Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering
Purpose: MR‐based pseudo‐CT has an important role in MR‐based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo‐CT generation of the brain using a...
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| Published in: | Medical physics (Lancaster) Vol. 42; no. 8; pp. 4974 - 4986 |
|---|---|
| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
American Association of Physicists in Medicine
01.08.2015
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| Subjects: | |
| ISSN: | 0094-2405, 2473-4209 |
| Online Access: | Get full text |
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| Abstract | Purpose:
MR‐based pseudo‐CT has an important role in MR‐based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo‐CT generation of the brain using a single‐acquisition, undersampled ultrashort echo time (UTE)‐mDixon pulse sequence.
Methods:
Nine patients were recruited for this study. For each patient, a 190‐s, undersampled, single acquisition UTE‐mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point‐spread functions of three external MR markers. Two‐point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2∗ images (1/T2∗) were then estimated and were used to provide bone information. Three image features, i.e., Dixon‐fat, Dixon‐water, and R2∗, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c‐means (FCM) algorithm. A two‐step, automatic tissue‐assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo‐CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low‐dose CT was acquired for each patient and was used as the gold standard for comparison.
Results:
The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM‐estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo‐CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low‐dose CT.
Conclusions:
The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo‐CT generation. |
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| AbstractList | Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2{sup ∗} images (1/T2{sup ∗}) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2{sup ∗}, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation. MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence. Nine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2(∗) images (1/T2(∗)) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2(∗), were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison. The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (-22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT. The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation. Purpose: MR‐based pseudo‐CT has an important role in MR‐based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo‐CT generation of the brain using a single‐acquisition, undersampled ultrashort echo time (UTE)‐mDixon pulse sequence. Methods: Nine patients were recruited for this study. For each patient, a 190‐s, undersampled, single acquisition UTE‐mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point‐spread functions of three external MR markers. Two‐point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2∗ images (1/T2∗) were then estimated and were used to provide bone information. Three image features, i.e., Dixon‐fat, Dixon‐water, and R2∗, were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c‐means (FCM) algorithm. A two‐step, automatic tissue‐assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo‐CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low‐dose CT was acquired for each patient and was used as the gold standard for comparison. Results: The contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM‐estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo‐CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (−22 ± 29 HU and 130 ± 16 HU) when compared to low‐dose CT. Conclusions: The MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo‐CT generation. PURPOSEMR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence.METHODSNine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2(∗) images (1/T2(∗)) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2(∗), were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison.RESULTSThe contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (-22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT.CONCLUSIONSThe MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation. |
