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Ferdian E, Marlevi D, Schollenberger J, Aristova M, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA, Young AA. Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure. Med Image Anal 2023; 88:102831. [PMID: 37244143 DOI: 10.1016/j.media.2023.102831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/04/2023] [Accepted: 04/20/2023] [Indexed: 05/29/2023]
Abstract
The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive full-field mapping of cerebrovascular hemodynamics. However, estimations are complicated by the narrow and tortuous intracranial vasculature, with accurate image-based quantification directly dependent on sufficient spatial resolution. Further, extended scan times are required for high-resolution acquisitions, and most clinical acquisitions are performed at comparably low resolution (>1 mm) where biases have been observed with regard to the quantification of both flow and relative pressure. The aim of our study was to develop an approach for quantitative intracranial super-resolution 4D Flow MRI, with effective resolution enhancement achieved by a dedicated deep residual network, and with accurate quantification of functional relative pressures achieved by subsequent physics-informed image processing. To achieve this, our two-step approach was trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 15.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s, and cosine similarity: 0.99 ± 0.06 at peak velocity) and flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.56 mL/s at peak flow), and with the coupled physics-informed image analysis allowing for maintained recovery of functional relative pressure throughout the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the quantitative super-resolution approach is applied to an in-vivo volunteer cohort, effectively generating intracranial flow images at <0.5 mm resolution and showing reduced low-resolution bias in relative pressure estimation. Our work thus presents a promising two-step approach to non-invasively quantify cerebrovascular hemodynamics, being applicable to dedicated clinical cohorts in the future.
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Affiliation(s)
- E Ferdian
- University of Auckland, Auckland 1142 New Zealand
| | - D Marlevi
- Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | | | - M Aristova
- Northwestern University, Chicago, IL 60611, USA
| | - E R Edelman
- Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - S Schnell
- Northwestern University, Chicago, IL 60611, USA; University of Greifswald, Greifswald 17489, Germany
| | - C A Figueroa
- University of Michigan, Ann Arbor, MI 48109, USA
| | - D A Nordsletten
- University of Michigan, Ann Arbor, MI 48109, USA; King's College London, London, SE1 7EH, UK
| | - A A Young
- University of Auckland, Auckland 1142 New Zealand; King's College London, London, SE1 7EH, UK
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Schollenberger J, Braet DJ, Hernandez-Garcia L, Osborne NH, Figueroa CA. A magnetic resonance imaging-based computational analysis of cerebral hemodynamics in patients with carotid artery stenosis. Quant Imaging Med Surg 2023; 13:1126-1137. [PMID: 36819242 PMCID: PMC9929419 DOI: 10.21037/qims-22-565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 11/28/2022] [Indexed: 01/09/2023]
Abstract
Management of asymptomatic carotid artery stenosis (CAS) relies on measuring the percentage of stenosis. The aim of this study was to investigate the impact of CAS on cerebral hemodynamics using magnetic resonance imaging (MRI)-informed computational fluid dynamics (CFD) and to provide novel hemodynamic metrics that may improve the understanding of stroke risk. CFD analysis was performed in two patients with similar degrees of asymptomatic high-grade CAS. Three-dimensional anatomical-based computational models of cervical and cerebral blood flow were constructed and calibrated patient-specifically using phase-contrast MRI flow and arterial spin labeling perfusion data. Differences in cerebral hemodynamics were assessed in preoperative and postoperative models. Preoperatively, patient 1 demonstrated large flow and pressure reductions in the stenosed internal carotid artery, while patient 2 demonstrated only minor reductions. Patient 1 exhibited a large amount of flow compensation between hemispheres (80.31%), whereas patient 2 exhibited only a small amount of collateral flow (20.05%). There were significant differences in the mean pressure gradient over the stenosis between patients preoperatively (26.3 vs. 1.8 mmHg). Carotid endarterectomy resulted in only minor hemodynamic changes in patient 2. MRI-informed CFD analysis of two patients with similar clinical classifications of stenosis revealed significant differences in hemodynamics which were not apparent from anatomical assessment alone. Moreover, revascularization of CAS might not always result in hemodynamic improvements. Further studies are needed to investigate the clinical impact of hemodynamic differences and how they pertain to stroke risk and clinical management.
