AI Medical Compendium Topic

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Image Enhancement

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Clinical efficacy of motion-insensitive imaging technique with deep learning reconstruction to improve image quality in cervical spine MR imaging.

The British journal of radiology
OBJECTIVE: To demonstrate that a T2 periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) technique using deep learning reconstruction (DLR) will provide better image quality and decrease image noise.

Panoramic imaging errors in machine learning model development: a systematic review.

Dento maxillo facial radiology
OBJECTIVES: To investigate the management of imaging errors from panoramic radiography (PAN) datasets used in the development of machine learning (ML) models.

Spatially variant deblur and image enhancement in a single multimode fiber imaged by deep learning.

Optics letters
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
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
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
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
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
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
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
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...