AIMC Topic: Phantoms, Imaging

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Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.

Journal of biomedical optics Jan 11, 2024
SIGNIFICANCE: Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorpti...
Monte Carlo Method Phantoms, Imaging Computer Simulation Spectrum Analysis Neural Networks, Computer
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Deep Generalized Learning Model for PET Image Reconstruction.

IEEE transactions on medical imaging Jan 2, 2024
Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. How...
Image Processing, Computer-Assisted Positron-Emission Tomography Phantoms, Imaging Neural Networks, Computer Algorithms
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Super-resolution dual-layer CBCT imaging with model-guided deep learning.

Physics in medicine and biology Dec 26, 2023
This study aims at investigating a novel super resolution CBCT imaging approach with a dual-layer flat panel detector (DL-FPD).With DL-FPD, the low-energy and high-energy projections acquired from the top and bottom detector layers contain over-sampl...
Phantoms, Imaging Spiral Cone-Beam Computed Tomography Deep Learning Cone-Beam Computed Tomography Tomography, X-Ray Computed
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Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

Acta radiologica (Stockholm, Sweden : 1987) Dec 25, 2023
This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of phot...
Photons Humans Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted Deep Learning Phantoms, Imaging
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Pediatric evaluations for deep learning CT denoising.

Medical physics Dec 21, 2023
BACKGROUND: Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL deno...
Phantoms, Imaging Radiation Dosage Signal-To-Noise Ratio Infant, Newborn Adult Algorithms Child Image Processing, Computer-Assisted Tomography, X-Ray Computed Deep Learning Humans Noise Adolescent
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Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Dec 14, 2023
Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has th...
Diagnostic Imaging Spectrum Analysis Deep Learning Photoacoustic Techniques Ultrasonography Phantoms, Imaging Humans
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Deep learning for fast super-resolution ultrasound microvessel imaging.

Physics in medicine and biology Dec 12, 2023
. Ultrasound localization microscopy (ULM) enables microvascular reconstruction by localizing microbubbles (MBs). Although ULM can obtain microvascular images that are beyond the ultimate resolution of the ultrasound (US) diffraction limit, it requir...
Microscopy Microbubbles Phantoms, Imaging Deep Learning Ultrasonography Microvessels
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Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding.

Magnetic resonance in medicine Dec 10, 2023
PURPOSE: To design an unsupervised deep learning (DL) model for correcting Nyquist ghosts of single-shot spatiotemporal encoding (SPEN) and evaluate the model for real MRI applications.
Brain Deep Learning Image Processing, Computer-Assisted Artifacts Algorithms Phantoms, Imaging Echo-Planar Imaging
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Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

Medical physics Dec 8, 2023
BACKGROUND: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied ...
Deep Learning Image Processing, Computer-Assisted Phantoms, Imaging Algorithms Tomography, X-Ray Computed Neural Networks, Computer
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Model-based deep learning framework for accelerated optical projection tomography.

Scientific reports Dec 8, 2023
In this work, we propose a model-based deep learning reconstruction algorithm for optical projection tomography (ToMoDL), to greatly reduce acquisition and reconstruction times. The proposed method iterates over a data consistency step and an image d...
Tomography Animals Deep Learning Zebrafish Phantoms, Imaging Image Processing, Computer-Assisted Neural Networks, Computer Algorithms
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