| Author | Pereira, Gisele C. Muzic, Raymond F. Hu, Lingzhi Jordan, David W. Traughber, Melanie Su, Kuan‐Hao Qian, Pengjiang Herrmann, Karin A. Helle, Michael Thompson, Cheryl L. Traughber, Bryan J. Stehning, Christian |
| Author_xml | – sequence: 1 givenname: Kuan‐Hao surname: Su fullname: Su, Kuan‐Hao – sequence: 2 givenname: Lingzhi surname: Hu fullname: Hu, Lingzhi – sequence: 3 givenname: Christian surname: Stehning fullname: Stehning, Christian – sequence: 4 givenname: Michael surname: Helle fullname: Helle, Michael – sequence: 5 givenname: Pengjiang surname: Qian fullname: Qian, Pengjiang – sequence: 6 givenname: Cheryl L. surname: Thompson fullname: Thompson, Cheryl L. – sequence: 7 givenname: Gisele C. surname: Pereira fullname: Pereira, Gisele C. – sequence: 8 givenname: David W. surname: Jordan fullname: Jordan, David W. – sequence: 9 givenname: Karin A. surname: Herrmann fullname: Herrmann, Karin A. – sequence: 10 givenname: Melanie surname: Traughber fullname: Traughber, Melanie – sequence: 11 givenname: Raymond F. surname: Muzic fullname: Muzic, Raymond F. – sequence: 12 givenname: Bryan J. surname: Traughber fullname: Traughber, Bryan J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26233223$$D View this record in MEDLINE/PubMed https://www.osti.gov/biblio/22581339$$D View this record in Osti.gov |
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| Cites_doi | 10.1118/1.4842575 10.1007/s00259‐013‐2660‐z 10.1007/s00259‐011‐1842‐9 10.1088/0031‐9155/45/2/314 10.1118/1.3651640 10.1002/mrm.21879 10.1007/s10334‐012‐0328‐5 10.1007/s11307‐014‐0777‐5 10.1007/s00259‐013‐2652‐z 10.1007/s00259‐014‐2751‐5 10.1002/jmri.24680 10.1118/1.4873315 10.1007/s00259‐012‐2113‐0 10.2967/jnumed.111.092577 10.3109/0284186X.2013.819119 10.1118/1.4711807 10.1097/RLI.0b013e318283292f 10.1016/j.ejrad.2014.03.022 10.1186/1748‐717X‐8‐51 10.1088/0031‐9155/58/23/8419 10.1118/1.4886766 10.1016/j.nima.2012.09.005 10.1002/mrm.23217 10.1007/s10334‐014‐0445‐4 10.1007/s10334‐012‐0330‐y 10.1016/j.radonc.2011.01.012 10.1016/0730‐725X(88)90472‐9 10.1097/00004424‐198703000‐00005 10.1088/0031‐9155/56/10/013 10.1109/91.413225 10.1118/1.4729716 10.1002/mrm.20128 10.1088/0031‐9155/59/23/7501 10.2967/jnumed.109.064824 10.2967/jnumed.109.069112 10.1088/0031‐9155/59/21/6595 10.1016/S0167‐8140(02)00440‐1 10.2967/jnumed.113.126813 10.2967/jnumed.112.113209 10.3109/0284186X.2012.692883 10.2967/jnumed.110.085076 10.1002/jmri.24232 10.1118/1.4894709 10.1006/jmre.1998.1396 10.1120/jacmp.v12i4.3522 10.1118/1.3578928 10.1007/s00259‐013‐2467‐y 10.1016/j.ejrad.2011.10.005 10.1007/s10334‐012‐0339‐2 10.2967/jnumed.108.054726 10.1002/mrm.22578 10.2967/jnumed.109.065425 10.1118/1.4812685 10.1002/mrm.1081 |
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| Copyright | 2015 American Association of Physicists in Medicine Copyright © 2015 American Association of Physicists in Medicine 2015 American Association of Physicists in Medicine |
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| Notes | Author to whom correspondence should be addressed. Electronic mail bryan.traughber@case.edu ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author to whom correspondence should be addressed. Electronic mail: bryan.traughber@case.edu |
