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Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks

OBJECTIVE. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. APPROACH. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine...

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Autores principales: Azizmohammadi, Fariba, Castellanos, Iñaki Navarro, Miró, Joaquim, Segars, Paul, Samei, Ehsan, Duong, Luc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280804/
https://www.ncbi.nlm.nih.gov/pubmed/36595253
http://dx.doi.org/10.1088/1361-6560/acaba8
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author Azizmohammadi, Fariba
Castellanos, Iñaki Navarro
Miró, Joaquim
Segars, Paul
Samei, Ehsan
Duong, Luc
author_facet Azizmohammadi, Fariba
Castellanos, Iñaki Navarro
Miró, Joaquim
Segars, Paul
Samei, Ehsan
Duong, Luc
author_sort Azizmohammadi, Fariba
collection PubMed
description OBJECTIVE. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. APPROACH. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D. A many-to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. MAIN RESULTS. Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm. SIGNIFICANCE. This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions.
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spelling pubmed-102808042023-06-20 Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks Azizmohammadi, Fariba Castellanos, Iñaki Navarro Miró, Joaquim Segars, Paul Samei, Ehsan Duong, Luc Phys Med Biol Article OBJECTIVE. To develop a novel patient-specific cardio-respiratory motion prediction approach for X-ray angiography time series based on a simple long short-term memory (LSTM) model. APPROACH. The cardio-respiratory motion behavior in an X-ray image sequence was represented as a sequence of 2D affine transformation matrices, which provide the displacement information of contrasted moving objects (arteries and medical devices) in a sequence. The displacement information includes translation, rotation, shearing, and scaling in 2D. A many-to-many LSTM model was developed to predict 2D transformation parameters in matrix form for future frames based on previously generated images. The method was developed with 64 simulated phantom datasets (pediatric and adult patients) using a realistic cardio-respiratory motion simulator (XCAT) and was validated using 10 different patient X-ray angiography sequences. MAIN RESULTS. Using this method we achieved less than 1 mm prediction error for complex cardio-respiratory motion prediction. The following mean prediction error values were recorded over all the simulated sequences: 0.39 mm (for both motions), 0.33 mm (for only cardiac motion), and 0.47 mm (for only respiratory motion). The mean prediction error for the patient dataset was 0.58 mm. SIGNIFICANCE. This study paves the road for a patient-specific cardio-respiratory motion prediction model, which might improve navigation guidance during cardiac interventions. 2023-01-05 /pmc/articles/PMC10280804/ /pubmed/36595253 http://dx.doi.org/10.1088/1361-6560/acaba8 Text en https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Azizmohammadi, Fariba
Castellanos, Iñaki Navarro
Miró, Joaquim
Segars, Paul
Samei, Ehsan
Duong, Luc
Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks
title Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks
title_full Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks
title_fullStr Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks
title_full_unstemmed Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks
title_short Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks
title_sort patient-specific cardio-respiratory motion prediction in x-ray angiography using lstm networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280804/
https://www.ncbi.nlm.nih.gov/pubmed/36595253
http://dx.doi.org/10.1088/1361-6560/acaba8
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