Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis ( CS- PCA): Demonstration in lung tumor tracking.
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| Title: | Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis ( CS- PCA): Demonstration in lung tumor tracking. |
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| Authors: | Dietz, Bryson1, Yip, Eugene1, Yun, Jihyun1, Fallone, B. Gino2,3, Wachowicz, Keith4,5 |
| Source: | Medical Physics. Aug2017, Vol. 44 Issue 8, p3978-3989. 12p. |
| Subjects: | Computer simulation of image reconstruction, Image reconstruction, Image processing, Treatment of lung tumors, Compressed sensing |
| Abstract: | Purpose This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis ( CS- PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Methods Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS- PCA algorithm. Using a database of fully sampled k-space, principal components ( PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS- PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error ( NMSE), as well as the dice coefficients ( DC) and centroid displacement of the tumor segmentations. Results Our results demonstrate that CS- PCA performed superior than CS alone. The CS- PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS- PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710 HQ CPU @ 2.5 GHz), depending on the chosen parameters. Conclusions A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac- MRI system. Our CS- PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The reconstruction speed for the Split Bregman CS ranged from 200 to 260 ms, whereas the CS- PCA reconstruction speed ranged from 5 to 20 ms implemented using nonoptimized MATLAB code. [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.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis ( CS- PCA): Demonstration in lung tumor tracking. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dietz%2C+Bryson%22">Dietz, Bryson</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yip%2C+Eugene%22">Yip, Eugene</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yun%2C+Jihyun%22">Yun, Jihyun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Fallone%2C+B%2E+Gino%22">Fallone, B. Gino</searchLink><relatesTo>2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wachowicz%2C+Keith%22">Wachowicz, Keith</searchLink><relatesTo>4,5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Aug2017, Vol. 44 Issue 8, p3978-3989. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+simulation+of+image+reconstruction%22">Computer simulation of image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+of+lung+tumors%22">Treatment of lung tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis ( CS- PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Methods Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS- PCA algorithm. Using a database of fully sampled k-space, principal components ( PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS- PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error ( NMSE), as well as the dice coefficients ( DC) and centroid displacement of the tumor segmentations. Results Our results demonstrate that CS- PCA performed superior than CS alone. The CS- PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS- PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710 HQ CPU @ 2.5 GHz), depending on the chosen parameters. Conclusions A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac- MRI system. Our CS- PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The reconstruction speed for the Split Bregman CS ranged from 200 to 260 ms, whereas the CS- PCA reconstruction speed ranged from 5 to 20 ms implemented using nonoptimized MATLAB code. [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.12354 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 3978 Subjects: – SubjectFull: Computer simulation of image reconstruction Type: general – SubjectFull: Image reconstruction Type: general – SubjectFull: Image processing Type: general – SubjectFull: Treatment of lung tumors Type: general – SubjectFull: Compressed sensing Type: general Titles: – TitleFull: Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis ( CS- PCA): Demonstration in lung tumor tracking. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dietz, Bryson – PersonEntity: Name: NameFull: Yip, Eugene – PersonEntity: Name: NameFull: Yun, Jihyun – PersonEntity: Name: NameFull: Fallone, B. Gino – PersonEntity: Name: NameFull: Wachowicz, Keith IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 44 – Type: issue Value: 8 Titles: – TitleFull: Medical Physics Type: main |
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