AIMC Topic: Phantoms, Imaging

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Deep Learning-Based Image Noise Quantification Framework for Computed Tomography.

Journal of computer assisted tomography Jun 30, 2023
OBJECTIVE: Noise quantification is fundamental to computed tomography (CT) image quality assessment and protocol optimization. This study proposes a deep learning-based framework, Single-scan Image Local Variance EstimatoR (SILVER), for estimating th...
Image Processing, Computer-Assisted Thorax Tomography, X-Ray Computed Deep Learning Humans Neural Networks, Computer Phantoms, Imaging
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Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Investigative radiology Jun 28, 2023
Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial...
Artificial Intelligence Phantoms, Imaging Software Tomography Scanners, X-Ray Computed Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Radiation Dosage
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Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks.

European radiology Jun 27, 2023
OBJECTIVE: We propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.
Humans Neural Networks, Computer Radiation Dosage Monte Carlo Method Tomography, X-Ray Computed Phantoms, Imaging Whole Body Imaging
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A predictive signal model for dynamic cardiac magnetic resonance imaging.

Scientific reports Jun 25, 2023
Robust dynamic cardiac magnetic resonance imaging (MRI) has been a long-standing endeavor-as real-time imaging can provide information on the temporal signatures of disease we currently cannot assess-with the past decade seeing remarkable advances in...
Algorithms Image Processing, Computer-Assisted Heart Phantoms, Imaging Magnetic Resonance Imaging Humans Artificial Intelligence
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Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

Magnetic resonance in medicine Jun 18, 2023
PURPOSE: To develop a deep learning-based method, dubbed Denoising CEST Network (DECENT), to fully exploit the spatiotemporal correlation prior to CEST image denoising.
Animals Image Processing, Computer-Assisted Signal-To-Noise Ratio Phantoms, Imaging Mice Algorithms Computer Simulation Humans Neural Networks, Computer
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Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

Magnetic resonance in medicine Jun 15, 2023
PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects.
Brain Computer Simulation Humans Neural Networks, Computer Phantoms, Imaging Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Reduction of SPECT acquisition time using deep learning: A phantom study.

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) Jun 9, 2023
Single photon emission computed tomography (SPECT) procedures are characterized by long acquisition time to acquire diagnostically acceptable image data. The goal of this investigation was to assess the feasibility of using a deep convolutional neura...
Humans Neural Networks, Computer Tomography, Emission-Computed, Single-Photon Image Processing, Computer-Assisted Deep Learning Phantoms, Imaging Time Factors
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Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study.

European journal of radiology Jun 4, 2023
PURPOSE: To compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning-based reconstruction (DLR) at a similar noise level in clinical study a...
Humans Phantoms, Imaging Tomography, X-Ray Computed Physical Examination Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Algorithms
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Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.

Physical and engineering sciences in medicine Jun 2, 2023
We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-based tube current modulation (OB-TCM) acquisition. An anthropomorphic head phantom and a cylindrica...
Deep Learning Radiation Dosage Phantoms, Imaging Algorithms Tomography, X-Ray Computed
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Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI.

NMR in biomedicine May 28, 2023
At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no...
Phantoms, Imaging Electromagnetic Fields Magnetic Resonance Imaging Signal-To-Noise Ratio Radio Waves Humans Deep Learning
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