AIMC Topic: Phantoms, Imaging

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Scatter correction of cone-beam CT using a deep residual convolution neural network (DRCNN).

Physics in medicine and biology
Scatter correction is an essential technique to improve the image quality of cone-beam CT (CBCT). Although different scatter correction methods have been proposed in the literature, a standard solution is still being studied due to the limitations in...

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

IEEE transactions on medical imaging
Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the precision of parametric image analysis. In this paper, we investigate the use of machine learning and a...

Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

Medical physics
PURPOSE: The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing meth...

Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks.

European radiology
OBJECTIVES: The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated constru...

A Novel Relative Position Estimation Method for Capsule Robot Moving in Gastrointestinal Tract.

Sensors (Basel, Switzerland)
Recently, a variety of positioning and tracking methods have been proposed for capsule robots moving in the gastrointestinal (GI) tract to provide real-time unobstructed spatial pose results. However, the current absolute position-based result cannot...

A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation.

IEEE transactions on medical imaging
In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available, when only the initial pressure is reconstructed. To obtain the desired information, algorithms for image segmentation are of...

ADMM-based deep reconstruction for limited-angle CT.

Physics in medicine and biology
In the real applications of computed tomography (CT) imaging, the projection data of the scanned objects are usually acquired within a limited-angle range because of the limitation of the scanning condition. Under these circumstances, conventional an...

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...