Výsledky vyhledávání - "Nathan R. Huber"

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma

Autor: Francis I. Baffour, Nathan R. Huber, Andrea Ferrero, Kishore Rajendran, Katrina N. Glazebrook, Nicholas B. Larson, Shaji Kumar, Joselle M. Cook, Shuai Leng, Elisabeth R. Shanblatt, Cynthia H. McCollough, Joel G. Fletcher

Publikováno v: Radiology. 306:229-236

Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatia

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aefae81039313dec572d7e3acbeb5c60
https://doi.org/10.1148/radiol.220311

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A minimum SNR criterion for computed tomography object detection in the projection domain

Autor: Scott S, Hsieh, Shuai, Leng, Lifeng, Yu, Nathan R, Huber, Cynthia H, McCollough

Publikováno v: Medical Physics. 49:4988-4998

A common rule of thumb for object detection is the Rose criterion, which states that a signal must be five standard deviations above background to be detectable to a human observer. The validity of the Rose criterion in CT imaging is limited due to t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d59b311fe8915d142b936f04841c417
https://doi.org/10.1002/mp.15832

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

Autor: Nathan R. Huber, Andrew D. Missert, Hao Gong, Shuai Leng, Lifeng Yu, Cynthia H. McCollough

Publikováno v: Medical physicsREFERENCES.

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 denoising models is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6aa458822162b538cae259ef74727ea4
https://pubmed.ncbi.nlm.nih.gov/36385704

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Evaluating a Convolutional Neural Network Noise Reduction Method When Applied to CT Images Reconstructed Differently Than Training Data

Autor: Shuai Leng, Lifeng Yu, Nathan R. Huber, Cynthia H. McCollough, Andrew D. Missert

Publikováno v: J Comput Assist Tomogr

OBJECTIVE: The aim of this study was to evaluate a narrowly trained convolutional neural network (CNN) denoising algorithm when applied to images reconstructed differently than training data set. METHODS: A residual CNN was trained using 10 noise ins

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29511069f44e8e01154a13d38d436eb6
https://doi.org/10.1097/rct.0000000000001150

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Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys

Autor: Cynthia H. McCollough, Lifeng Yu, Andrew D. Missert, Tara L. Anderson, Nathan R. Huber, Shuai Leng, Joel G. Fletcher, Katrina N. Glazebrook, Mark C. Adkins

Publikováno v: Skeletal Radiology. 51:145-151

This study evaluated the clinical utility of a phantom-based convolutional neural network noise reduction framework for whole-body-low-dose CT skeletal surveys. The CT exams of ten patients with multiple myeloma were retrospectively analyzed. Exams w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b19b531d3b55e040039d5dadb44e4130
https://doi.org/10.1007/s00256-021-03828-2

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Dedicated convolutional neural network for noise reduction in ultra-high-resolution photon-counting detector computed tomography

Autor: Nathan R Huber, Andrea Ferrero, Kishore Rajendran, Francis Baffour, Katrina N Glazebrook, Felix E Diehn, Akitoshi Inoue, Joel G Fletcher, Lifeng Yu, Shuai Leng, Cynthia H McCollough

Publikováno v: Physics in Medicine & Biology. 67:175014

Objective. To develop a convolutional neural network (CNN) noise reduction technique for ultra-high-resolution photon-counting detector computed tomography (UHR-PCD-CT) that can be efficiently implemented using only clinically available reconstructed

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e169be8a7525ec821da68e610af4738
https://doi.org/10.1088/1361-6560/ac8866

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