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Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Res...

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Autores principales: Allen, Timothy J., Henze Bancroft, Leah C., Wang, Kang, Wang, Ping Ni, Unal, Orhan, Estkowski, Lloyd D., Cashen, Ty A., Bayram, Ersin, Strigel, Roberta M., Holmes, James H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204486/
https://www.ncbi.nlm.nih.gov/pubmed/37218939
http://dx.doi.org/10.3390/tomography9030079
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author Allen, Timothy J.
Henze Bancroft, Leah C.
Wang, Kang
Wang, Ping Ni
Unal, Orhan
Estkowski, Lloyd D.
Cashen, Ty A.
Bayram, Ersin
Strigel, Roberta M.
Holmes, James H.
author_facet Allen, Timothy J.
Henze Bancroft, Leah C.
Wang, Kang
Wang, Ping Ni
Unal, Orhan
Estkowski, Lloyd D.
Cashen, Ty A.
Bayram, Ersin
Strigel, Roberta M.
Holmes, James H.
author_sort Allen, Timothy J.
collection PubMed
description Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.
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spelling pubmed-102044862023-05-24 Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network Allen, Timothy J. Henze Bancroft, Leah C. Wang, Kang Wang, Ping Ni Unal, Orhan Estkowski, Lloyd D. Cashen, Ty A. Bayram, Ersin Strigel, Roberta M. Holmes, James H. Tomography Article Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was −2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model. MDPI 2023-05-10 /pmc/articles/PMC10204486/ /pubmed/37218939 http://dx.doi.org/10.3390/tomography9030079 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Allen, Timothy J.
Henze Bancroft, Leah C.
Wang, Kang
Wang, Ping Ni
Unal, Orhan
Estkowski, Lloyd D.
Cashen, Ty A.
Bayram, Ersin
Strigel, Roberta M.
Holmes, James H.
Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
title Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
title_full Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
title_fullStr Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
title_full_unstemmed Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
title_short Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network
title_sort automated placement of scan and pre-scan volumes for breast mri using a convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204486/
https://www.ncbi.nlm.nih.gov/pubmed/37218939
http://dx.doi.org/10.3390/tomography9030079
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