AIMC Topic: Phantoms, Imaging
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DLPVI: Deep learning framework integrating projection, view-by-view backprojection, and image domains for high- and ultra-sparse-view CBCT reconstruction.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 1, 2025
This study proposes a deep learning framework, DLPVI, which integrates projection, view-by-view backprojection (VVBP), and image domains to improve the quality of high-sparse-view and ultra-sparse-view cone-beam computed tomography (CBCT) images. The...
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.
NeuroImage
Feb 1, 2025
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...
Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.
Magma (New York, N.Y.)
Feb 1, 2025
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...
Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.
Medical physics
Jan 30, 2025
BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.
Continuous single-ended depth-of-interaction measurement using highly multiplexed signals and artificial neural networks.
Physics in medicine and biology
Jan 27, 2025
. This study aims to enhance positron emission tomography (PET) imaging systems by developing a continuous depth-of-interaction (DOI) measurement technique using a single-ended readout. Our primary focus is on reducing the number of readout channels ...
Deep proximal gradient network for absorption coefficient recovery in photoacoustic tomography.
Physics in medicine and biology
Jan 22, 2025
The optical absorption properties of biological tissues in photoacoustic (PA) tomography are typically quantified by inverting acoustic measurements. Conventional approaches to solving the inverse problem of forward optical models often involve itera...
Robot-Assisted CT-Guided Biopsy with an Artificial Intelligence-Based Needle-Path Generator: An Experimental Evaluation Using a Phantom Model.
Journal of vascular and interventional radiology : JVIR
Jan 21, 2025
PURPOSE: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for computed tomography (CT)-guided biopsy compared with the conventional freehand technique.
Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.
Scientific reports
Jan 20, 2025
Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with...
Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging.
Sensors (Basel, Switzerland)
Jan 15, 2025
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering...
Deep Network Regularization for Phase-Based Magnetic Resonance Electrical Properties Tomography With Stein's Unbiased Risk Estimator.
IEEE transactions on bio-medical engineering
Jan 15, 2025
Magnetic resonance imaging (MRI) can estimate tissue conductivity values using phase-based magnetic resonance electrical properties tomography (MR-EPT). However, this method is prone to noise amplification due to the Laplacian operator's sensitivity....
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