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| References | 2012; 81 2013; 48 2013; 26 2013; 702 2009; 61 2000; 45 2013; 40 2011; 52 2011; 98 2012; 39 2011; 12 2011; 56 2003 2014; 28 2014; 41 2011; 38 1998; 132 2001; 45 2013; 8 2014; 83 1995; 3 2012; 53 2001; 42 2004; 52 1987; 22 2013; 58 2013; 54 2009; 50 1988; 6 2013; 52 2014; 59 2011; 65 2014; 17 2014; 39 2012; 68 2014; 55 2010; 51 2003; 66 2007; 48 e_1_2_7_5_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_60_1 e_1_2_7_17_1 e_1_2_7_15_1 e_1_2_7_41_1 e_1_2_7_13_1 e_1_2_7_43_1 e_1_2_7_11_1 e_1_2_7_45_1 Teplinsky E. (e_1_2_7_2_1) e_1_2_7_47_1 e_1_2_7_26_1 e_1_2_7_49_1 e_1_2_7_28_1 e_1_2_7_50_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_52_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_54_1 e_1_2_7_21_1 e_1_2_7_35_1 e_1_2_7_56_1 e_1_2_7_37_1 e_1_2_7_58_1 e_1_2_7_39_1 e_1_2_7_6_1 e_1_2_7_4_1 e_1_2_7_8_1 e_1_2_7_18_1 e_1_2_7_16_1 e_1_2_7_40_1 e_1_2_7_14_1 e_1_2_7_42_1 e_1_2_7_12_1 e_1_2_7_44_1 Muzic R. F. (e_1_2_7_38_1) 2001; 42 e_1_2_7_10_1 e_1_2_7_46_1 e_1_2_7_48_1 e_1_2_7_27_1 e_1_2_7_29_1 Surti S. (e_1_2_7_34_1) 2007; 48 e_1_2_7_51_1 e_1_2_7_30_1 e_1_2_7_53_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_55_1 e_1_2_7_22_1 e_1_2_7_57_1 e_1_2_7_20_1 e_1_2_7_36_1 e_1_2_7_59_1 23927344 - Med Phys. 2013 Aug;40(8):082301 25321341 - Phys Med Biol. 2014 Nov 7;59(21):6595-606 24123342 - J Magn Reson Imaging. 2014 Apr;39(4):988-97 3054380 - Magn Reson Imaging. 1988 Jul-Aug;6(4):355-68 3557896 - Invest Radiol. 1987 Mar;22(3):209-15 24784377 - Med Phys. 2014 May;41(5):051711 25096328 - Mol Imaging Biol. 2015 Apr;17(2):264-76 24965907 - J Magn Reson Imaging. 2015 May;41(5):1465-74 23442772 - Invest Radiol. 2013 May;48(5):323-32 22712634 - Acta Oncol. 2013 Apr;52(3):612-8 24425423 - Eur J Nucl Med Mol Imaging. 2014 Jun;41(6):1176-89 15236387 - Magn Reson Med. 2004 Jul;52(1):197-203 12648793 - Radiother Oncol. 2003 Feb;66(2):203-16 20008992 - J Nucl Med. 2010 Jan;51(1):77-84 23984810 - Acta Oncol. 2013 Oct;52(7):1369-73 24217183 - Phys Med Biol. 2013 Dec 7;58(23 ):8419-35 23817684 - Eur J Nucl Med Mol Imaging. 2013 Oct;40(10 ):1486-99 22089006 - J Appl Clin Med Phys. 2011 Nov 15;12(4):3522 22047365 - Med Phys. 2011 Nov;38(11):6010-9 22505568 - J Nucl Med. 2012 May;53(5):796-804 20439508 - J Nucl Med. 2010 May;51(5):812-8 17332626 - J Nucl Med. 2007 Mar;48(3):471-80 24352789 - Eur J Nucl Med Mol Imaging. 2014 May;41(5):887-97 25393873 - Phys Med Biol. 2014 Dec 7;59(23):7501-19 24833495 - J Nucl Med. 2014 Jun;55(6):923-31 22830764 - Med Phys. 2012 Jul;39(7):4306-15 21724984 - J Nucl Med. 2011 Jul;52(7):1142-9 19235919 - Magn Reson Med. 2009 May;61(5):1083-9 9615415 - J Magn Reson. 1998 May;132(1):150-3 22189904 - Magn Reson Med. 2012 Jul;68(1):120-9 11337554 - J Nucl Med. 2001 Apr;42(4):636-45 21339009 - Radiother Oncol. 2011 Mar;98 (3):330-4 22923020 - MAGMA. 2013 Feb;26(1):115-26 24387496 - Med Phys. 2014 Jan;41(1):011704 23497586 - Radiat Oncol. 2013 Mar 06;8:51 22755711 - Med Phys. 2012 Jun;39(6):3283-90 20860006 - Magn Reson Med. 2011 Jan;65(1):96-107 19289430 - J Nucl Med. 2009 Apr;50(4):520-6 22526955 - Eur J Nucl Med Mol Imaging. 2012 Jul;39(7):1154-60 10701515 - Phys Med Biol. 2000 Feb;45(2):459-78 22955943 - MAGMA. 2013 Feb;26(1):127-36 21776807 - Med Phys. 2011 May;38(5):2708-14 24780817 - Eur J Radiol. 2014 Jul;83(7):1177-83 11283987 - Magn Reson Med. 2001 Apr;45(4):595-604 24652234 - Eur J Nucl Med Mol Imaging. 2014 Aug;41(8):1574-84 25086551 - Med Phys. 2014 Aug;41(8):082302 21688050 - Eur J Nucl Med Mol Imaging. 2011 Sep;38(9):1691-701 24009273 - J Nucl Med. 2013 Oct;54(10 ):1768-74 22078793 - Eur J Radiol. 2012 Oct;81(10):2658-65 25281971 - Med Phys. 2014 Oct;41(10):102301 21508443 - Phys Med Biol. 2011 May 21;56(10):3091-106 22868642 - MAGMA. 2013 Feb;26(1):5-23 20810759 - J Nucl Med. 2010 Sep;51(9):1431-8 |
