Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors.
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| Title: | Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors. |
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| Authors: | Yip, Eugene1, Yun, Jihyun2, Wachowicz, Keith3, Heikal, Amr A.2, Gabos, Zsolt4, Rathee, Satyapal3, Fallone, B. G.5 |
| Source: | Medical Physics. Aug2014, Vol. 41 Issue 8, p1-N.PAG. 12p. |
| Subjects: | Lung cancer patients, Magnetic resonance imaging of cancer, Data analysis, Spatio-temporal variation, Two-dimensional models, Image quality analysis |
| Abstract: | Purpose: Hybrid radiotherapy-MRI devices promise real time tracking of moving tumors to focus the radiation portals to the tumor during irradiation. This approach will benefit from the increased temporal resolution of MRI's data acquisition and reconstruction. In this work, the authors propose a novel spatial-temporal compressed sensing (CS) imaging strategy for the real time MRI--prior data assisted compressed sensing (PDACS), which aims to improve the image quality of the conventional CS without significantly increasing reconstruction times. Methods: Conventional 2D CS requires a random sampling of partial k-space data, as well as an iterative reconstruction that simultaneously enforces the image's sparsity in a transform domain as well as maintains the fidelity to the acquired k-space. PDACS method requires the additional acquisition of the prior data, and for reconstruction, it additionally enforces fidelity to the prior k-space domain similar to viewsharing. In this work, the authors evaluated the proposed PDACS method by comparing its results to those obtained from the 2D CS and viewsharing methods when performed individually. All three methods are used to reconstruct images from lung cancer patients whose tumors move and who are likely to benefit from lung tumor tracking. The patients are scanned, using a 3T MRI, under free breathing using the fully sampled k-space with 2D dynamic bSSFP sequence in a sagittal plane containing lung tumor. These images form a reference set for the evaluation of the partial k-space methods. To create partial k-space, the fully sampled k-space is retrospectively undersampled to obtain a range of acquisition acceleration factors, and reconstructed with 2D-CS, PDACS, and viewshare methods. For evaluation, metrics assessing global image artifacts as well as tumor contour shape fidelity are determined from the reconstructed images. These analyses are performed both for the original 3T images and those at a simulated 0.5T equivalent noise level. Results: In the 3.0T images, the PDACS strategy is shown to give superior results compared to viewshare and conventional 2D CS using all metrics. The 2D-CS tends to perform better than viewshare at the low acceleration factors, while the opposite is true at the high acceleration factors. At simulated 0.5T images, PDACS method performs only marginally better than the viewsharing method, both of which are superior compared to 2D CS. The PDACS image reconstruction time (0.3 s/image) is similar to that of the conventional 2D CS. Conclusions: The PDACS method can potentially improve the real time tracking of moving tumors by significantly increasing MRI's data acquisition speeds. In 3T images, the PDACS method does provide a benefit over the other two methods in terms of both the overall image quality and the ability to accurately and automatically contour the tumor shape. MRI's data acquisition may be accelerated using the simpler viewsharing strategy at the lower, 0.5T magnetic field, as the marginal benefit of the PDACS method may not justify its additional reconstruction times. [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: 97387006 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors. – Name: Author Label: Authors Group: Au Data: <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>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wachowicz%2C+Keith%22">Wachowicz, Keith</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Heikal%2C+Amr+A%2E%22">Heikal, Amr A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Gabos%2C+Zsolt%22">Gabos, Zsolt</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Rathee%2C+Satyapal%22">Rathee, Satyapal</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Fallone%2C+B%2E+G%2E%22">Fallone, B. G.</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Aug2014, Vol. 41 Issue 8, p1-N.PAG. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Lung+cancer+patients%22">Lung cancer patients</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging+of+cancer%22">Magnetic resonance imaging of cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Spatio-temporal+variation%22">Spatio-temporal variation</searchLink><br /><searchLink fieldCode="DE" term="%22Two-dimensional+models%22">Two-dimensional models</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: Hybrid radiotherapy-MRI devices promise real time tracking of moving tumors to focus the radiation portals to the tumor during irradiation. This approach will benefit from the increased temporal resolution of MRI's data acquisition and reconstruction. In this work, the authors propose a novel spatial-temporal compressed sensing (CS) imaging strategy for the real time MRI--prior data assisted compressed sensing (PDACS), which aims to improve the image quality of the conventional CS without significantly increasing reconstruction times. Methods: Conventional 2D CS requires a random sampling of partial k-space data, as well as an iterative reconstruction that simultaneously enforces the image's sparsity in a transform domain as well as maintains the fidelity to the acquired k-space. PDACS method requires the additional acquisition of the prior data, and for reconstruction, it additionally enforces fidelity to the prior k-space domain similar to viewsharing. In this work, the authors evaluated the proposed PDACS method by comparing its results to those obtained from the 2D CS and viewsharing methods when performed individually. All three methods are used to reconstruct images from lung cancer patients whose tumors move and who are likely to benefit from lung tumor tracking. The patients are scanned, using a 3T MRI, under free breathing using the fully sampled k-space with 2D dynamic bSSFP sequence in a sagittal plane containing lung tumor. These images form a reference set for the evaluation of the partial k-space methods. To create partial k-space, the fully sampled k-space is retrospectively undersampled to obtain a range of acquisition acceleration factors, and reconstructed with 2D-CS, PDACS, and viewshare methods. For evaluation, metrics assessing global image artifacts as well as tumor contour shape fidelity are determined from the reconstructed images. These analyses are performed both for the original 3T images and those at a simulated 0.5T equivalent noise level. Results: In the 3.0T images, the PDACS strategy is shown to give superior results compared to viewshare and conventional 2D CS using all metrics. The 2D-CS tends to perform better than viewshare at the low acceleration factors, while the opposite is true at the high acceleration factors. At simulated 0.5T images, PDACS method performs only marginally better than the viewsharing method, both of which are superior compared to 2D CS. The PDACS image reconstruction time (0.3 s/image) is similar to that of the conventional 2D CS. Conclusions: The PDACS method can potentially improve the real time tracking of moving tumors by significantly increasing MRI's data acquisition speeds. In 3T images, the PDACS method does provide a benefit over the other two methods in terms of both the overall image quality and the ability to accurately and automatically contour the tumor shape. MRI's data acquisition may be accelerated using the simpler viewsharing strategy at the lower, 0.5T magnetic field, as the marginal benefit of the PDACS method may not justify its additional reconstruction times. [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: 10.1118/1.4885960 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Lung cancer patients Type: general – SubjectFull: Magnetic resonance imaging of cancer Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Spatio-temporal variation Type: general – SubjectFull: Two-dimensional models Type: general – SubjectFull: Image quality analysis Type: general Titles: – TitleFull: Prior data assisted compressed sensing: A novel MR imaging strategy for real time tracking of lung tumors. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yip, Eugene – PersonEntity: Name: NameFull: Yun, Jihyun – PersonEntity: Name: NameFull: Wachowicz, Keith – PersonEntity: Name: NameFull: Heikal, Amr A. – PersonEntity: Name: NameFull: Gabos, Zsolt – PersonEntity: Name: NameFull: Rathee, Satyapal – PersonEntity: Name: NameFull: Fallone, B. G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 41 – Type: issue Value: 8 Titles: – TitleFull: Medical Physics Type: main |
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