Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging
PurposeTo develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging.MethodsThe proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility We...
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2020.581474/full |
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| author | Sara Gharabaghi Sara Gharabaghi Saifeng Liu Ying Wang Ying Wang Yongsheng Chen Sagar Buch Mojtaba Jokar Thomas Wischgoll Nasser H. Kashou Chunyan Zhang Bo Wu Jingliang Cheng E. Mark Haacke E. Mark Haacke E. Mark Haacke E. Mark Haacke E. Mark Haacke |
| author_facet | Sara Gharabaghi Sara Gharabaghi Saifeng Liu Ying Wang Ying Wang Yongsheng Chen Sagar Buch Mojtaba Jokar Thomas Wischgoll Nasser H. Kashou Chunyan Zhang Bo Wu Jingliang Cheng E. Mark Haacke E. Mark Haacke E. Mark Haacke E. Mark Haacke E. Mark Haacke |
| author_sort | Sara Gharabaghi |
| collection | DOAJ |
| description | PurposeTo develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging.MethodsThe proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects.ResultsThe unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature.ConclusionThis study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts. |
| first_indexed | 2024-12-23T05:15:51Z |
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| last_indexed | 2024-12-23T05:15:51Z |
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| spelling | doaj.art-c117d9960d77498f8f0810817b2445fc2022-12-21T17:58:49ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-10-011410.3389/fnins.2020.581474581474Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) ImagingSara Gharabaghi0Sara Gharabaghi1Saifeng Liu2Ying Wang3Ying Wang4Yongsheng Chen5Sagar Buch6Mojtaba Jokar7Thomas Wischgoll8Nasser H. Kashou9Chunyan Zhang10Bo Wu11Jingliang Cheng12E. Mark Haacke13E. Mark Haacke14E. Mark Haacke15E. Mark Haacke16E. Mark Haacke17Department of Computer Science and Engineering, Wright State University, Dayton, OH, United StatesMagnetic Resonance Innovations, Inc., Bingham Farms, MI, United StatesThe MRI Institute for Biomedical Research, Bingham Farms, MI, United StatesThe MRI Institute for Biomedical Research, Bingham Farms, MI, United StatesDepartment of Radiology, Wayne State University, Detroit, MI, United StatesDepartment of Neurology, Wayne State University, Detroit, MI, United StatesDepartment of Radiology, Wayne State University, Detroit, MI, United StatesMagnetic Resonance Innovations, Inc., Bingham Farms, MI, United StatesDepartment of Computer Science and Engineering, Wright State University, Dayton, OH, United StatesDepartment of Biomedical, Industrial and Human Factors Engineering, Wright State University, Dayton, OH, United StatesDepartment of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaShanghai Zhu Yan Medical Technology Ltd., Shanghai, ChinaDepartment of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, ChinaMagnetic Resonance Innovations, Inc., Bingham Farms, MI, United StatesThe MRI Institute for Biomedical Research, Bingham Farms, MI, United StatesDepartment of Radiology, Wayne State University, Detroit, MI, United StatesDepartment of Neurology, Wayne State University, Detroit, MI, United StatesDepartment of Biomedical Engineering, Wayne State University, Detroit, MI, United StatesPurposeTo develop a method to reconstruct quantitative susceptibility mapping (QSM) from multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging.MethodsThe proposed QSM reconstruction algorithm, referred to as “structurally constrained Susceptibility Weighted Imaging and Mapping” scSWIM, performs an ℓ1 and ℓ2 regularization-based reconstruction in a single step. The unique contrast of the T1 weighted enhanced (T1WE) image derived from STAGE imaging was used to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. The multi-echo multi-flip angle data were used for improving the contrast-to-noise ratio in QSM through a weighted averaging scheme. The measured susceptibility values from scSWIM for both simulated and in vivo data were compared to the: original susceptibility model (for simulated data only), the multi orientation COSMOS (for in vivo data only), truncated k-space division (TKD), iterative susceptibility weighted imaging and mapping (iSWIM), and morphology enabled dipole inversion (MEDI) algorithms. Goodness of fit was quantified by measuring the root mean squared error (RMSE) and structural similarity index (SSIM). Additionally, scSWIM was assessed in ten healthy subjects.ResultsThe unique contrast and tissue boundaries from T1WE and iSWIM enable the accurate definition of edges of high susceptibility regions. For the simulated brain model without the addition of microbleeds and calcium, the RMSE was best at 5.21ppb for scSWIM and 8.74ppb for MEDI thanks to the reduced streaking artifacts. However, by adding the microbleeds and calcium, MEDI’s performance dropped to 47.53ppb while scSWIM performance remained the same. The SSIM was highest for scSWIM (0.90) and then MEDI (0.80). The deviation from the expected susceptibility in deep gray matter structures for simulated data relative to the model (and for the in vivo data relative to COSMOS) as measured by the slope was lowest for scSWIM + 1%(−1%); MEDI + 2%(−11%) and then iSWIM −5%(−10%). Finally, scSWIM measurements in the basal ganglia of healthy subjects were in agreement with literature.ConclusionThis study shows that using a data fidelity term and structural constraints results in reduced noise and streaking artifacts while preserving structural details. Furthermore, the use of STAGE imaging with multi-echo and multi-flip data helps to improve the signal-to-noise ratio in QSM data and yields less artifacts.https://www.frontiersin.org/articles/10.3389/fnins.2020.581474/fullquantitative susceptibility mapping (QSM)constrained image reconstructiongradient recalled echo (GRE) phase dataill-posed inverse problemstrategically acquired gradient echo (STAGE) imaging |
| spellingShingle | Sara Gharabaghi Sara Gharabaghi Saifeng Liu Ying Wang Ying Wang Yongsheng Chen Sagar Buch Mojtaba Jokar Thomas Wischgoll Nasser H. Kashou Chunyan Zhang Bo Wu Jingliang Cheng E. Mark Haacke E. Mark Haacke E. Mark Haacke E. Mark Haacke E. Mark Haacke Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging Frontiers in Neuroscience quantitative susceptibility mapping (QSM) constrained image reconstruction gradient recalled echo (GRE) phase data ill-posed inverse problem strategically acquired gradient echo (STAGE) imaging |
| title | Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging |
| title_full | Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging |
| title_fullStr | Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging |
| title_full_unstemmed | Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging |
| title_short | Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging |
| title_sort | multi echo quantitative susceptibility mapping for strategically acquired gradient echo stage imaging |
| topic | quantitative susceptibility mapping (QSM) constrained image reconstruction gradient recalled echo (GRE) phase data ill-posed inverse problem strategically acquired gradient echo (STAGE) imaging |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2020.581474/full |
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