| References_xml | – volume: 51 start-page: 77 year: 2010 end-page: 84 article-title: Integrated software environment based on for analyzing tracer pharmacokinetics with molecular imaging publication-title: J. Nucl. Med. – volume: 41 start-page: 051711 year: 2014 article-title: MRI‐based treatment planning with pseudo CT generated through atlas registration publication-title: Med. Phys. – volume: 45 start-page: 459 year: 2000 end-page: 478 article-title: Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions publication-title: Phys. Med. Biol. – volume: 41 start-page: 1574 year: 2014 end-page: 1584 article-title: Comparison of MR‐based attenuation correction and CT‐based attenuation correction of whole‐body PET/MR imaging publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 66 start-page: 203 year: 2003 end-page: 216 article-title: Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone publication-title: Radiother. Oncol. – volume: 65 start-page: 96 year: 2011 end-page: 107 article-title: Dual‐echo Dixon imaging with flexible choice of echo times publication-title: Magn. Reson. Med. – volume: 52 start-page: 197 year: 2004 end-page: 203 article-title: Fast isotropic volumetric coronary MR angiography using free‐breathing 3D radial balanced FFE acquisition publication-title: Magn. Reson. Med. – volume: 3 start-page: 370 year: 1995 end-page: 379 article-title: On cluster validity for the fuzzy c‐means model publication-title: IEEE Trans. Fuzzy Syst. – volume: 41 start-page: 887 year: 2014 end-page: 897 article-title: Comparison of PET/CT and PET/MRI hybrid systems using a 68Ga‐labelled PSMA ligand for the diagnosis of recurrent prostate cancer: Initial experience publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 38 start-page: 1691 year: 2011 end-page: 1701 article-title: Value of a Dixon‐based MR/PET attenuation correction sequence for the localization and evaluation of PET‐positive lesions publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 58 start-page: 8419 year: 2013 end-page: 8435 article-title: Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy publication-title: Phys. Med. Biol. – volume: 55 start-page: 923 year: 2014 end-page: 931 article-title: Systematic comparison of the performance of integrated whole‐body PET/MR imaging to conventional PET/CT for 18F‐FDG brain imaging in patients examined for suspected dementia publication-title: J. Nucl. Med. – volume: 52 start-page: 1369 year: 2013 end-page: 1373 article-title: Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information‐potential application for MR‐only radiotherapy and attenuation correction in positron emission tomography publication-title: Acta Oncol. – volume: 51 start-page: 812 year: 2010 end-page: 818 article-title: MRI‐based attenuation correction for PET/MRI using ultrashort echo time sequences publication-title: J. Nucl. Med. – volume: 41 start-page: 1176 year: 2014 end-page: 1189 article-title: A comparison of CT‐and MR‐based attenuation correction in neurological PET publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 132 start-page: 150 year: 1998 end-page: 153 article-title: Simple correction method fork‐space trajectory deviations in MRI publication-title: J. Magn. Reson. – volume: 26 start-page: 127 year: 2013 end-page: 136 article-title: Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images publication-title: Magn. Reson. Mater. Phys., Biol. Med. – volume: 39 start-page: 4306 year: 2012 end-page: 4315 article-title: Simultaneous PET/MR imaging: MR‐based attenuation correction of local radiofrequency surface coils publication-title: Med. Phys. – volume: 54 start-page: 1768 year: 2013 end-page: 1774 article-title: MR‐based attenuation correction methods for improved PET quantification in lesions within bone and susceptibility artifact regions publication-title: J. Nucl. Med. – volume: 48 start-page: 471 year: 2007 end-page: 480 article-title: Performance of Philips Gemini TF PET/CT scanner with special consideration for its time‐of‐flight imaging capabilities publication-title: J. Nucl. Med. – volume: 39 start-page: 1154 year: 2012 end-page: 1160 article-title: PET/MR imaging of bone lesions–implications for PET quantification from imperfect attenuation correction publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 83 start-page: 1177 year: 2014 end-page: 1183 article-title: Whole‐body PET/MRI: The effect of bone attenuation during MR‐based attenuation correction in oncology imaging publication-title: Eur. J. Radiol. – volume: 51 start-page: 1431 year: 2010 end-page: 1438 article-title: