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Evaluation of an artificial intelligence noise reduction tool for conventional X-ray imaging - a visual grading study of pediatric chest examinations at different radiation dose levels using anthropomorphic phantoms. [PDF]

Pediatr Radiol
Hultenmo M, Pernbro J, Ahlin J, Bonnier M, Båth M.   +4 more
europepmc   +1 more source

Optimization of ^99mTc-SPECT in the presence of ⁹⁰Y for radioembolization. [PDF]

EJNMMI Phys
Kerckhaert CEM, Dietze MMA, van Rooij R, Meddens MBM, Wijnen N, Smits MLJ, Lam MGEH, de Jong HWAM.   +7 more
europepmc   +1 more source

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An anthropomorphic torso section phantom for ultrasonic imaging

Medical Physics, 1980
Using stable materials capable of mimicking any soft tissues of the human body—quantitatively with respect to speed of sound, density, and attenuation coefficient and semiquantitatively with respect to scatter—we have constructed a torso section phantom containing simulated bowel gas, kidney, kidney fat pad, liver, tumor, cyst, bony structures, and ...
E L, Madsen, J A, Zagzebski, T, Ghilardi-Netto   +2 more
openaire   +2 more sources

4D digital anthropomorphic breast phantom for iodinated contrast-enhanced imaging

Journal of Medical Imaging, 2023
Differentiating between benign and malignant masses is one of the biggest challenges in breast imaging. The challenge is ingrained in the similarity of the attenuation coefficients between different types of lesion tissues and fibroglandular tissues.
Dan, Li, Andrey, Makeev, Stephen J, Glick   +2 more
openaire   +2 more sources

Understanding CNN based anthropomorphic model observer using classification images

Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 2021
It is widely accepted that medical imaging reconstruction strategies should be optimized by maximizing the chance of a radiologist making a correct diagnostic. This implies organizing costly sessions with doctors to evaluate the images and provide feedback.
Iris Lorente, Craig Abbey, Jovan G. Brankov   +2 more
openaire   +1 more source

Multimodality image correlation quality assurance anthropomorphic phantom

Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143), 2002
Multimodality images of many radiation therapy patients could be correlated/fused using software tools provided in treatment planning software. We have developed a phantom and a registration method for evaluation and verification of the fusion process and fusion software.
S. Mutic, W.R. Bosch, R.E. Drzymala, K.S.C. Chao, D.A. Low, J.S. Lewis, P.D. Cutler   +6 more
openaire   +1 more source

An Anthropomorphic Breast Model for Breast Imaging Simulation and Optimization

Academic Radiology, 2011
Optimization studies for x-ray-based breast imaging systems using computer simulation can greatly benefit from a phantom capable of modeling varying anatomical variability across different patients. This study aimed to develop a three-dimensional phantom model with realistic and randomizable anatomical features.A voxelized breast model was developed ...
Baiyu, Chen, Jamie, Shorey, Robert S, Saunders, Samuel, Richard, John, Thompson, Loren W, Nolte, Ehsan, Samei   +6 more
openaire   +2 more sources

Anthropomorphic breast phantoms for preclinical imaging evaluation with transmission or emission imaging

SPIE Proceedings, 2005
With the development of several classes of dedicated emission and transmission imaging technologies utilizing ionizing radiation for improved breast cancer detection and in vivo characterization, it is extremely useful to have available anthropomorphic breast phantoms in a variety of shapes, sizes and malleability prior to clinical imaging.
Martin P. Tornai, Randolph L. McKinley, Caryl N. Bryzmialkiewicz, Spencer J. Cutler, Dominic J. Crotty   +4 more
openaire   +1 more source

Anthropomorphic ResNet18 for multi-vendor DBT image quality evaluation

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 2020
Purpose: This work aims to develop an anthropomorphic convolutional neural network (CNN) classifier, based on the ResNet18 deep learning network and validate it for task based image quality evaluation of digital breast tomosynthesis (DBT) using a structured phantom with non-spiculated mass simulating lesions. Methods: The phantom is constructed from an
Dimitar Petrov, Nicholas Marshall, Liesbeth Vancoillie, Lesley Cockmartin, Hilde Bosmans   +4 more
openaire   +1 more source