…This approach has employed the U-Net architecture in the classification process. [7] proposed a parallel Framework for HCC detection in DCE-MRI sequences with wavelet-based description and SVM (Support Vector Machine)classification. This work realized parallel patch-based processing of DCE-MRI images characterizing three contrast-enhancement phases.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Approach for Liver MRI Classification and HCC Detection

Messaoudi

Jaziri

Vacavant

et al. 2020

Pattern Recognition and Artificial Intelligence

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This work proposes a deep learning algorithm based on the Convolutional Neural Network (CNN) architecture to detect HepatoCellular Carcinoma (HCC) from liver DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences. The Deep Learning technique is an artificial intelligence technique (AI) that tries to imitate the human brain work in the training data and creating models used for decision. Actually, it is widely used for various clinical issues. To diagnose HCC, radiologists consider three different phases during contrast injection (before injection; arterial phase; portal phase for instance). This paper presents an approach that offers a parallel preprocessing algorithm. It allows HCC detection and localization in MRI images via a CNN algorithm. The created CNN model reached an accuracy level of 90% in both arterial and portal phases using MRI patches of 64×64 pixels. We mention also its ability to decrease false detection comparing with our previous works. The obtained good accuracy is considered to be ameliorated in our future works.

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“…Two blood inflows characterize the vascular system of the liver including the portal vein and the hepatic artery, as opposed to organs such as the kidneys and brain, which are supplied by one inflow of blood. The hepatic blood outflow through hepatic veins are split into intricate tree-like networks upon advancing within the liver (Lebre et al, 2017 ). The entire liver can be divided into functional units called liver lobules.…”

Section: Introductionmentioning

confidence: 99%

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review

et al. 2021

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MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.

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Medical Image Processing and Numerical Simulation for Digital Hepatic Parenchymal Blood Flow

Cited by 11 publications

References 13 publications

Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction

Cloud-enabled Internet of Things Medical Image Processing Compressed Sensing Reconstruction

A Novel Deep Learning Approach for Liver MRI Classification and HCC Detection

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review

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