AI Medical Compendium Topic:
Phantoms, Imaging

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Deep Generative Adversarial Neural Networks for Compressive Sensing MRI.

IEEE transactions on medical imaging
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS)...

Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network.

Computers in biology and medicine
Single-shot gradient-echo echo-planar imaging (GE-EPI) plays a significant role in applications where high temporal resolution is necessary. However, GE-EPI is susceptible to inhomogeneous magnetic fields that will cause image distortion. Most existi...

Deep Endoscopic Visual Measurements.

IEEE journal of biomedical and health informatics
Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is ess...

Synthesizing retinal and neuronal images with generative adversarial nets.

Medical image analysis
This paper aims at synthesizing multiple realistic-looking retinal (or neuronal) images from an unseen tubular structured annotation that contains the binary vessel (or neuronal) morphology. The generated phantoms are expected to preserve the same tu...

Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.

Medical image analysis
Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colon...

Automatic hand phantom map generation and detection using decomposition support vector machines.

Biomedical engineering online
BACKGROUND: There is a need for providing sensory feedback for myoelectric prosthesis users. Providing tactile feedback can improve object manipulation abilities, enhance the perceptual embodiment of myoelectric prostheses and help reduce phantom lim...

Machine learning-based dual-energy CT parametric mapping.

Physics in medicine and biology
The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z), relative electron density (ρ ), mean excitation energy (I ), and relative stopping power (RSP) from clinical dual...

Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: During needle interventions, successful automated detection of the needle immediately after insertion is necessary to allow the physician identify and correct any misalignment of the needle and the target at early stages, which reduces needl...

Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

Medical & biological engineering & computing
Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reacti...

Automatic Robotic Steering of Flexible Needles from 3D Ultrasound Images in Phantoms and Ex Vivo Biological Tissue.

Annals of biomedical engineering
Robotic control of needle bending aims at increasing the precision of percutaneous procedures. Ultrasound feedback is preferable for its clinical ease of use, cost and compactness but raises needle detection issues. In this paper, we propose a comple...