Toward implementing an MRI‐based PET attenuation‐correction method for neurologic studies on the MR‐PET brain prototype publication-title: J. Nucl. Med. – volume: 42 start-page: 636 year: 2001 end-page: 645 article-title: : Compartment model kinetic analysis tool publication-title: J. Nucl. Med. – volume: 39 start-page: 988 year: 2014 end-page: 997 article-title: Pulmonary 3 T MRI with ultrashort TEs: Influence of ultrashort echo time interval on pulmonary functional and clinical stage assessments of smokers publication-title: J. Magn. Reson. Imaging – volume: 52 start-page: 1142 year: 2011 end-page: 1149 article-title: Attenuation correction methods suitable for brain imaging with a PET/MRI scanner: A comparison of tissue atlas and template attenuation map approaches publication-title: J. Nucl. Med. – volume: 56 start-page: 3091 year: 2011 end-page: 3106 article-title: Design and performance evaluation of a whole‐body ingenuity TF PET–MRI system publication-title: Phys. Med. Biol. – volume: 702 start-page: 114 year: 2013 end-page: 116 article-title: Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR publication-title: Nucl. Instrum. Methods Phys. Res., Sect. A – volume: 41 start-page: 102301 year: 2014 article-title: K‐space sampling optimization for ultrashort TE imaging of cortical bone: Applications in radiation therapy planning and MR‐based PET attenuation correction publication-title: Med. Phys. – volume: 26 start-page: 115 year: 2013 end-page: 126 article-title: Quantitative accuracy of attenuation correction in the Philips ingenuity TF whole‐body PET/MR system: A direct comparison with transmission‐based attenuation correction publication-title: Magn. Reson. Mater. Phys., Biol. Med. – volume: 41 start-page: 082302 year: 2014 article-title: CT substitutes derived from MR images reconstructed with parallel imaging publication-title: Med. Phys. – volume: 53 start-page: 796 year: 2012 end-page: 804 article-title: MRI‐based attenuation correction for hybrid PET/MRI systems: A 4‐Class tissue segmentation technique using a combined ultrashort‐echo‐time/Dixon MRI sequence publication-title: J. Nucl. Med. – volume: 38 start-page: 6010 year: 2011 end-page: 6019 article-title: The effect of errors in segmented attenuation maps on PET quantification publication-title: Med. Phys. – year: 2003 article-title: Correction of trajectory errors in radial acquisitions – volume: 8 start-page: 1 year: 2013 end-page: 13 article-title: MRI‐based treatment plan simulation and adaptation for ion radiotherapy using a classification‐based approach publication-title: Radiat. Oncol. – volume: 41 start-page: 011704 year: 2014 article-title: A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI‐based radiotherapy treatment planning of prostate cancer publication-title: Med. Phys. – volume: 39 start-page: 3283 year: 2012 end-page: 3290 article-title: Voxel‐wise uncertainty in CT substitute derived from MRI publication-title: Med. Phys. – volume: 45 start-page: 595 year: 2001 end-page: 604 article-title: Neuroimaging at 1.5 T and 3.0 T: Comparison of oxygenation‐sensitive magnetic resonance imaging publication-title: Magn. Reson. Med. – volume: 28 start-page: 101 year: 2014 article-title: Erratum to: Quantitative accuracy of attenuation correction in the Philips ingenuity TF whole‐body PET/MR system: A direct comparison with transmission‐based attenuation correction publication-title: Magn. Reson. Mater. Phys., Biol. Med. – volume: 52 start-page: 612 year: 2013 end-page: 618 article-title: T1/T2 ‐weighted MRI provides clinically relevant pseudo‐CT density data for the pelvic bones in MRI‐only based radiotherapy treatment planning publication-title: Acta Oncol. – volume: 40 start-page: 1486 year: 2013 end-page: 1499 article-title: Comparison of integrated whole‐body [11C]choline PET/MR with PET/CT in patients with prostate cancer publication-title: Eur. J. Nucl. Med. Mol. Imaging – volume: 68 start-page: 120 year: 2012 end-page: 129 article-title: Simple method for MR gradient system