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Affiliation(s)
- Jonas Schollenberger
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Drew J. Braet
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Luis Hernandez-Garcia
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA;,Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, USA
| | | | - C. Alberto Figueroa
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA;,Department of Surgery, University of Michigan, Ann Arbor, MI, USA
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Hernandez‐Garcia L, Aramendía‐Vidaurreta V, Bolar DS, Dai W, Fernández‐Seara MA, Guo J, Madhuranthakam AJ, Mutsaerts H, Petr J, Qin Q, Schollenberger J, Suzuki Y, Taso M, Thomas DL, van Osch MJP, Woods J, Zhao MY, Yan L, Wang Z, Zhao L, Okell TW. Recent Technical Developments in ASL: A Review of the State of the Art. Magn Reson Med 2022; 88:2021-2042. [PMID: 35983963 PMCID: PMC9420802 DOI: 10.1002/mrm.29381] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/31/2022] [Accepted: 06/18/2022] [Indexed: 12/11/2022]
Abstract
This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of nonperfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultrahigh field ASL. We aim to provide a high level of understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics outside the scope of this article that are reviewed at length in separate articles include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations.
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Affiliation(s)
| | | | - Divya S. Bolar
- Center for Functional Magnetic Resonance Imaging, Department of RadiologyUniversity of California at San DiegoSan DiegoCaliforniaUSA
| | - Weiying Dai
- Department of Computer ScienceState University of New York at BinghamtonBinghamtonNYUSA
| | | | - Jia Guo
- Department of BioengineeringUniversity of California RiversideRiversideCaliforniaUSA
| | | | - Henk Mutsaerts
- Department of Radiology & Nuclear MedicineAmsterdam University Medical Center, Amsterdam NeuroscienceAmsterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins UniversityBaltimoreMarylandUSA
| | | | - Yuriko Suzuki
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Manuel Taso
- Division of MRI research, RadiologyBeth Israel Deaconess Medical Center and Harvard Medical SchoolBostonMassachusettsUSA
| | - David L. Thomas
- Department of Brain Repair and RehabilitationUCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Matthias J. P. van Osch
- C.J. Gorter Center for high field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Joseph Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Department of RadiologyUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Lirong Yan
- Department of Radiology, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument ScienceZhejiang UniversityZhejiangPeople's Republic of China
| | - Thomas W. Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
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Using a Human Circulation Mathematical Model to Simulate the Effects of Hemodialysis and Therapeutic Hypothermia. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app12010307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background: We developed a hemodynamic mathematical model of human circulation coupled to a virtual hemodialyzer. The model was used to explore mechanisms underlying our clinical observations involving hemodialysis. Methods: The model consists of whole body human circulation, baroreflex feedback control, and a hemodialyzer. Four model populations encompassing baseline, dialysed, therapeutic hypothermia treated, and simultaneous dialysed with hypothermia were generated. In all populations atrial fibrillation and renal failure as co-morbidities, and exercise as a treatment were simulated. Clinically relevant measurables were used to quantify the effects of each in silico experiment. Sensitivity analysis was used to uncover the most relevant parameters. Results: Relative to baseline, the modelled dialysis increased the population mean diastolic blood pressure by 5%, large vessel wall shear stress by 6%, and heart rate by 20%. Therapeutic hypothermia increased systolic blood pressure by 3%, reduced large vessel shear stress by 15%, and did not affect heart rate. Therapeutic hypothermia reduced wall shear stress by 15% in the aorta and 6% in the kidneys, suggesting a potential anti-inflammatory benefit. Therapeutic hypothermia reduced cardiac output under atrial fibrillation by 12% and under renal failure by 20%. Therapeutic hypothermia and exercise did not affect dialyser function, but increased water removal by approximately 40%. Conclusions: This study illuminates some mechanisms of the action of therapeutic hypothermia. It also suggests clinical measurables that may be used as surrogates to diagnose underlying diseases such as atrial fibrillation.
