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Phantoms, Imaging

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New Frontiers in Oncological Imaging With Computed Tomography: From Morphology to Function.

Seminars in ultrasound, CT, and MR
The latest evolutions in Computed Tomography (CT) technology have several applications in oncological imaging. The innovations in hardware and software allow for the optimization of the oncological protocol. Low-kV acquisitions are possible thanks to...

Rapid estimation of patient-specific organ doses using a deep learning network.

Medical physics
BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on ti...

A quality-checked and physics-constrained deep learning method to estimate material basis images from single-kV contrast-enhanced chest CT scans.

Medical physics
BACKGROUND: Single-kV CT imaging is one of the primary imaging methods in radiology practices. However, it does not provide material basis images for some subtle lesion characterization tasks in clinical diagnosis.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.

Journal of computer assisted tomography
OBJECTIVE: Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement ...

A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.

Physics in medicine and biology
. Range uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3Drange verification. However, the conventional back-projected PG i...

PET scatter estimation using deep learning U-Net architecture.

Physics in medicine and biology
Positron emission tomography (PET) image reconstruction needs to be corrected for scatter in order to produce quantitatively accurate images. Scatter correction is traditionally achieved by incorporating an estimated scatter sinogram into the forward...

Devising a deep neural network based mammography phantom image filtering algorithm using images obtained under mAs and kVp control.

Scientific reports
We study whether deep neural network based algorithm can filter out mammography phantom images that will pass or fail. With 543 phantom images generated from a mammography unit, we created VGG16 based phantom shape scoring models (multi-and binary-cl...

Evaluation of the performance of robot assisted CT-guided percutaneous needle insertion: Comparison with freehand insertion in a phantom.

European journal of radiology
PURPOSE: To evaluate the performance of a novel robot for CT-guided needle positioning procedures and compare it to the freehand technique in an abdominal phantom.

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen.

Medical physics
BACKGROUND: Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning rec...

Sinogram domain metal artifact correction of CT via deep learning.

Computers in biology and medicine
PURPOSE: Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in im...