Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93678
DC Field | Value | Language |
---|---|---|
dc.creator | Sun, D | en_US |
dc.creator | Liang, X | en_US |
dc.creator | Yin, F | en_US |
dc.creator | Cai, J | en_US |
dc.date.accessioned | 2022-07-25T02:44:04Z | - |
dc.date.available | 2022-07-25T02:44:04Z | - |
dc.identifier.issn | 2223-4292 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93678 | - |
dc.language.iso | en | en_US |
dc.publisher | AME Publishing Company | en_US |
dc.rights | © Quantitative Imaging in Medicine and Surgery. All right reserved. | en_US |
dc.rights | This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/). | en_US |
dc.rights | The following publication Sun, D., Liang, X., Yin, F., & Cai, J. (2019). Probability-based 3D k-space sorting for motion robust 4D-MRI. Quantitative Imaging in Medicine and Surgery, 9(7), 1326-1336 is available at https://doi.org/10.21037/qims.2019.07.06 | en_US |
dc.subject | Motion artifacts | en_US |
dc.subject | 4D-MRI | en_US |
dc.subject | K-space sorting | en_US |
dc.subject | Probability-based | en_US |
dc.subject | Extended cardiac-torso (XCAT) | en_US |
dc.title | Probability-based 3D k-space sorting for motion robust 4D-MRI | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1326 | en_US |
dc.identifier.epage | 1336 | en_US |
dc.identifier.volume | 9 | en_US |
dc.identifier.issue | 7 | en_US |
dc.identifier.doi | 10.21037/qims.2019.07.06 | en_US |
dcterms.abstract | Background: Current 4D-MRI techniques are prone to breathing-variation-induced motion artifacts. This study developed a novel method for motion-robust multi-cycle 4D-MRI using probability-based multi-cycle sorting to overcome this deficiency. | en_US |
dcterms.abstract | Methods: The main cycles were first extracted from the breathing signal. 3D k-space data were then sorted using a result-driven method for each main cycle. The new method was tested on a 4D-extended cardiac-torso (XCAT) phantom with a patient and an artificially generated breathing curve. For comparison, the k-space data were sorted using conventional phase sorting to generate single-cycle 4D-MRI images. Signal-to-noise ratio (SNR) of tumor and liver, tumor volume consistency, and average intensity projection (AIP) accuracy were compared between the two methods. The original phantom images were used as references for the evaluation. | en_US |
dcterms.abstract | Results: The new method showed improved tumor-to-liver SNR and tumor volume consistency as compared to 3D k-space phase sorting in both the simulated artificial and real patient breathing signals. For the artificial breathing cycles, the average tumor-to-liver SNR and standard deviation (SD) of tumor volume were 2.53 and 3.80% for cycle 1, 2.24 and 6.16% for cycle 2 of probability-based sorting as compared to 1.47 and 21.83% obtained using the phase sorting method; for the patient breathing curve, values of 1.99 and 2.71%, 1.97 and 3.29%, 1.88 and 4.16% were observed for cycle 1, cycle 2 and cycle 3 of probability-based sorting, versus 1.44 and 7.20% for phase sorting method. Furthermore, the AIP accuracy was improved in the probability-based sorting approach when compared to phase sorting, with the average intensity difference per voxel reduced from 0.39 to 0.15 for the artificial curve, and from 0.46 to 0.21 for the patient curve. | en_US |
dcterms.abstract | Conclusions: We demonstrated the feasibility of probability-based 3D k-space sorting for motion-robust multi-cycle 4D-MRI reconstruction with breathing variation induced motion artifact reduction compared with conventional 2D image sorting and 3D phase sorting methods. This new technique can potentially improve the accuracy of radiation treatment guidance for mobile targets. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Quantitative imaging in medicine and surgery, July 2019, v. 9, no. 7, p. 1326-1336 | en_US |
dcterms.isPartOf | Quantitative imaging in medicine and surgery | en_US |
dcterms.issued | 2019-07 | - |
dc.identifier.isi | WOS:000477984600012 | - |
dc.identifier.scopus | 2-s2.0-85076389161 | - |
dc.identifier.pmid | 31448217 | - |
dc.identifier.eissn | 2223-4306 | en_US |
dc.description.validate | 202207 bcvc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | HTI-0169 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | NIH (1R21CA165384 and 1R21CA195317) | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 25857696 | - |
Appears in Collections: | Journal/Magazine Article |
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27408-PB1-2625-R2.pdf | 1.88 MB | Adobe PDF | View/Open |
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