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Marlevi D, Schollenberger J, Aristova M, Ferdian E, Ma Y, Young AA, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA. Noninvasive quantification of cerebrovascular pressure changes using 4D Flow MRI. Magn Reson Med 2021; 86:3096-3110. [PMID: 34431550 DOI: 10.1002/mrm.28928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 05/24/2021] [Accepted: 06/25/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE Hemodynamic alterations are indicative of cerebrovascular disease. However, the narrow and tortuous cerebrovasculature complicates image-based assessment, especially when quantifying relative pressure. Here, we present a systematic evaluation of image-based cerebrovascular relative pressure mapping, investigating the accuracy of the routinely used reduced Bernoulli (RB), the extended unsteady Bernoulli (UB), and the full-field virtual work-energy relative pressure ( ν WERP) method. METHODS Patient-specific in silico models were used to generate synthetic cerebrovascular 4D Flow MRI, with RB, UB, and ν WERP performance quantified as a function of spatiotemporal sampling and image noise. Cerebrovascular relative pressures were also derived in 4D Flow MRI from healthy volunteers ( n = 8 ), acquired at two spatial resolutions (dx = 1.1 and 0.8 mm). RESULTS The in silico analysis indicate that accurate relative pressure estimations are inherently coupled to spatial sampling: at dx = 1.0 mm high errors are reported for all methods; at dx = 0.5 mm ν WERP recovers relative pressures at a mean error of 0.02 ± 0.25 mm Hg, while errors remain higher for RB and UB (mean error of -2.18 ± 1.91 and -2.18 ± 1.87 mm Hg, respectively). The dependence on spatial sampling is also indicated in vivo, albeit with higher correlative dependence between resolutions using ν WERP (k = 0.64, R2 = 0.81 for dx = 1.1 vs. 0.8 mm) than with RB or UB (k = 0.04, R2 = 0.03, and k = 0.07, R2 = 0.07, respectively). CONCLUSION Image-based full-field methods such as ν WERP enable cerebrovascular relative pressure mapping; however, accuracy is directly dependent on utilized spatial resolution.
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Affiliation(s)
- David Marlevi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonas Schollenberger
- Department of Surgery and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Maria Aristova
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Edward Ferdian
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - Yue Ma
- Department of Radiology, Northwestern University, Chicago, IL, USA
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Alistair A Young
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- School of Biomedical Engineering and Imaging Sciences, The Rayne Institute, King's College London, London, UK
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Susanne Schnell
- Department of Radiology, Northwestern University, Chicago, IL, USA
- Department of Medical Physics, Institute of Physics, University of Greifswald, Greifswald, Germany
| | - C Alberto Figueroa
- Department of Surgery and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - David A Nordsletten
- Department of Surgery and Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- School of Biomedical Engineering and Imaging Sciences, The Rayne Institute, King's College London, London, UK
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Schollenberger J, Osborne NH, Hernandez-Garcia L, Figueroa CA. A Combined Computational Fluid Dynamics and Arterial Spin Labeling MRI Modeling Strategy to Quantify Patient-Specific Cerebral Hemodynamics in Cerebrovascular Occlusive Disease. Front Bioeng Biotechnol 2021; 9:722445. [PMID: 34485260 PMCID: PMC8416094 DOI: 10.3389/fbioe.2021.722445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 07/19/2021] [Indexed: 11/13/2022] Open
Abstract
Cerebral hemodynamics in the presence of cerebrovascular occlusive disease (CVOD) are influenced by the anatomy of the intracranial arteries, the degree of stenosis, the patency of collateral pathways, and the condition of the cerebral microvasculature. Accurate characterization of cerebral hemodynamics is a challenging problem. In this work, we present a strategy to quantify cerebral hemodynamics using computational fluid dynamics (CFD) in combination with arterial spin labeling MRI (ASL). First, we calibrated patient-specific CFD outflow boundary conditions using ASL-derived flow splits in the Circle of Willis. Following, we validated the calibrated CFD model by evaluating the fractional blood supply from the main neck arteries to the vascular territories using Lagrangian particle tracking and comparing the results against vessel-selective ASL (VS-ASL). Finally, the feasibility and capability of our proposed method were demonstrated in two patients with CVOD and a healthy control subject. We showed that the calibrated CFD model accurately reproduced the fractional blood supply to the vascular territories, as obtained from VS-ASL. The two patients revealed significant differences in pressure drop over the stenosis, collateral flow, and resistance of the distal vasculature, despite similar degrees of clinical stenosis severity. Our results demonstrated the advantages of a patient-specific CFD analysis for assessing the hemodynamic impact of stenosis.
