AIMC Topic: Phantoms, Imaging

Clear Filters Showing 181 to 190 of 825 articles

Improving quantitative MRI using self-supervised deep learning with model reinforcement: Demonstration for rapid T1 mapping.

Magnetic resonance in medicine
PURPOSE: This paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed me...

Deep learning-based PET image denoising and reconstruction: a review.

Radiological physics and technology
This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection thr...

Artifact suppression for breast specimen imaging in micro CBCT using deep learning.

BMC medical imaging
BACKGROUND: Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation...

Organ dose prediction for patients undergoing radiotherapy CBCT chest examinations using artificial intelligence.

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: To propose an artificial intelligence (AI)-based method for personalized and real-time dosimetry for chest CBCT acquisitions.

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound.

Ultrasonics
Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator v...

Reducing windmill artifacts in clinical spiral CT using a deep learning-based projection raw data upsampling: Method and robustness evaluation.

Medical physics
BACKGROUND: Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmil...

Dynamic Virtual Fixture Generation Based on Intra-Operative 3D Image Feedback in Robot-Assisted Minimally Invasive Thoracic Surgery.

Sensors (Basel, Switzerland)
This paper proposes a method for generating dynamic virtual fixtures with real-time 3D image feedback to facilitate human-robot collaboration in medical robotics. Seamless shared control in a dynamic environment, like that of a surgical field, remain...

A denoising method based on deep learning for proton radiograph using energy resolved dose function.

Physics in medicine and biology
Proton radiograph has been broadly applied in proton radiotherapy which is affected by scattered protons which result in the lower spatial resolution of proton radiographs than that of x-ray images. Traditional image denoising method may lead to the ...

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

Journal of biomedical optics
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...

Deep Generalized Learning Model for PET Image Reconstruction.

IEEE transactions on medical imaging
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...