characterization and k‐space trajectory estimation publication-title: Magn. Reson. Med. – volume: 38 start-page: 2708 year: 2011 end-page: 2714 article-title: CT substitute derived from MRI sequences with ultrashort echo time publication-title: Med. Phys. – volume: 48 start-page: 323 year: 2013 end-page: 332 article-title: Magnetic resonance–based attenuation correction for PET/MR hybrid imaging using continuous valued attenuation maps publication-title: Invest. Radiol. – article-title: Detection of metastases in breast cancer: Is whole body PET/MR better than PET/CT? publication-title: Journal of Clinical Oncology – volume: 26 start-page: 5 year: 2013 end-page: 23 article-title: Sequential whole‐body PET/MR scanner: Concept, clinical use, and optimisation after two years in the clinic. The manufacturer's perspective publication-title: Magn. Reson. Mater. Phys., Biol. Med. – volume: 41 start-page: 1465 year: 2014 end-page: 1474 article-title: Ultra‐short echo‐time pulmonary MRI: Evaluation and reproducibility in COPD subjects with and without bronchiectasis publication-title: J. Magn. Reson. Imaging – volume: 61 start-page: 1083 year: 2009 end-page: 1089 article-title: Double half RF pulses for reduced sensitivity to eddy currents in UTE imaging publication-title: Magn. Reson. Med. – volume: 22 start-page: 209 year: 1987 end-page: 215 article-title: Two postprocessing CT techniques for determining the composition of trabecular bone publication-title: Invest. Radiol. – volume: 59 start-page: 7501 year: 2014 end-page: 7519 article-title: A voxel‐based investigation for MRI‐only radiotherapy of the brain using ultra short echo times publication-title: Phys. Med. Biol. – volume: 81 start-page: 2658 year: 2012 end-page: 2665 article-title: Simulation of a MR–PET protocol for staging of head‐and‐neck cancer including Dixon MR for attenuation correction publication-title: Eur. J. Radiol. – volume: 98 start-page: 330 year: 2011 end-page: 334 article-title: MRI‐guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI‐based dose planning publication-title: Radiother. Oncol. – volume: 59 start-page: 6595 year: 2014 end-page: 6606 article-title: A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning publication-title: Phys. Med. Biol. – volume: 17 start-page: 264 year: 2014 end-page: 276 article-title: Clinical assessment of MR‐guided 3‐Class and 4‐Class attenuation correction in PET/MR publication-title: Mol. Imaging Biol. – volume: 50 start-page: 520 year: 2009 end-page: 526 article-title: Tissue classification as a potential approach for attenuation correction in whole‐body PET/MRI: Evaluation with PET/CT data publication-title: J. Nucl. Med. – volume: 6 start-page: 355 year: 1988 end-page: 368 article-title: Fast field echo imaging: An overview and contrast calculations publication-title: Magn. Reson. Imaging – volume: 12 start-page: 97 year: 2011 end-page: 104 article-title: Comparison of bulk electron density and voxel‐based electron density treatment planning publication-title: J. Appl. Clin. Med. Phys. – volume: 40 start-page: 082301 year: 2013 article-title: Integrated PET/MR imaging: Automatic attenuation correction of flexible RF coils publication-title: Med. Phys. – ident: e_1_2_7_60_1 doi: 10.1118/1.4842575 – ident: e_1_2_7_3_1 doi: 10.1007/s00259‐013‐2660‐z – ident: e_1_2_7_23_1 doi: 10.1007/s00259‐011‐1842‐9 – ident: e_1_2_7_41_1 doi: 10.1088/0031‐9155/45/2/314 – volume: 42 start-page: 636 year: 2001 ident: e_1_2_7_38_1 article-title: comkat: Compartment model kinetic analysis tool publication-title: J. Nucl. Med. – ident: e_1_2_7_17_1 doi: 10.1118/1.3651640 – ident: e_1_2_7_30_1 doi: 10.1002/mrm.21879 – ident: e_1_2_7_11_1 doi: 10.1007/s10334‐012‐0328‐5 – ident: e_1_2_7_13_1 doi: 10.1007/s11307‐014‐0777‐5 – ident: e_1_2_7_5_1 doi: 10.1007/s00259‐013‐2652‐z – ident: e_1_2_7_16_1 doi: 10.1007/s00259‐014‐2751‐5 – ident: e_1_2_7_33_1 – ident: e_1_2_7_58_1 doi: 10.1002/jmri.24680 – ident: e_1_2_7_50_1 doi: 10.1118/1.4873315 – ident: e_1_2_7_10_1 doi: 