Joint Annual Meeting ISMRM-ESMRMB 2014 10-16 May 2014 Milan, Italy

Stroke: Clinical Applications & Predictive Modeling

Wednesday 14 May 2014
Silver  13:30 - 15:30 Moderators: Patricia M. Desmond, M.D., Walter Kucharczyk, M.D., F.R.C.P.C.

13:30 0587.   Acute lesion topography relationship with clinical admission symptoms and long-term functional outcomes in stroke patients
Ona Wu1, Lisa Cloonan2, Steven Mocking1, Mark Bouts1, Jonathan Rosand2, Karen L. Furie2, and Natalia S. Rost2
1Athinoula A Martinos Center, Massachusetts General Hospital, Boston, MA, United States, 2Department of Neurology, Massachusetts General Hospital, Boston, United States

Neuroimaging is often proposed as a potential surrogate for clinical outcome in the evaluation of novel stroke therapies. We analyzed 480 patients from a prospective single center stroke data repository using voxel-based symptom mapping (VLSM) techniques with acute DWI lesions and follow-up modified Rankin Scale Score and NIH Stroke Scale Score. We found acute lesions in the left hemisphere were associated with poor outcome. VLSM methods may provide insight into patients at risk of poor long-term outcome and provide guidance towards which patients will most likely benefit from aggressive acute intervention and follow-up physical therapy.

13:42 0588.   Independent component analysis for assessing tissue at risk of infarction in acute ischemic stroke - permission withheld
Venkata Veerendranadh Chebrolu1, Ashish Rao2, Dattesh D Shanbhag1, Sandeep N Gupta3, Uday Patil1,4, Patrice Hervo5, Catherine Oppenheim6,7, and Rakesh Mullick8
1Medical Image Analysis Lab, GE Global Research, Bangalore, Karnataka, India, 2Bio-Medical Signal Analysis Lab, GE Global Research, Bangalore, Karnataka, India, 3Biomedical Image Processing Lab, GE Global Research, Niskayuna, NY, United States, 4Manipal Health Enterprises Pvt. Ltd., Bangalore, Karnataka, India, 5GE Healthcare, Buc, France, 6Departments of Radiology and Neurology, Centre Hospitalier, Sainte-Anne, Paris, France,7Université Paris Descartes, Paris, France, 8Diagnostics and Biomedical Technologies, GE Global Research, Bangalore, Karnataka, India

Independent component analysis of DSC-MRI data was demonstrated to identify hypo-perfused regions in acute ischemic stroke. Independent component analysis based assessment acute ischemic stroke can be achieved without arterial input function detection and explicit parameterization of the DSC perfusion data.

13:54 0589.   
The role of predictive algorithm selection on the accuracy of MRI-based prediction of tissue outcome after acute ischemic stroke
Mark JRJ Bouts1, Elissa McIntosh1, Raquel Bezerra1, Izzuddin Diwan1, Steven Mocking1, Priya Garg1, William T Kimberly2, Ethem M Arsava1, William A Copen3, Pamela W Schaefer3, Hakan Ay1, Aneesh B Singhal2, Bruce R Rosen1, Rick M Dijkhuizen4, and Ona Wu1
1Athinoula A. Martinos Center, Dept Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States, 2Dept Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States, 3Dept Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States, 4Biomedical imaging & Spectroscopy group, Image Sciences Institute, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

MRI-based prediction algorithms may more accurately assess tissue at risk of infarction following acute ischemic stroke than perfusion-diffusion mismatch. Yet, few studies quantitatively evaluated the predictive performance of several algorithms. We evaluated four algorithms in a cohort of acute ischemic stroke patients not receiving subsequent revascularization intervention nor novel therapeutics. Two regression models (generalized linear model, generalized additive model) were tested against two ensemble methods (adaptive boosting and random forest). All algorithms performed better than perfusion-diffusion mismatch for predicting follow-up infarct. More complex algorithms offered improved accuracy in predicting tissue infarction after stroke.

