Výsledky vyhledávání - "Andrew D. Missert"

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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Synthesizing images from multiple kernels using a deep convolutional neural network

Autor: Shuai Leng, Lifeng Yu, Joel G. Fletcher, Cynthia H. McCollough, Andrew D. Missert

Publikováno v: Medical Physics. 47:422-430

Purpose Filtering measured projections with a particular convolutional kernel is an essential step in analytic reconstruction of computed tomography (CT) images. A tradeoff between noise and spatial resolution exists for different choices of reconstr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae7bbdcd8571451d543d230f8796d11c
https://doi.org/10.1002/mp.13918

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CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques

Autor: Payam Mohammadinejad, Cynthia H. McCollough, Hao Gong, Joel G. Fletcher, Corey T. Jensen, Achille Mileto, Lifeng Yu, Shuai Leng, Luis S. Guimaraes, Andrew D. Missert

Publikováno v: Radiographics : a review publication of the Radiological Society of North America, Inc. 41(5)

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4010699eba90ac522d021f2c1d81b0c7
https://pubmed.ncbi.nlm.nih.gov/34469209

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Random search as a neural network optimization strategy for Convolutional-Neural-Network (CNN)-based noise reduction in CT

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

Publikováno v: Proc SPIE Int Soc Opt Eng

In this study, we describe a systematic approach to optimize deep-learning-based image processing algorithms using random search. The optimization technique is demonstrated on a phantom-based noise reduction training framework; however, the technique

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6eb1351a3056ddceb03ac281a0de377e
https://doi.org/10.1117/12.2582143

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Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow

Autor: Joel G. Fletcher, Lifeng Yu, Andrew D. Missert, Cynthia H. McCollough, Shuai Leng

Publikováno v: Medical Imaging 2019: Physics of Medical Imaging.

In this study we simulated the effect of reconstructing computed tomography (CT) images with different reconstruction kernels by employing a convolutional neural network (CNN) to map images produced by a fixed input kernel to images produced by diffe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::71f16086261278eb7bb74c232f76fc11
https://doi.org/10.1117/12.2513240

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Magician’s Corner: 7. Using Convolutional Neural Networks to Reduce Noise in Medical Images

Autor: Nathan R. Huber, Andrew D. Missert, Bradley J. Erickson

Publikováno v: Radiol Artif Intell

This article shows how to train a convolutional neural network to reduce noise in CT images, although the principles apply to medical and nonmedical images; authors also explore mathematical and visually weighted loss functions to adjust the appearan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be99cb879dafad285f34d31ae4396e34
https://doi.org/10.1148/ryai.2020200036

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Improving the T2K Oscillation Analysis With fiTQun: A New Maximum-Likelihood Event Reconstruction for Super-Kamiokande

Autor: Andrew D. Missert

Publikováno v: Journal of Physics: Conference Series. 888:012066

A new event reconstruction algorithm, fiTQun, has been developed for the Super-Kamiokande detector. Super-Kamiokande is a ring-imaging water Cherenkov detector with a 22.5-kton fiducial volume located 1000 m underground in the Kamioka Mine in Japan.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8a21152e563883299b724e80701f31af
https://doi.org/10.1088/1742-6596/888/1/012066

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