Real time volumetric MRI for 3D motion tracking via geometry‐informed deep learning.
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| Title: | Real time volumetric MRI for 3D motion tracking via geometry‐informed deep learning. |
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| Authors: | Liu, Lianli1 (AUTHOR) llliu@stanford.edu, Shen, Liyue2 (AUTHOR), Johansson, Adam3,4,5 (AUTHOR), Balter, James M.3 (AUTHOR), Cao, Yue3 (AUTHOR), Chang, Daniel1 (AUTHOR), Xing, Lei1,2 (AUTHOR) |
| Source: | Medical Physics. Sep2022, Vol. 49 Issue 9, p6110-6119. 10p. |
| Subjects: | Deep learning, Magnetic resonance imaging, Centroid, Time series analysis, Tracking algorithms, Longitudinal method, Spatial resolution, Image reconstruction |
| Abstract: | Purpose: To develop a geometry‐informed deep learning framework for volumetric MRI with sub‐second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time. Methods: A 2D–3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k‐space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high‐resolution volumetric images. Patient‐specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model‐reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5‐min time series of both ground truth and model‐reconstructed volumetric images with a temporal resolution of 340 ms. Results: Across the seven patients evaluated, the median distances between model‐predicted and ground truth GTV centroids in the superior‐inferior direction were 0.4 ± 0.3 mm and 0.5 ± 0.4 mm for cine and radial acquisitions, respectively. The 95‐percentile Hausdorff distances between model‐predicted and ground truth GTV contours were 4.7 ± 1.1 mm and 3.2 ± 1.5 mm for cine and radial acquisitions, which are of the same scale as cross‐plane image resolution. Conclusion: Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI‐guided radiotherapy precision. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 159063772 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real time volumetric MRI for 3D motion tracking via geometry‐informed deep learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Lianli%22">Liu, Lianli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> llliu@stanford.edu</i><br /><searchLink fieldCode="AR" term="%22Shen%2C+Liyue%22">Shen, Liyue</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Johansson%2C+Adam%22">Johansson, Adam</searchLink><relatesTo>3,4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Balter%2C+James+M%2E%22">Balter, James M.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Yue%22">Cao, Yue</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chang%2C+Daniel%22">Chang, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Lei%22">Xing, Lei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Sep2022, Vol. 49 Issue 9, p6110-6119. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Centroid%22">Centroid</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+algorithms%22">Tracking algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: To develop a geometry‐informed deep learning framework for volumetric MRI with sub‐second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time. Methods: A 2D–3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k‐space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high‐resolution volumetric images. Patient‐specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model‐reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5‐min time series of both ground truth and model‐reconstructed volumetric images with a temporal resolution of 340 ms. Results: Across the seven patients evaluated, the median distances between model‐predicted and ground truth GTV centroids in the superior‐inferior direction were 0.4 ± 0.3 mm and 0.5 ± 0.4 mm for cine and radial acquisitions, respectively. The 95‐percentile Hausdorff distances between model‐predicted and ground truth GTV contours were 4.7 ± 1.1 mm and 3.2 ± 1.5 mm for cine and radial acquisitions, which are of the same scale as cross‐plane image resolution. Conclusion: Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI‐guided radiotherapy precision. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.15822 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 6110 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Centroid Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Tracking algorithms Type: general – SubjectFull: Longitudinal method Type: general – SubjectFull: Spatial resolution Type: general – SubjectFull: Image reconstruction Type: general Titles: – TitleFull: Real time volumetric MRI for 3D motion tracking via geometry‐informed deep learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Lianli – PersonEntity: Name: NameFull: Shen, Liyue – PersonEntity: Name: NameFull: Johansson, Adam – PersonEntity: Name: NameFull: Balter, James M. – PersonEntity: Name: NameFull: Cao, Yue – PersonEntity: Name: NameFull: Chang, Daniel – PersonEntity: Name: NameFull: Xing, Lei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 49 – Type: issue Value: 9 Titles: – TitleFull: Medical Physics Type: main |
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