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Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI

OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) applicatio...

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Autores principales: Pradella, Maurice, Scott, Michael B., Omer, Muhammad, Hill, Seth K., Lockhart, Lisette, Yi, Xin, Amir-Khalili, Alborz, Sojoudi, Alireza, Allen, Bradley D., Avery, Ryan, Markl, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935671/
https://www.ncbi.nlm.nih.gov/pubmed/36307551
http://dx.doi.org/10.1007/s00330-022-09179-3
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author Pradella, Maurice
Scott, Michael B.
Omer, Muhammad
Hill, Seth K.
Lockhart, Lisette
Yi, Xin
Amir-Khalili, Alborz
Sojoudi, Alireza
Allen, Bradley D.
Avery, Ryan
Markl, Michael
author_facet Pradella, Maurice
Scott, Michael B.
Omer, Muhammad
Hill, Seth K.
Lockhart, Lisette
Yi, Xin
Amir-Khalili, Alborz
Sojoudi, Alireza
Allen, Bradley D.
Avery, Ryan
Markl, Michael
author_sort Pradella, Maurice
collection PubMed
description OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA). METHODS: We included 97 consecutive patients (age = 52.9 ± 16 years, 41 female) with 2D-CINE-PC-MRI imaging on 1.5T MRI systems at sinotubular junction (STJ), and 28/97 also received 2D-CINE-PC at main pulmonary artery (PA). A cardiovascular radiologist performed MA (reference) and corSAA (built-in tool) in commercial software for all cardiac time frames (median: 20, total contours per analysis: 2358 STJ, 680 PA). DL-analysis automatically performed VCD, followed by net flow (NF) and peak velocity (PV) quantification. Contours were compared using Dice similarity coefficients (DSC). Discrepant cases (> ± 10 mL or > ± 10 cm/s) were reviewed in detail. RESULTS: DL was successfully applied to 97% (121/125) of the 2D-CINE-PC-MRI series (STJ: 95/97, 98%, PA: 26/28, 93%). Compared to MA, mean DSC were 0.91 ± 0.02 (DL), 0.94 ± 0.02 (corSAA) at STJ, and 0.85 ± 0.08 (DL), 0.93 ± 0.02 (corSAA) at PA; this indicated good to excellent DL-performance. Flow quantification revealed similar NF at STJ (p = 0.48) and PA (p > 0.05) between methods while PV assessment was significantly different (STJ: p < 0.001, PA: p = 0.04). A detailed review showed noisy voxels in MA and corSAA impacted PV results. Overall, DL analysis compared to human assessments was accurate in 113/121 (93.4%) cases. CONCLUSIONS: Fully-automated DL-analysis of 2D-CINE-PC-MRI provided flow quantification at STJ and PA at expert level in > 93% of cases with results being available instantaneously. KEY POINTS: • Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases. • Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location. • The evaluated tool indicates usability in daily practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09179-3.
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spelling pubmed-99356712023-02-18 Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI Pradella, Maurice Scott, Michael B. Omer, Muhammad Hill, Seth K. Lockhart, Lisette Yi, Xin Amir-Khalili, Alborz Sojoudi, Alireza Allen, Bradley D. Avery, Ryan Markl, Michael Eur Radiol Magnetic Resonance OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA). METHODS: We included 97 consecutive patients (age = 52.9 ± 16 years, 41 female) with 2D-CINE-PC-MRI imaging on 1.5T MRI systems at sinotubular junction (STJ), and 28/97 also received 2D-CINE-PC at main pulmonary artery (PA). A cardiovascular radiologist performed MA (reference) and corSAA (built-in tool) in commercial software for all cardiac time frames (median: 20, total contours per analysis: 2358 STJ, 680 PA). DL-analysis automatically performed VCD, followed by net flow (NF) and peak velocity (PV) quantification. Contours were compared using Dice similarity coefficients (DSC). Discrepant cases (> ± 10 mL or > ± 10 cm/s) were reviewed in detail. RESULTS: DL was successfully applied to 97% (121/125) of the 2D-CINE-PC-MRI series (STJ: 95/97, 98%, PA: 26/28, 93%). Compared to MA, mean DSC were 0.91 ± 0.02 (DL), 0.94 ± 0.02 (corSAA) at STJ, and 0.85 ± 0.08 (DL), 0.93 ± 0.02 (corSAA) at PA; this indicated good to excellent DL-performance. Flow quantification revealed similar NF at STJ (p = 0.48) and PA (p > 0.05) between methods while PV assessment was significantly different (STJ: p < 0.001, PA: p = 0.04). A detailed review showed noisy voxels in MA and corSAA impacted PV results. Overall, DL analysis compared to human assessments was accurate in 113/121 (93.4%) cases. CONCLUSIONS: Fully-automated DL-analysis of 2D-CINE-PC-MRI provided flow quantification at STJ and PA at expert level in > 93% of cases with results being available instantaneously. KEY POINTS: • Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases. • Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location. • The evaluated tool indicates usability in daily practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-022-09179-3. Springer Berlin Heidelberg 2022-10-29 2023 /pmc/articles/PMC9935671/ /pubmed/36307551 http://dx.doi.org/10.1007/s00330-022-09179-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Magnetic Resonance
Pradella, Maurice
Scott, Michael B.
Omer, Muhammad
Hill, Seth K.
Lockhart, Lisette
Yi, Xin
Amir-Khalili, Alborz
Sojoudi, Alireza
Allen, Bradley D.
Avery, Ryan
Markl, Michael
Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
title Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
title_full Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
title_fullStr Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
title_full_unstemmed Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
title_short Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI
title_sort fully-automated deep learning-based flow quantification of 2d cine phase contrast mri
topic Magnetic Resonance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935671/
https://www.ncbi.nlm.nih.gov/pubmed/36307551
http://dx.doi.org/10.1007/s00330-022-09179-3
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