Achieving robust labeling above the circle of Willis with vessel‐encoded arterial spin labeling
Purpose To improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain. Methods Our proposed improved optimized encoding...
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Published in | Magnetic resonance in medicine Vol. 94; no. 4; pp. 1415 - 1431 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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01.10.2025
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Abstract | Purpose
To improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain.
Methods
Our proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR‐efficient encodings than previous approaches. Pseudo‐continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients.
Results
In simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients.
Conclusions
Combining IOES with optimized PCASL parameters, the vessel‐decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation. |
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AbstractList | Purpose
To improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain.
Methods
Our proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR‐efficient encodings than previous approaches. Pseudo‐continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients.
Results
In simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients.
Conclusions
Combining IOES with optimized PCASL parameters, the vessel‐decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation. To improve the robustness of noninvasive vessel-selective perfusion imaging and angiography using vessel-encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain.PURPOSETo improve the robustness of noninvasive vessel-selective perfusion imaging and angiography using vessel-encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain.Our proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR-efficient encodings than previous approaches. Pseudo-continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients.METHODSOur proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR-efficient encodings than previous approaches. Pseudo-continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients.In simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients.RESULTSIn simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients.Combining IOES with optimized PCASL parameters, the vessel-decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation.CONCLUSIONSCombining IOES with optimized PCASL parameters, the vessel-decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation. To improve the robustness of noninvasive vessel-selective perfusion imaging and angiography using vessel-encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain. Our proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR-efficient encodings than previous approaches. Pseudo-continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients. In simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients. Combining IOES with optimized PCASL parameters, the vessel-decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation. Purpose To improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when applied to complex vascular geometries, such as above the circle of Willis (CoW) in the brain. Methods Our proposed improved optimized encoding scheme (IOES) better accounts for vascular geometry and the VEASL encoding process, leading to more SNR‐efficient encodings than previous approaches. Pseudo‐continuous arterial spin labeling (PCASL) parameters were optimized for a thinner labeling region, allowing tortuous vessels to be more accurately treated as single points within the labeling plane. Our optimized approach was compared to the original OES method above the CoW in healthy volunteers, with preliminary application in two Moyamoya patients. Results In simulation, the IOES improved SNR efficiency by approximately 10% and used longer wavelength encodings that are less sensitive to subject motion. The effective labeling thickness was reduced using optimized PCASL parameters, which maintained high labeling efficiency. In healthy volunteers, these improvements allowed for the separation of at least nine arteries and their downstream tissues, with more accurate vessel decoding and closer alignment between the measured VEASL signal modulation and the encoding design. Vascular territories consistent with angiography were found in the Moyamoya patients. Conclusions Combining IOES with optimized PCASL parameters, the vessel‐decoding efficacy in a region with complex vascular geometry above the CoW was improved. The automated encoding design process and scan times under 6 min make it feasible to observe flow patterns above the CoW in clinical settings, particularly for studies of collateral circulation. |
Author | Chen, Zhensen Wang, Jian Ji, Yang Wang, He Liao, Yujun Suzuki, Yuriko Okell, Thomas W. Li, Hongwei Qian, Tiansheng Woods, Joseph G. |
AuthorAffiliation | 2 Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences University of Oxford Oxford UK 5 Department of Neurosurgery Fudan University Huashan Hospital Shanghai People's Republic of China 3 Department of Electronic Engineering and Information Science, School of Information Science and Technology University of Science and Technology of China Hefei People's Republic of China 6 Clinical Medical Center of Neurosurgery Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration Shanghai People's Republic of China 4 MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence Fudan University Shanghai People's Republic of China 1 Institute of Science and Technology for Brain‐inspired Intelligence Fudan University Shanghai People's Republic of China 7 Department of Neurosurgery, The Second People's Hospital of Changzhou The Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center C |
AuthorAffiliation_xml | – name: 1 Institute of Science and Technology for Brain‐inspired Intelligence Fudan University Shanghai People's Republic of China – name: 7 Department of Neurosurgery, The Second People's Hospital of Changzhou The Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center Changzhou People's Republic of China – name: 2 Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences University of Oxford Oxford UK – name: 5 Department of Neurosurgery Fudan University Huashan Hospital Shanghai People's Republic of China – name: 6 Clinical Medical Center of Neurosurgery Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration Shanghai People's Republic of China – name: 4 MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence Fudan University Shanghai People's Republic of China – name: 3 Department of Electronic Engineering and Information Science, School of Information Science and Technology University of Science and Technology of China Hefei People's Republic of China |
Author_xml | – sequence: 1 givenname: Hongwei orcidid: 0000-0003-1591-7674 surname: Li fullname: Li, Hongwei organization: University of Oxford – sequence: 2 givenname: Yang orcidid: 0000-0003-4134-374X surname: Ji fullname: Ji, Yang email: jiyang@ustc.edu.cn organization: University of Science and Technology of China – sequence: 3 givenname: He surname: Wang fullname: Wang, He organization: Fudan University – sequence: 4 givenname: Zhensen surname: Chen fullname: Chen, Zhensen organization: Fudan University – sequence: 5 givenname: Tiansheng surname: Qian fullname: Qian, Tiansheng organization: Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration – sequence: 6 givenname: Yujun surname: Liao fullname: Liao, Yujun organization: Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration – sequence: 7 givenname: Jian surname: Wang fullname: Wang, Jian organization: The Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center – sequence: 8 givenname: Joseph G. orcidid: 0000-0002-0329-824X surname: Woods fullname: Woods, Joseph G. organization: University of Oxford – sequence: 9 givenname: Yuriko orcidid: 0000-0002-4851-7872 surname: Suzuki fullname: Suzuki, Yuriko organization: University of Oxford – sequence: 10 givenname: Thomas W. orcidid: 0000-0001-8258-0659 surname: Okell fullname: Okell, Thomas W. organization: University of Oxford |
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Keywords | encoding scheme vessel‐selective vascular territory SNR efficiency vessel‐encoded arterial spin labeling (VEASL) |
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To improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when... To improve the robustness of noninvasive vessel-selective perfusion imaging and angiography using vessel-encoded arterial spin labeling (VEASL) when applied to... Purpose To improve the robustness of noninvasive vessel‐selective perfusion imaging and angiography using vessel‐encoded arterial spin labeling (VEASL) when... |
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SubjectTerms | Adult Algorithms Angiography Arteries Blood vessels Brain - blood supply Brain - diagnostic imaging Cerebrovascular Circulation Circle of Willis - diagnostic imaging Coding Computer Simulation Effectiveness encoding scheme Female Flow distribution Healthy Volunteers Humans Image Processing, Computer-Assisted - methods Imaging Methodology Labeling Magnetic Resonance Angiography - methods Male Medical imaging Moyamoya Disease - diagnostic imaging Neuroimaging Parameters Signal-To-Noise Ratio SNR efficiency Spin labeling Spin Labels vascular territory vessel‐encoded arterial spin labeling (VEASL) vessel‐selective |
Title | Achieving robust labeling above the circle of Willis with vessel‐encoded arterial spin labeling |
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