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Phantoms, Imaging

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X-ray dose profiles using artificial neural networks.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15 MV at different depths and field sizes using Artificial Neural Networks within the error margin req...

Technical note: Phantom-based training framework for convolutional neural network CT noise reduction.

Medical physics
BACKGROUND: Deep artificial neural networks such as convolutional neural networks (CNNs) have been shown to be effective models for reducing noise in CT images while preserving anatomic details. A practical bottleneck for developing CNN-based denoisi...

Clipped DeepControl: Deep neural network two-dimensional pulse design with an amplitude constraint layer.

Artificial intelligence in medicine
Advanced radio-frequency pulse design used in magnetic resonance imaging has recently been demonstrated with deep learning of (convolutional) neural networks and reinforcement learning. For two-dimensionally selective radio-frequency pulses, the (con...

Development and performance evaluation of a deep learning lung nodule detection system.

BMC medical imaging
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths throughout the world. Chest computed tomography (CT) is now widely used in the screening and diagnosis of lung cancer due to its effectiveness. Radiologists must identify each smal...

Intravascular Tracking of Micro-Agents Using Medical Ultrasound: Towards Clinical Applications.

IEEE transactions on bio-medical engineering
OBJECTIVE: This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clinically-relevant scenarios.

Deep learning for hetero-homo conversion in channel-domain for phase aberration correction in ultrasound imaging.

Ultrasonics
Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arr...

Robot-Assisted Needle Insertion for CT-Guided Puncture: Experimental Study with a Phantom and Animals.

Cardiovascular and interventional radiology
PURPOSE: This study aimed to evaluate the accuracy and safety of robotic CT-guided needle insertion in phantom and animal experiments.

Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals.

Magnetic resonance in medicine
PURPOSE: A supervised deep learning (DL) approach for frequency and phase correction (FPC) of MRS data recently showed encouraging results, but obtaining transients with labels for supervised learning is challenging. This work investigates the feasib...

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

European radiology
OBJECTIVES: To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).

3Ddose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.

Physics in medicine and biology
Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-time3D dose verification. Howev...