AIMC Topic: Phantoms, Imaging

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Super-resolution dual-layer CBCT imaging with model-guided deep learning.

Physics in medicine and biology
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...

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

Acta radiologica (Stockholm, Sweden : 1987)
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...

Pediatric evaluations for deep learning CT denoising.

Medical physics
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...

Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
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...

Deep learning for fast super-resolution ultrasound microvessel imaging.

Physics in medicine and biology
. 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...

Unsupervised deep learning model for correcting Nyquist ghosts of single-shot spatiotemporal encoding.

Magnetic resonance in medicine
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.

Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

Medical physics
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 ...

Model-based deep learning framework for accelerated optical projection tomography.

Scientific reports
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...

Automatic chest computed tomography image noise quantification using deep learning.

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)
PURPOSE: This study aimed to develop a deep learning (DL) method for noise quantification for clinical chest computed tomography (CT) images without the need for repeated scanning or homogeneous tissue regions.