Improved unsupervised physics‐informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
Purpose Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved versi...
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| Published in: | Magnetic resonance in medicine Vol. 86; no. 4; pp. 2250 - 2265 |
|---|---|
| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Wiley Subscription Services, Inc
01.10.2021
John Wiley and Sons Inc |
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| ISSN: | 0740-3194, 1522-2594, 1522-2594 |
| Online Access: | Get full text |
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| Abstract | Purpose
Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved version, IVIM‐NEToptim, and characterizes its superior performance in pancreatic cancer patients.
Method
In simulations (signal‐to‐noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM‐NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root‐mean‐square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CVNET), respectively. The best performing network, IVIM‐NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM‐NEToptim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within‐subject standard deviations (wSD) and treatment‐induced changes were assessed.
Results
In simulations (SNR = 20), IVIM‐NEToptim outperformed IVIM‐NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET(D) = 0.013 vs 0.104; CVNET(f) = 0.020 vs 0.054; CVNET(D*) = 0.036 vs 0.110). IVIM‐NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM‐NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM‐NEToptim detected the most individual patients with significant parameter changes compared to day‐to‐day variations.
Conclusion
IVIM‐NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data. |
|---|---|
| AbstractList | Earlier work showed that IVIM-NET
, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NET
, and characterizes its superior performance in pancreatic cancer patients.
In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CV
), respectively. The best performing network, IVIM-NET
was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET
's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.
In simulations (SNR = 20), IVIM-NET
outperformed IVIM-NET
in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CV
(D) = 0.013 vs 0.104; CV
(f) = 0.020 vs 0.054; CV
(D*) = 0.036 vs 0.110). IVIM-NET
showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NET
showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NET
detected the most individual patients with significant parameter changes compared to day-to-day variations.
IVIM-NET
is recommended for accurate, informative, and consistent IVIM fitting to DWI data. PurposeEarlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved version, IVIM‐NEToptim, and characterizes its superior performance in pancreatic cancer patients.MethodIn simulations (signal‐to‐noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM‐NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root‐mean‐square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CVNET), respectively. The best performing network, IVIM‐NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM‐NEToptim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within‐subject standard deviations (wSD) and treatment‐induced changes were assessed.ResultsIn simulations (SNR = 20), IVIM‐NEToptim outperformed IVIM‐NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET(D) = 0.013 vs 0.104; CVNET(f) = 0.020 vs 0.054; CVNET(D*) = 0.036 vs 0.110). IVIM‐NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM‐NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM‐NEToptim detected the most individual patients with significant parameter changes compared to day‐to‐day variations.ConclusionIVIM‐NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data. Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients.PURPOSEEarlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients.In simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.METHODIn simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.In simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations.RESULTSIn simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations.IVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.CONCLUSIONIVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data. Purpose Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved version, IVIM‐NEToptim, and characterizes its superior performance in pancreatic cancer patients. Method In simulations (signal‐to‐noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM‐NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root‐mean‐square error (NRMSE), Spearman’s ρ, and the coefficient of variation (CVNET), respectively. The best performing network, IVIM‐NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM‐NEToptim’s performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within‐subject standard deviations (wSD) and treatment‐induced changes were assessed. Results In simulations (SNR = 20), IVIM‐NEToptim outperformed IVIM‐NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET(D) = 0.013 vs 0.104; CVNET(f) = 0.020 vs 0.054; CVNET(D*) = 0.036 vs 0.110). IVIM‐NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM‐NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM‐NEToptim detected the most individual patients with significant parameter changes compared to day‐to‐day variations. Conclusion IVIM‐NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data. |
| Author | Crezee, Hans Nederveen, Aart J. Gurney‐Champion, Oliver J. While, Peter T. Laarhoven, Hanneke W. M. Barbieri, Sebastiano Klaassen, Remy Kaandorp, Misha P. T. |
| AuthorAffiliation | 4 Centre for Big Data Research in Health UNSW Sydney Australia 1 Department of Radiology and Nuclear Medicine Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam Amsterdam the Netherlands 5 Department of Medical Oncology Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam Amsterdam the Netherlands 3 Department of Circulation and Medical Imaging NTNU – Norwegian University of Science and Technology Trondheim Norway 2 Department of Radiology and Nuclear Medicine St. Olav’s University Hospital Trondheim Norway |
| AuthorAffiliation_xml | – name: 1 Department of Radiology and Nuclear Medicine Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam Amsterdam the Netherlands – name: 2 Department of Radiology and Nuclear Medicine St. Olav’s University Hospital Trondheim Norway – name: 4 Centre for Big Data Research in Health UNSW Sydney Australia – name: 3 Department of Circulation and Medical Imaging NTNU – Norwegian University of Science and Technology Trondheim Norway – name: 5 Department of Medical Oncology Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam Amsterdam the Netherlands |
| Author_xml | – sequence: 1 givenname: Misha P. T. orcidid: 0000-0002-7340-8256 surname: Kaandorp fullname: Kaandorp, Misha P. T. email: mpkaando@stud.ntnu.no organization: NTNU – Norwegian University of Science and Technology – sequence: 2 givenname: Sebastiano orcidid: 0000-0002-5919-372X surname: Barbieri fullname: Barbieri, Sebastiano organization: UNSW – sequence: 3 givenname: Remy surname: Klaassen fullname: Klaassen, Remy organization: Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam – sequence: 4 givenname: Hanneke W. M. orcidid: 0000-0003-3546-9709 surname: Laarhoven fullname: Laarhoven, Hanneke W. M. organization: Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam – sequence: 5 givenname: Hans surname: Crezee fullname: Crezee, Hans organization: Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam – sequence: 6 givenname: Peter T. orcidid: 0000-0003-2602-0758 surname: While fullname: While, Peter T. organization: NTNU – Norwegian University of Science and Technology – sequence: 7 givenname: Aart J. surname: Nederveen fullname: Nederveen, Aart J. organization: Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam – sequence: 8 givenname: Oliver J. orcidid: 0000-0003-1750-6617 surname: Gurney‐Champion fullname: Gurney‐Champion, Oliver J. organization: Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34105184$$D View this record in MEDLINE/PubMed |
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| Copyright | 2021 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. 2021. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | pancreatic cancer IVIM unsupervised physics-informed deep learning intravoxel incoherent motion deep neural network diffusion-weighted magnetic resonance imaging |
| Language | English |
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Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art... Earlier work showed that IVIM-NET , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art... PurposeEarlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art... Earlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art... |
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| SubjectTerms | Adenocarcinoma Algorithms Artificial neural networks Bayes Theorem Bayesian analysis Cancer Chemoradiotherapy Chemotherapy Coefficient of variation Computer architecture Consistency Deep Learning deep neural network Diffusion Magnetic Resonance Imaging diffusion‐weighted magnetic resonance imaging Humans intravoxel incoherent motion IVIM Machine learning Motion Neural networks Pancreatic cancer Pancreatic Neoplasms - diagnostic imaging Parameters Patients Performance evaluation Physics Radiation therapy Reproducibility of Results s—Computer Processing and Modeling unsupervised physics‐informed deep learning |
| Title | Improved unsupervised physics‐informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients |
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