Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes
© 2023 Institute of Physics and Engineering in Medicine..
Objective.The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments.Approach.We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference.Main results.The framework is demonstrated in 2D on a motion phantom, andin vivoon volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated byin silico3D experiments with a digital motion phantom. The framework was compared with template matching-a reference, image-based, method-and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement.Significance.The high accuracy in combination with a total latency of less than 10 ms-including data acquisition and processing-make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:68 |
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| Enthalten in: |
Physics in medicine and biology - 68(2023), 14 vom: 05. Juli |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Huttinga, Niek R F [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 06.07.2023 Date Revised 18.07.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1088/1361-6560/ace023 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM358411351 |
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| 100 | 1 | |a Huttinga, Niek R F |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 Institute of Physics and Engineering in Medicine. | ||
| 520 | |a Objective.The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that allows to track myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments.Approach.We present a tracking framework that requires only few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance, including both the acquisition of required data, and the tracking inference.Main results.The framework is demonstrated in 2D on a motion phantom, andin vivoon volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated byin silico3D experiments with a digital motion phantom. The framework was compared with template matching-a reference, image-based, method-and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement.Significance.The high accuracy in combination with a total latency of less than 10 ms-including data acquisition and processing-make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a MR-linac | |
| 650 | 4 | |a STAR | |
| 650 | 4 | |a magnetic resonance imaging | |
| 650 | 4 | |a motion management | |
| 650 | 4 | |a radiotherapy | |
| 650 | 4 | |a real-time tracking | |
| 650 | 4 | |a stereotactic arrhythmia radio-ablation | |
| 700 | 1 | |a Akdag, Osman |e verfasserin |4 aut | |
| 700 | 1 | |a Fast, Martin F |e verfasserin |4 aut | |
| 700 | 1 | |a Verhoeff, Joost J C |e verfasserin |4 aut | |
| 700 | 1 | |a Mohamed Hoesein, Firdaus A A |e verfasserin |4 aut | |
| 700 | 1 | |a van den Berg, Cornelis A T |e verfasserin |4 aut | |
| 700 | 1 | |a Sbrizzi, Alessandro |e verfasserin |4 aut | |
| 700 | 1 | |a Mandija, Stefano |e verfasserin |4 aut | |
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