AIMC Topic: Phantoms, Imaging
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A Deep Learning-Based Approach to Characterize Skull Physical Properties: A Phantom Study.
Journal of biophotonics
Nov 14, 2024
Transcranial ultrasound imaging is a popular method to study cerebral functionality and diagnose brain injuries. However, the detected ultrasound signal is greatly distorted due to the aberration caused by the skull bone. The aberration mechanism mai...
Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.
Journal of applied clinical medical physics
Nov 14, 2024
OBJECTIVE: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Feasibility Studies
Organs at Risk
Radiotherapy Planning, Computer-Assisted
Pelvic Neoplasms
Humans
Radiotherapy Dosage
Algorithms
Radiation Dosage
Radiotherapy, Image-Guided
Prospective Studies
Deep Learning
Signal-To-Noise Ratio
Radiographic Image Enhancement
Image Processing, Computer-Assisted
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Radiotherapy, Intensity-Modulated
Female
Phantoms, Imaging
Male
Abdominal Neoplasms
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
Nov 13, 2024
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
Nov 11, 2024
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
Nov 5, 2024
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
Nov 5, 2024
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
Nov 4, 2024
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
Oct 28, 2024
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
Oct 24, 2024
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
Oct 16, 2024
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 ...
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