Quantification of hemodynamics of cerebral arteriovenous malformations after stereotactic radiosurgery using <scp>4D</scp> flow <scp>magnetic resonance imaging</scp>

Srinivas, Shanmukha; Retson, Tara; Simon, Aaron B.; Hattangadi‐Gluth, Jona A.; Hsiao, Albert; Farid, Nikdokht

doi:10.1002/jmri.27490

Cited by 5 publications

(7 citation statements)

References 42 publications

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“…Srinivas et al compared hemodynamic and morphological changes in AVMs after SRS by using MRI, including time-resolved 3D flow. 30 Their results showed that the hemodynamic changes were significantly different before and after SRS, even though some morphological features, such as the diameter of the feeding artery and the AVM volume, were not significantly different before and after SRS. They mentioned the possibility that hemodynamic changes in AVMs precede morphological changes.…”

Section: Discussionmentioning

confidence: 95%

Hemodynamic changes during the obliteration process for cerebral arteriovenous malformations after radiosurgery

Takeda¹,

Hasegawa²,

Kin³

et al. 2022

Neurosurgical Focus

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OBJECTIVE The process of cerebral arteriovenous malformation (AVM) obliteration following radiosurgery is poorly understood. Authors of this retrospective study aimed to assess the changes in AVM hemodynamics after stereotactic radiosurgery (SRS) by using 3D flow magnetic resonance imaging (MRI) to elucidate the process of AVM obliteration. METHODS Twenty-four patients with AVMs treated with SRS between July 2015 and December 2017 were included in this study and classified into two groups depending on the duration of AVM obliteration: group A, obliteration within 3 years (n = 15); and group B, obliteration taking more than 3 years or no obliteration (n = 9). Blood flow (ml/min) in the largest feeding artery was measured before and after SRS by using time-averaged 3D flow MRI. The decreasing rate of blood flow in the feeding artery after SRS was calculated as the percent change from baseline blood flow. A Wilcoxon rank-sum test was used to compare the decreasing blood flow rate between the two groups at 4 and 12 months after SRS. RESULTS For the entire cohort, the mean decrease in blood flow in the feeding artery from baseline was 29% at 4 months and 71% at 12 months after SRS. In general, blood flow after SRS decreased faster in group A and slower in group B. The decreasing rates in blood flow at 4 and 12 months after SRS were significantly different between the two groups (p = 0.02 and < 0.001, respectively). CONCLUSIONS Tracking changes in AVM hemodynamics after SRS may be useful for assessing the progress of AVM obliteration and the therapeutic effects of SRS, possibly contributing to the prediction of subsequent obliteration outcome.

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Section: Discussionmentioning

confidence: 95%

Hemodynamic changes during the obliteration process for cerebral arteriovenous malformations after radiosurgery

Takeda¹,

Hasegawa²,

Kin³

et al. 2022

Neurosurgical Focus

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“…Clinical implementation of 4D Flow MRI has become more feasible with the productization of the pulse sequence, and new indications for its use in the neurovasculature are beginning to emerge. This is now part of the clinical routine at our institution for monitoring treatment response of AVMs after radiosurgery and identifying patients at risk for stroke [5,32].…”

Section: Discussionmentioning

confidence: 99%

“…4D Flow MRI has become increasingly valuable for diagnosis and clinical management in patients with cardiovascular disease [1], but has not yet been widely adopted for management of cerebrovascular disease. Recent studies have shown that measurements of blood flow from 4D Flow are similarly accurate and reproducible in the cerebral vasculature [2], and multiples studies have been undertaken to explore its application to diagnosis, prognostication, and monitoring treatment response of AVMs, carotid artery disease, and cerebral aneurysms [3][4][5]. However, a major limiting factor for clinical use of 4D Flow is the need for post-processing correction of background phase error.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Phase Error Correction for Cerebrovascular 4D Flow MRI

Srini

Masutani

Norbash

et al. 2022

Preprint

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Background and Purpose Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. Methods With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland-Altman, Wilcoxon-signed rank (WSR) and F-tests. Results Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833–0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945–0.981) and decreased variance (p < 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Conclusions Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction.

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“…4D Flow MRI has become increasingly valuable for diagnosis and clinical management in patients with cardiovascular disease 1 , but has not yet been widely adopted for management of cerebrovascular disease. Recent studies have shown that measurements of blood flow from 4D Flow are similarly accurate and reproducible in the cerebral vasculature 2 , and multiples studies have been undertaken to explore its application to diagnosis, prognostication, and monitoring treatment response of AVMs, carotid artery disease, and cerebral aneurysms 3 – 5 . However, a major limiting factor for clinical use of 4D Flow is the need for post-processing correction of background phase error.…”

Section: Introductionmentioning

confidence: 99%

Deep learning phase error correction for cerebrovascular 4D flow MRI

Srinivas

Masutani

Norbash

et al. 2023

Sci Rep

Self Cite

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Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the potential of a convolutional neural network (CNN), a form of deep learning, to directly infer the correction vector field. With IRB waiver of informed consent, we retrospectively identified 96 MRI exams from 48 patients who underwent cerebrovascular 4D Flow MRI from October 2015 to 2020. Flow measurements of the anterior, posterior, and venous circulation were performed to assess inflow-outflow error and the benefit of manual image-based phase error correction. A CNN was then trained to directly infer the phase-error correction field, without segmentation, from 4D Flow volumes to automate correction, reserving from 23 exams for testing. Statistical analyses included Spearman correlation, Bland–Altman, Wilcoxon-signed rank (WSR) and F-tests. Prior to correction, there was strong correlation between inflow and outflow (ρ = 0.833–0.947) measurements with the largest discrepancy in the venous circulation. Manual phase error correction improved inflow-outflow correlation (ρ = 0.945–0.981) and decreased variance (p < 0.001, F-test). Fully automated CNN correction was non-inferior to manual correction with no significant differences in correlation (ρ = 0.971 vs ρ = 0.982) or bias (p = 0.82, Wilcoxon-Signed Rank test) of inflow and outflow measurements. Residual background phase error can impair inflow-outflow consistency of cerebrovascular flow volume measurements. A CNN can be used to directly infer the phase-error vector field to fully automate phase error correction.

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Quantification of hemodynamics of cerebral arteriovenous malformations after stereotactic radiosurgery using 4D flow magnetic resonance imaging

Cited by 5 publications

References 42 publications

Hemodynamic changes during the obliteration process for cerebral arteriovenous malformations after radiosurgery

Hemodynamic changes during the obliteration process for cerebral arteriovenous malformations after radiosurgery

Deep Learning Phase Error Correction for Cerebrovascular 4D Flow MRI

Deep learning phase error correction for cerebrovascular 4D flow MRI

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