10.1007/s00259‐012‐2113‐0 – ident: e_1_2_7_6_1 doi: 10.2967/jnumed.111.092577 – ident: e_1_2_7_43_1 doi: 10.3109/0284186X.2013.819119 – ident: e_1_2_7_44_1 doi: 10.1118/1.4711807 – ident: e_1_2_7_9_1 doi: 10.1097/RLI.0b013e318283292f – ident: e_1_2_7_14_1 doi: 10.1016/j.ejrad.2014.03.022 – ident: e_1_2_7_47_1 doi: 10.1186/1748‐717X‐8‐51 – ident: e_1_2_7_25_1 doi: 10.1088/0031‐9155/58/23/8419 – ident: e_1_2_7_42_1 doi: 10.1118/1.4886766 – ident: e_1_2_7_48_1 doi: 10.1016/j.nima.2012.09.005 – ident: e_1_2_7_2_1 article-title: Detection of metastases in breast cancer: Is whole body PET/MR better than PET/CT? publication-title: Journal of Clinical Oncology – ident: e_1_2_7_36_1 doi: 10.1002/mrm.23217 – ident: e_1_2_7_12_1 doi: 10.1007/s10334‐014‐0445‐4 – ident: e_1_2_7_31_1 doi: 10.1007/s10334‐012‐0330‐y – ident: e_1_2_7_19_1 doi: 10.1016/j.radonc.2011.01.012 – ident: e_1_2_7_57_1 doi: 10.1016/0730‐725X(88)90472‐9 – ident: e_1_2_7_40_1 doi: 10.1097/00004424‐198703000‐00005 – ident: e_1_2_7_32_1 doi: 10.1088/0031‐9155/56/10/013 – ident: e_1_2_7_39_1 doi: 10.1109/91.413225 – ident: e_1_2_7_53_1 doi: 10.1118/1.4729716 – ident: e_1_2_7_29_1 doi: 10.1002/mrm.20128 – ident: e_1_2_7_46_1 doi: 10.1088/0031‐9155/59/23/7501 – ident: e_1_2_7_37_1 doi: 10.2967/jnumed.109.064824 – ident: e_1_2_7_28_1 – ident: e_1_2_7_21_1 doi: 10.2967/jnumed.109.069112 – ident: e_1_2_7_51_1 doi: 10.1088/0031‐9155/59/21/6595 – ident: e_1_2_7_20_1 doi: 10.1016/S0167‐8140(02)00440‐1 – ident: e_1_2_7_7_1 doi: 10.2967/jnumed.113.126813 – ident: e_1_2_7_15_1 doi: 10.2967/jnumed.112.113209 – ident: e_1_2_7_49_1 doi: 10.3109/0284186X.2012.692883 – ident: e_1_2_7_52_1 doi: 10.2967/jnumed.110.085076 – ident: e_1_2_7_59_1 doi: 10.1002/jmri.24232 – ident: e_1_2_7_22_1 doi: 10.1118/1.4894709 – ident: e_1_2_7_27_1 doi: 10.1006/jmre.1998.1396 – ident: e_1_2_7_18_1 doi: 10.1120/jacmp.v12i4.3522 – ident: e_1_2_7_26_1 doi: 10.1118/1.3578928 – ident: e_1_2_7_4_1 doi: 10.1007/s00259‐013‐2467‐y – ident: e_1_2_7_24_1 doi: 10.1016/j.ejrad.2011.10.005 – ident: e_1_2_7_56_1 doi: 10.1007/s10334‐012‐0339‐2 – ident: e_1_2_7_55_1 doi: 10.2967/jnumed.108.054726 – ident: e_1_2_7_35_1 doi: 10.1002/mrm.22578 – ident: e_1_2_7_8_1 doi: 10.2967/jnumed.109.065425 – volume: 48 start-page: 471 year: 2007 ident: e_1_2_7_34_1 article-title: Performance of Philips Gemini TF PET/CT scanner with special consideration for its time‐of‐flight imaging capabilities publication-title: J. Nucl. Med. – ident: e_1_2_7_54_1 doi: 10.1118/1.4812685 – ident: e_1_2_7_45_1 doi: 10.1002/mrm.1081 – reference: 22923020 - MAGMA. 2013 Feb;26(1):115-26 – reference: 25321341 - Phys Med Biol. 2014 Nov 7;59(21):6595-606 – reference: 23442772 - Invest Radiol. 2013 May;48(5):323-32 – reference: 24009273 - J Nucl Med. 2013 Oct;54(10 ):1768-74 – reference: 22955943 - MAGMA. 2013 Feb;26(1):127-36 – reference: 21776807 - Med Phys. 2011 May;38(5):2708-14 – reference: 23927344 - Med Phys. 2013 Aug;40(8):082301 – reference: 23497586 - Radiat Oncol. 2013 Mar 06;8:51 – reference: 9615415 - J Magn Reson. 1998 May;132(1):150-3 – reference: 21724984 - J Nucl Med. 2011 Jul;52(7):1142-9 – reference: 24652234 - Eur J Nucl Med Mol Imaging. 2014 Aug;41(8):1574-84 – reference: 24123342 - J Magn Reson Imaging. 2014 Apr;39(4):988-97 – reference: 25086551 - Med Phys. 2014 Aug;41(8):082302 – reference: 24425423 - Eur J Nucl Med Mol Imaging. 2014 Jun;41(6):1176-89 – reference: 10701515 - Phys Med Biol. 2000 Feb;45(2):459-78 – reference: 12648793 - Radiother Oncol. 2003 Feb;66(2):203-16 – reference: 20860006 - Magn Reson Med. 2011 Jan;65(1):96-107 – reference: 22868642 - MAGMA. 2013 Feb;26(1):5-23 – reference: 21508443 - Phys Med Biol. 2011 May 21;56(10):3091-106 – reference: 22078793 - Eur J Radiol. 2012 Oct;81(10):2658-65 – reference: 17332626 - J Nucl Med. 2007 Mar;48(3):471-80 – reference: 20810759 - J Nucl Med. 2010 Sep;51(9):1431-8 – reference: 22712634 - Acta Oncol. 