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Martins N, Aires A, Mendez B, Boned S, Rubiera M, Tomasello A, Coscojuela P, Hernandez D, Muchada M, Rodríguez-Luna D, Rodríguez N, Juega JM, Pagola J, Molina CA, Ribó M. Ghost Infarct Core and Admission Computed Tomography Perfusion: Redefining the Role of Neuroimaging in Acute Ischemic Stroke. INTERVENTIONAL NEUROLOGY 2018; 7:513-521. [PMID: 30410531 DOI: 10.1159/000490117] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 04/22/2018] [Indexed: 12/13/2022]
Abstract
Background Determining the size of infarct extent is crucial to elect patients for reperfusion therapies. Computed tomography perfusion (CTP) based on cerebral blood volume may overestimate infarct core on admission and consequently include ghost infarct core (GIC) in a definitive lesional area. Purpose Our goal was to confirm and better characterize the GIC phenomenon using CTP cerebral blood flow (CBF) as the reference parameter to determine infarct core. Methods We performed a retrospective, single-center analysis of consecutive thrombectomies of middle cerebral or intracranial internal carotid artery occlusions considering noncontrast CT Alberta Stroke Program Early CT Score ≥6 in patients with pretreatment CTP. We used the RAPID® software to measure admission infarct core based on initial CBF. The final infarct was extracted from follow-up CT. GIC was defined as initial core minus final infarct > 10 mL. Results A total of 123 patients were included. The median National Institutes of Health Stroke Scale score was 18 (13-20), the median time from symptoms to CTP was 188 (67-288) min, and the recanalization rate (Thrombolysis in Cerebral Infarction score 2b, 2c, or 3) was 83%. Twenty patients (16%) presented with GIC. GIC was associated with shorter time to recanalization (150 [105-291] vs. 255 [163-367] min, p = 0.05) and larger initial CBF core volume (38 [26-59] vs. 6 [0-27] mL, p < 0.001). An adjusted logistic regression model identified time to recanalization < 302 min (OR 4.598, 95% CI 1.143-18.495, p = 0.032) and initial infarct volume (OR 1.01, 95% CI 1.001-1.019, p = 0.032) as independent predictors of GIC. At 24 h, clinical improvement was more frequent in patients with GIC (80 vs. 49%, p = 0.01). Conclusions CTP CBF < 30% may overestimate infarct core volume, especially in patients imaged in the very early time window and with fast complete reperfusion. Therefore, the CTP CBF technique may exclude patients who would benefit from endovascular treatment.
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Affiliation(s)
- Nuno Martins
- Department of Internal Medicine, Hospital Fernando Fonseca, Amadora, Portugal
| | - Ana Aires
- Department of Neurology, São João Hospital Center, Porto, Portugal.,Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Beatriz Mendez
- Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Sandra Boned
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Marta Rubiera
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Alejandro Tomasello
- Department of Neuroradiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Pilar Coscojuela
- Department of Neuroradiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - David Hernandez
- Department of Neuroradiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marián Muchada
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - David Rodríguez-Luna
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Noelia Rodríguez
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Jesús M Juega
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Jorge Pagola
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Carlos A Molina
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Marc Ribó
- Stroke Unit, Department of Neurology, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain.,Departament de Medicina, Universitat Autónoma de Barcelona, Barcelona, Spain
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Boned S, Padroni M, Rubiera M, Tomasello A, Coscojuela P, Romero N, Muchada M, Rodríguez-Luna D, Flores A, Rodríguez N, Juega J, Pagola J, Alvarez-Sabin J, Molina CA, Ribó M. Admission CT perfusion may overestimate initial infarct core: the ghost infarct core concept. J Neurointerv Surg 2016; 9:66-69. [DOI: 10.1136/neurintsurg-2016-012494] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Revised: 07/30/2016] [Accepted: 08/05/2016] [Indexed: 11/03/2022]
Abstract
BackgroundIdentifying infarct core on admission is essential to establish the amount of salvageable tissue and indicate reperfusion therapies. Infarct core is established on CT perfusion (CTP) as the severely hypoperfused area, however the correlation between hypoperfusion and infarct core may be time-dependent as it is not a direct indicator of tissue damage. This study aims to characterize those cases in which the admission core lesion on CTP does not reflect an infarct on follow-up imaging.MethodsWe studied patients with cerebral large vessel occlusion who underwent CTP on admission but received endovascular thrombectomy based on a non-contrast CT Alberta Stroke Program Early CT Score (ASPECTS) >6. Admission infarct core was measured on initial cerebral blood volume (CBV) CTP and final infarct on follow-up CT. We defined ghost infarct core (GIC) as initial core minus final infarct >10 mL.Results79 patients were studied. Median National Institutes of Health Stroke Scale (NIHSS) score was 17 (11–20), median time from symptoms to CTP was 215 (87–327) min, and recanalization rate (TICI 2b–3) was 77%. Thirty patients (38%) presented with a GIC >10 mL. GIC >10 mL was associated with recanalization (TICI 2b–3: 90% vs 68%; p=0.026), admission glycemia (<185 mg/dL: 42% vs 0%; p=0.028), and time to CTP (<185 min: 51% vs >185 min: 26%; p=0.033). An adjusted logistic regression model identified time from symptom to CTP imaging <185 min as the only predictor of GIC >10 mL (OR 2.89, 95% CI 1.04 to 8.09). At 24 hours, clinical improvement was more frequent in patients with GIC >10 mL (66.6% vs 39%; p=0.017).ConclusionsCT perfusion may overestimate final infarct core, especially in the early time window. Selecting patients for reperfusion therapies based on the CTP mismatch concept may deny treatment to patients who might still benefit from reperfusion.
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: the workflows, methods, and platforms. Brain Inform 2015; 2:181-195. [PMID: 27747508 PMCID: PMC4737665 DOI: 10.1007/s40708-015-0020-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 08/20/2015] [Indexed: 12/20/2022] Open
Abstract
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- School of IT, The University of Sydney, Sydney, Australia
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform 2015; 2:167-180. [PMID: 27747507 PMCID: PMC4737664 DOI: 10.1007/s40708-015-0019-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 08/08/2015] [Indexed: 12/20/2022] Open
Abstract
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, and the Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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