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Affiliation(s)
- Jonas Schollenberger
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Nicholas H Osborne
- Department of Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Luis Hernandez-Garcia
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.,Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States
| | - C Alberto Figueroa
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.,Department of Surgery, University of Michigan, Ann Arbor, MI, United States
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7
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Yuan J, Qu J, Lv Z, Wu C, Zhang D, Liu X, Yang B, Liu Y. Assessment of blood supply of the external carotid artery in moyamoya disease using super-selective pseudo-continuous arterial spin labeling technique. Eur Radiol 2021; 31:9287-9295. [PMID: 34021389 DOI: 10.1007/s00330-021-07893-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/23/2021] [Accepted: 03/15/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To evaluate the diagnostic accuracy of super-selective pseudo-continuous arterial spin labeling (ss-pCASL) at depicting external carotid artery (ECA) perfusion territory in moyamoya disease (MMD). METHODS In total, 103 patients with MMD who underwent both ss-pCASL and digital subtraction angiography (DSA, the reference standard) were included. There were 3, 184, and 19 normal, preoperative, and postoperative MMD hemispheres, respectively. The ss-pCASL results were interpreted by two different visual inspection criteria: presence or absence and definite or indefinite ECA perfusion territory. The performance of ss-pCASL at depiction of ECA perfusion territory was compared to that of DSA. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. The κ statistic was used to assess intermodality and inter-reader agreement. RESULTS When interpreted as presence or absence, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of ss-pCASL for depicting ECA perfusion territory were 78.3 %, 79.6 %, 92.5 %, 53.4 %, and 78.6 %, respectively, and the intermodality and inter-reader agreement were κ = 0.49 (CI: 0.43 - 0.55, p < 0.01) and 0.71 (CI: 0.66 - 0.76, p < 0.01), respectively. When interpreted as definite or indefinite, the respective values were 61.1%, 100%, 100%, 44.5%, 70.4%, κ = 0.42 (CI: 0.37 - 0.47, p < 0.01), and 0.90 (CI: 0.87 - 0.93, p < 0.01). CONCLUSION ss-pCASL has substantial sensitivity and specificity compared with DSA for depicting the presence versus absence of ECA perfusion territory in MMD. As a noninvasive method in which no ion radiation or contrast medium is needed, ss-pCASL may potentially reduce the need for repeated DSA examination. KEY POINTS • Super-selective pseudo-continuous arterial spin labeling (ss-pCASL) is a noninvasive vessel-selective MR technique to demonstrate perfusion territory of a single cerebral artery. • Compared with digital subtraction angiography, ss-pCASL has substantial sensitivity and specificity for depicting the perfusion territory of the external carotid artery in brain parenchyma in moyamoya disease. • ss-pCASL may potentially reduce the need for repeated DSA examination.
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Affiliation(s)
- Jing Yuan
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | | | - Zheng Lv
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | - Chunxue Wu
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China
| | - Dong Zhang
- Neurosurgery Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xingju Liu
- Neurosurgery Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bao Yang
- Neurosurgery Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Radiology Department, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China.
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