2013 Apr;52(3):612-8 – reference: 11337554 - J Nucl Med. 2001 Apr;42(4):636-45 – reference: 22755711 - Med Phys. 2012 Jun;39(6):3283-90 – reference: 22505568 - J Nucl Med. 2012 May;53(5):796-804 – reference: 22089006 - J Appl Clin Med Phys. 2011 Nov 15;12(4):3522 – reference: 22189904 - Magn Reson Med. 2012 Jul;68(1):120-9 – reference: 22047365 - Med Phys. 2011 Nov;38(11):6010-9 – reference: 3557896 - Invest Radiol. 1987 Mar;22(3):209-15 – reference: 24352789 - Eur J Nucl Med Mol Imaging. 2014 May;41(5):887-97 – reference: 25096328 - Mol Imaging Biol. 2015 Apr;17(2):264-76 – reference: 19289430 - J Nucl Med. 2009 Apr;50(4):520-6 – reference: 24833495 - J Nucl Med. 2014 Jun;55(6):923-31 – reference: 25393873 - Phys Med Biol. 2014 Dec 7;59(23):7501-19 – reference: 24387496 - Med Phys. 2014 Jan;41(1):011704 – reference: 21339009 - Radiother Oncol. 2011 Mar;98 (3):330-4 – reference: 21688050 - Eur J Nucl Med Mol Imaging. 2011 Sep;38(9):1691-701 – reference: 19235919 - Magn Reson Med. 2009 May;61(5):1083-9 – reference: 24780817 - Eur J Radiol. 2014 Jul;83(7):1177-83 – reference: 11283987 - Magn Reson Med. 2001 Apr;45(4):595-604 – reference: 3054380 - Magn Reson Imaging. 1988 Jul-Aug;6(4):355-68 – reference: 20008992 - J Nucl Med. 2010 Jan;51(1):77-84 – reference: 20439508 - J Nucl Med. 2010 May;51(5):812-8 – reference: 23984810 - Acta Oncol. 2013 Oct;52(7):1369-73 – reference: 23817684 - Eur J Nucl Med Mol Imaging. 2013 Oct;40(10 ):1486-99 – reference: 24784377 - Med Phys. 2014 May;41(5):051711 – reference: 15236387 - Magn Reson Med. 2004 Jul;52(1):197-203 – reference: 24965907 - J Magn Reson Imaging. 2015 May;41(5):1465-74 – reference: 22526955 - Eur J Nucl Med Mol Imaging. 2012 Jul;39(7):1154-60 – reference: 24217183 - Phys Med Biol. 2013 Dec 7;58(23 ):8419-35 – reference: 25281971 - Med Phys. 2014 Oct;41(10):102301 – reference: 22830764 - Med Phys. 2012 Jul;39(7):4306-15 |
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| Snippet | Purpose:
MR‐based pseudo‐CT has an important role in MR‐based radiation therapy planning and PET attenuation correction. The purpose of this study is to... MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a... PURPOSEMR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to... Purpose: MR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to... |
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| SubjectTerms | 60 APPLIED LIFE SCIENCES ALGORITHMS Animal or vegetable oils, fats, fatty substances or waxes; Fatty acids therefrom; Detergents; Candles ANIMAL TISSUES biomedical MRI bone BRAIN Brain - anatomy & histology Clinical applications Cluster Analysis clustering Compositions of oils, fats or waxes; Compositions of derivatives thereof Computed tomography Computerised tomographs computerised tomography CORRECTIONS data acquisition Digital computing or data processing equipment or methods, specially adapted for specific applications FATS Feasibility Studies feature extraction FUZZY LOGIC fuzzy set theory Humans image classification Image data processing or generation, in general image enhancement Image enhancement or restoration, e.g. from bit‐mapped to bit‐mapped creating a similar image image matching image reconstruction image sampling Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging Magnetic Resonance Imaging - methods Medical image artifacts medical image processing Medical image reconstruction Methods or arrangements for processing data by operating upon the order or content of the data handled MRI neurophysiology PATIENTS pattern clustering POSITRON COMPUTED TOMOGRAPHY Pulse sequences RADIATION DOSES RADIATION PROTECTION AND DOSIMETRY Radiation Therapy Physics RADIOTHERAPY Reconstruction SKULL Skull - anatomy & histology Spatial resolution spin‐spin relaxation Tissues Tomography - methods undersampling UTE water |
| Title | Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1118%2F1.4926756 https://www.ncbi.nlm.nih.gov/pubmed/26233223 https://www.proquest.com/docview/1701300807 https://www.osti.gov/biblio/22581339 https://pubmed.ncbi.nlm.nih.gov/PMC5148184 |
| Volume | 42 |
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