AI Medical Compendium Topic:
Phantoms, Imaging

Clear Filters Showing 541 to 550 of 749 articles

Higher SNR PET image prediction using a deep learning model and MRI image.

Physics in medicine and biology
PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training. Our proposed DNN model consist...

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

Magnetic resonance in medicine
PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

Domain Progressive 3D Residual Convolution Network to Improve Low-Dose CT Imaging.

IEEE transactions on medical imaging
The wide applications of X-ray computed tomography (CT) bring low-dose CT (LDCT) into a clinical prerequisite, but reducing the radiation exposure in CT often leads to significantly increased noise and artifacts, which might lower the judgment accura...

Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network.

IEEE transactions on bio-medical engineering
As a widely used imaging modality in the medical field, ultrasound has been applied in community medicine, rural medicine, and even telemedicine in recent years. Therefore, the development of portable ultrasound devices has become a popular research ...

Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.

IEEE transactions on medical imaging
Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed...

Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: In surgical navigation, pre-operative organ models are presented to surgeons during the intervention to help them in efficiently finding their target. In the case of soft tissue, these models need to be deformed and adapted to the current si...

Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.

IEEE transactions on medical imaging
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided ...

Image domain dual material decomposition for dual-energy CT using butterfly network.

Medical physics
PURPOSE: Dual-energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to...

Ultrafast (milliseconds), multidimensional RF pulse design with deep learning.

Magnetic resonance in medicine
PURPOSE: Some advanced RF pulses, like multidimensional RF pulses, are often long and require substantial computation time because of a number of constraints and requirements, sometimes hampering clinical use. However, the pulses offer opportunities ...

Analysis of a CT patient dose database with an unsupervised clustering approach.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This study investigated the benefits of implementing a cluster analysis technique to extract relevant information from a computed tomography (CT) dose registry archive.