AIMC Topic: Phantoms, Imaging

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Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo study.

Computers in biology and medicine Mar 10, 2025
The increasing use of ionizing radiation has raised concerns about adverse and long-term health risks for individuals. Therefore, to evaluate the range of risks and protection against ionizing radiation, it is necessary to assess the dosimetry calcul...
Radiometry Uncertainty Monte Carlo Method Phantoms, Imaging Humans Lung Fuzzy Logic Models, Statistical
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Direct-to-Treatment Adaptive Radiation Therapy: Live Planning of Spine Metastases Using Novel Cone Beam Computed Tomography.

International journal of radiation oncology, biology, physics Mar 9, 2025
PURPOSE: Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out using a synthetic CT (sCT) created through deformable registration between the patient-specific fan-beam CT, fan-beam computed tomography (FBCT), and...
Humans Phantoms, Imaging Radiotherapy Planning, Computer-Assisted Cone-Beam Computed Tomography Radiotherapy, Image-Guided Spinal Neoplasms Organs at Risk Radiotherapy Dosage
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KBA-PDNet: A primal-dual unrolling network with kernel basis attention for low-dose CT reconstruction.

Journal of X-ray science and technology Mar 3, 2025
Computed tomography (CT) image reconstruction is faced with challenge of balancing image quality and radiation dose. Recent unrolled optimization methods address low-dose CT image quality issues using convolutional neural networks or self-attention m...
Neural Networks, Computer Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted Radiation Dosage Humans Phantoms, Imaging
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Sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization.

Computers in biology and medicine Mar 1, 2025
Cone beam computed tomography (CBCT) is an emerging medical imaging technique to visualize the internal anatomical structures of patients. During a CBCT scan, several projection images of different angles or views are collectively utilized to reconst...
Algorithms Cone-Beam Computed Tomography Image Processing, Computer-Assisted Humans Neural Networks, Computer Phantoms, Imaging
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Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.

Magma (New York, N.Y.) Feb 18, 2025
OBJECTIVE: Using deep learning-based techniques to overcome physical limitations and explore the potential performance of 0.2 T low-field unshielded MRI in terms of imaging quality and speed.
Equipment Design Deep Learning Humans Signal-To-Noise Ratio Image Processing, Computer-Assisted Algorithms Phantoms, Imaging Magnetic Resonance Imaging
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Complex conjugate removal in optical coherence tomography using phase aware generative adversarial network.

Journal of biomedical optics Feb 17, 2025
SIGNIFICANCE: Current methods for complex conjugate removal (CCR) in frequency-domain optical coherence tomography (FD-OCT) often require additional hardware components, which increase system complexity and cost. A software-based solution would provi...
Humans Deep Learning Algorithms Mice Image Processing, Computer-Assisted Animals Tomography, Optical Coherence Skin Artifacts Phantoms, Imaging Eye Generative Adversarial Networks
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Evaluation of deep learning-based scatter correction on a long-axial field-of-view PET scanner.

European journal of nuclear medicine and molecular imaging Feb 7, 2025
OBJECTIVE: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However this extended angle increases the...
Image Processing, Computer-Assisted Positron-Emission Tomography Monte Carlo Method Humans Deep Learning Scattering, Radiation Phantoms, Imaging
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Deep learning aided determination of the optimal number of detectors for photoacoustic tomography.

Biomedical physics & engineering express Feb 7, 2025
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology...
Humans Neural Networks, Computer Photoacoustic Techniques Tomography Image Processing, Computer-Assisted Algorithms Artifacts Deep Learning Phantoms, Imaging
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High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies.

European radiology Feb 4, 2025
OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary compu...
Aged Male Middle Aged Retrospective Studies Female Phantoms, Imaging Reproducibility of Results Deep Learning Computed Tomography Angiography Humans Coronary Angiography Coronary Stenosis Radiographic Image Interpretation, Computer-Assisted
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Quantification of tissue stiffness with magnetic resonance elastography and finite difference time domain (FDTD) simulation-based spatiotemporal neural network.

Magnetic resonance imaging Feb 4, 2025
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...
Signal-To-Noise Ratio Phantoms, Imaging Elastic Modulus Elasticity Imaging Techniques Reproducibility of Results Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Algorithms Computer Simulation Humans Neural Networks, Computer
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