AIMC Topic: Image Enhancement
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Machine learning analysis of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute graft dysfunction in kidney transplant recipients.
Medical ultrasonography
Sep 4, 2024
AIM: The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipients based on contrast-enhanced ultrasound (CEUS) analysis of the graft.Materials and methods: This pro...
Deep learning applications for quantitative and qualitative PET in PET/MR: technical and clinical unmet needs.
Magma (New York, N.Y.)
Aug 21, 2024
We aim to provide an overview of technical and clinical unmet needs in deep learning (DL) applications for quantitative and qualitative PET in PET/MR, with a focus on attenuation correction, image enhancement, motion correction, kinetic modeling, and...
DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.
Magma (New York, N.Y.)
Aug 8, 2024
INTRODUCTION: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption.
SSP-Net: A Siamese-Based Structure-Preserving Generative Adversarial Network for Unpaired Medical Image Enhancement.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Recently, unpaired medical image enhancement is one of the important topics in medical research. Although deep learning-based methods have achieved remarkable success in medical image enhancement, such methods face the challenge of low-quality traini...
Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT.
Medical image analysis
Jul 31, 2024
In positron emission tomography (PET) and X-ray computed tomography (CT), reducing radiation dose can cause significant degradation in image quality. For image quality enhancement in low-dose PET and CT, we propose a novel theoretical adversarial and...
Enabling AI-Generated Content for Gadolinium-Free Contrast-Enhanced Breast Magnetic Resonance Imaging.
Journal of magnetic resonance imaging : JMRI
Jul 25, 2024
BACKGROUND: There is increasing interest in utilizing AI-generated content for gadolinium-free contrast-enhanced breast MRI.
Humans
Artificial Intelligence
Algorithms
Retrospective Studies
Female
Reproducibility of Results
Contrast Media
Magnetic Resonance Imaging
Breast
Image Interpretation, Computer-Assisted
Breast Neoplasms
Gadolinium
Middle Aged
Image Enhancement
Imaging, Three-Dimensional
Diffusion Magnetic Resonance Imaging
Image Processing, Computer-Assisted
Aged
Adult
Sensitivity and Specificity
Multi-degradation-adaptation network for fundus image enhancement with degradation representation learning.
Medical image analysis
Jul 14, 2024
Fundus image quality serves a crucial asset for medical diagnosis and applications. However, such images often suffer degradation during image acquisition where multiple types of degradation can occur in each image. Although recent deep learning base...
AI-based automated evaluation of image quality and protocol tailoring in patients undergoing MRI for suspected prostate cancer.
European journal of radiology
Jun 17, 2024
PURPOSE: To develop and validate an artificial intelligence (AI) application in a clinical setting to decide whether dynamic contrast-enhanced (DCE) sequences are necessary in multiparametric prostate MRI.
Deep Learning Based Cystoscopy Image Enhancement.
Journal of endourology
May 28, 2024
Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex ...
A Retinex-based network for image enhancement in low-light environments.
PloS one
May 24, 2024
Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement netw...
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