AIMC Topic: Phantoms, Imaging

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Deep Guess acceleration for explainable image reconstruction in sparse-view CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Mar 21, 2025
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Reconstructions based on the traditional Filtered Back Projection algorithm suffer from severe artifacts due to sparse data. In c...
Neural Networks, Computer Algorithms Tomography, X-Ray Computed Humans Deep Learning Image Processing, Computer-Assisted Artifacts Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Phantoms, Imaging
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Initial characterization of a novel dual-robot orthovoltage radiotherapy system.

Biomedical physics & engineering express Mar 17, 2025
Adequate access to radiotherapy is a critical global concern affecting low-resource settings such as low- and middle-income countries and rural regions. We propose to reduce this disparity by developing a novel low-cost radiotherapy device that treat...
Phantoms, Imaging Radiometry Robotics Equipment Design Radiotherapy Planning, Computer-Assisted Humans Radiotherapy Dosage Computer Simulation Monte Carlo Method Radiotherapy
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Deep Learning for High Speed Optical Coherence Elastography With a Fiber Scanning Endoscope.

IEEE transactions on medical imaging Mar 17, 2025
Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions,...
Deep Learning Image Processing, Computer-Assisted Equipment Design Phantoms, Imaging Endoscopes Elasticity Imaging Techniques Humans Animals Swine
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DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.

IEEE transactions on medical imaging Mar 17, 2025
4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projection data will cause severe streak artifacts in 4D CBCT images. Existing deep learning (DL) methods ...
Deep Learning Four-Dimensional Computed Tomography Cone-Beam Computed Tomography Image Processing, Computer-Assisted Lung Neoplasms Humans Artifacts Algorithms Phantoms, Imaging Lung
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Transcranial adaptive aberration correction using deep learning for phased-array ultrasound therapy.

Ultrasonics Mar 14, 2025
This study aims to explore the feasibility of a deep learning approach to correct the distortion caused by the skull, thereby developing a transcranial adaptive focusing method for safe ultrasonic treatment in opening of the blood-brain barrier (BBB)...
Feasibility Studies Ultrasonic Therapy Skull Humans Phantoms, Imaging Blood-Brain Barrier Computer Simulation Deep Learning
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Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.

Cancer imaging : the official publication of the International Cancer Imaging Society Mar 12, 2025
PURPOSE: This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and machine learning classifiers in a clinical study to predict the lymphovascular invasion (LVI) of non-...
Phantoms, Imaging Lung Neoplasms Carcinoma, Non-Small-Cell Lung Neoplasm Invasiveness Radiomics Male Algorithms Tomography, X-Ray Computed Humans Female Machine Learning
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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...
Humans Lung Fuzzy Logic Models, Statistical Monte Carlo Method Uncertainty Phantoms, Imaging Radiometry
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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...
Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Neural Networks, Computer Radiation Dosage 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...
Neural Networks, Computer Algorithms Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted 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 Phantoms, Imaging Image Processing, Computer-Assisted Algorithms Magnetic Resonance Imaging
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