AIMC Topic: Phantoms, Imaging

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Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment.

Academic radiology
OBJECTIVE: To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorit...

Imaging error reduction in radial cine-MRI with deep learning-based intra-frame motion compensation.

Physics in medicine and biology
Radial cine-MRI allows for sliding window reconstruction at nearly arbitrary frame rate, promising high-speed imaging for intra-fractional motion monitoring in magnetic resonance guided radiotherapy. However, motion within the reconstruction window m...

Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.

Journal of computer assisted tomography
OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CAD v ) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR...

Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method.

European radiology
OBJECTIVE: To estimate proton density fat fraction (PDFF) from chemical shift encoded (CSE) MR images using a deep learning (DL)-based method that is precise and robust to different MR scanners and acquisition echo times (TEs).

Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction.

IEEE transactions on medical imaging
Photon-counting computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among different channel images. In addition, reconstruction of each channel image suffers phot...

Towards high-performance deep learning architecture and hardware accelerator design for robust analysis in diffuse correlation spectroscopy.

Computer methods and programs in biomedicine
This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to gener...

Deep learning corrects artifacts in RASER MRI profiles.

Magnetic resonance imaging
A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by the stimulated emission of radiation" (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require...

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.

Medical image analysis
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning ...

Deep plug-and-play MRI reconstruction based on multiple complementary priors.

Magnetic resonance imaging
Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method ...

Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis.

Cardiovascular engineering and technology
PURPOSE: Cardiovascular simulators are used in the preclinical testing phase of medical devices. Their reliability increases the more they resemble clinically relevant scenarios. In this study, a physiologically actuated soft robotic left ventricle (...