AIMC Topic: Phantoms, Imaging

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Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning.

Physics in medicine and biology
In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data.In Trip...

Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study.

Physics in medicine and biology
While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potenti...

Precision forceps tracking and localisation using a Kalman filter for continuous curvilinear capsulorhexis.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Continuous curvilinear capsulorhexis (CCC) requires surgeons to manipulate fragile eye tissue at the microscale. The limited perceptual accuracy of surgeons makes it difficult to precisely position the forceps. Robot technology provides a...

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

Medical physics
BACKGROUND: Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolutio...

High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.

Journal of biomedical optics
SIGNIFICANCE: In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning...

EPI phase error correction with deep learning (PEC-DL) at 7 T.

Magnetic resonance in medicine
PURPOSE: The phase mismatch between odd and even echoes in EPI causes Nyquist ghost artifacts. Existing ghost correction methods often suffer from severe residual artifacts and are ineffective with k-space undersampling data. This study proposed a de...

Deep learning for improving the spatial resolution of magnetic particle imaging.

Physics in medicine and biology
Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional ...

Puncture accuracy of an optical tracked robotic aiming device-a phantom study.

European radiology
OBJECTIVES: To evaluate the targeting accuracy of stereotactic punctures based on a hybrid robotic device in combination with optical tracking-a phantom study.

Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging.

Physics in medicine and biology
External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified b...