AIMC Topic: Image Enhancement
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Spatially variant deblur and image enhancement in a single multimode fiber imaged by deep learning.
Optics letters
Oct 1, 2022
A single multimode fiber has been applied in minimally invasive endoscopy with wavefront shaping for biological research such as brain imaging. Most of the fibers, such as step-index and graded-index multimode fibers, give rise to spatially variant b...
Performance comparison of image enhancers with and without deep learning.
Journal of the Optical Society of America. A, Optics, image science, and vision
Apr 1, 2022
Image enhancement is a computational procedure to improve visibility of details and content of an input image. Several image enhancement algorithms have been developed thus far, from traditional methods that process a single image based on physical m...
Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image Quality.
Investigative radiology
Mar 1, 2022
OBJECTIVES: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradie...
Diagnosis of Choroidal Disease With Deep Learning-Based Image Enhancement and Volumetric Quantification of Optical Coherence Tomography.
Translational vision science & technology
Jan 3, 2022
PURPOSE: The purpose of this study was to quantify choroidal vessels (CVs) in pathological eyes in three dimensions (3D) using optical coherence tomography (OCT) and a deep-learning analysis.
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.
PET clinics
Oct 1, 2021
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the post...
Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity.
Investigative radiology
Aug 1, 2021
OBJECTIVES: The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE i...
Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.
Optics letters
May 15, 2021
Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are...
3D computational cannula fluorescence microscopy enabled by artificial neural networks.
Optics express
Oct 26, 2020
Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and...
Evaluation of Rectal Cancer Circumferential Resection Margin Using Faster Region-Based Convolutional Neural Network in High-Resolution Magnetic Resonance Images.
Diseases of the colon and rectum
Feb 1, 2020
BACKGROUND: High-resolution MRI is regarded as the best method to evaluate whether there is an involved circumferential resection margin in rectal cancer.
Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.
Korean journal of radiology
Jan 1, 2020
OBJECTIVE: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images.
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