14:06 0590.   Tissue Outcome Prediction in Ischaemic Stroke with Diffusion, Perfusion and pH Sensitive CEST Imaging at Three Different Time Points
Jacob Levman1, George Harston2, Yee Kai Tee1, Thomas W Okell3, Nicholas Blockley3, Michael Chappell1, Peter Jezzard3, James Kennedy2, and Stephen Payne1
1Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, England, United Kingdom, 2Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, England, United Kingdom, 3Department of Clinical Neurosciences, Oxford Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, England, United Kingdom

This study assessed MRI modalities for predicting tissue outcome in acute stroke subjects. Diffusion, perfusion and pH sensitive MR images were acquired at 4, 6 and 24 hours post stroke. Follow-up imaging was performed at 7 and 28 days. Receiver operating characteristic curve analysis was performed to evaluate each modality’s ability to predict tissue outcome as assessed by follow-up fluid attenuated inversion recovery (FLAIR) imaging. A demonstration of machine learning combining the three acute modalities was able to predict an additional 6% of FLAIR assessed tissue damage that was not suspicious based on diffusion characteristics alone.

14:18 0591.   Rate of FLAIR signal evolution depends on depth of ischemia and time: predicting ischemia age
Hongyu An1, Andria L Ford2, Yasheng Chen1, Katie Vo3, William Powers4, Jin-Moo Lee2, and Weili Lin1
1Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States, 2Neurology, Washington University in St. Louis, St. Louis, MO, United States, 3Radiology, Washington University in St. Louis, St. Louis, MO, United States, 4Neurology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States

Rates of FLAIR change depends on both time from stroke onset and depth of ischemia. Lesion age prediction is more reliable in moderate to severe ischemia. The median absolute prediction error on ischemia age is ~1hour across all patient using a generalized linear model.

14:30 0592.   
Serial perfusion imaging using arterial spin labeling in acute ischemic stroke
George William John Harston1, Thomas Okell2, Fintan Sheerin3, Martino Cellerini3, Stephen Payne4, Peter Jezzard2, Michael Chappell4, and James Kennedy1
1Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom, 2Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, United Kingdom, 3Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, United Kingdom, 4Department of Engineering Science, University of Oxford, Oxford, Oxfordshire, United Kingdom

Hypoperfusion underlies the pathophysiology of ischemic stroke and as such has been used extensively to identify tissue at risk of infarction. However, presenting perfusion imaging has not been successfully used to select patients for treatment. In this observational cohort study patients with large volume ischemic stroke were scanned serially with quantitative arterial spin labeling perfusion imaging to better understand the natural history of absolute perfusion measurements and the dynamics of cerebral blood flow over the first hours and days following a stroke. A marked individual and temporal heterogeneity of cerebral blood flow was observed.

14:42 0593.   Significant MRI scanner model related differences in hemodynamic imaging: A secondary analysis of 174 dynamic susceptibility contrast MRI studies from the MR RESCUE clinical trial
Jeffry R Alger1, David S Liebeskind1, Reza Jahan2, Jeffrey L Saver1, and Chelsea S. Kidwell3,4
1Neurology, Geffen School of Medicine, UCLA, Los Angeles, CA, United States, 2Radiological Sciences, Geffen School of Medicine, UCLA, Los Angeles, CA, United States, 3Neurology, University of Arizona, Tuscon, Arizona, United States, 4Medical Imaging, University of Arizona, Tuscon, Arizona, United States

An analysis of scanner model bias in DSC MRI CBF readings from acute ischemic stroke patients enrolled in the MR RESCUE clinical trial was performed. The data were obtained using 16 unique MRI scanner models at 19 imaging centers. Results indicate a statistically significant scanner model related bias is present in CBF readings from well perfused tissues as well as hypoperfused tissues. Future multicenter studies that use DSC MRI should therefore take scanner model bias into consideration. Between-patient variability that is unrelated to scanner model is also present and this variability is as large as the scanner model-related variability.

14:54 0594.   
Using Structural Connectivity Graph Analysis to Predict Cognitive Decline in Patients After Carotid Endarterectomy
Salil Soman1,2, Gautam Prasad3,4, Elizabeth Hitchner5, Wei Zhou5,6, Michael Moseley7, and Allyson Rosen8,9
1Radiology, Stanford University, Menlo Park, CA, United States, 2California War Related Illness and Injury Study Center, Palo Alto Veteras Affairs Hospital, Palo Alto, CA, United States, 3LONI, University of Southern California, Los Angeles, CA, United States, 4Psychology, Stanford University, CA, United States, 5Vascular Surgery, Stanford University, CA, United States, 6Vascular Surgery, Veterans Affairs Palo Alto Health Care System, CA, United States, 7Radiology, Stanford University, CA, United States, 8Pschology, Stanford University, CA, United States, 9Pscychology, Veterans Affairs Palo Alto Health Care System, CA, United States

Some patients with carotid stenosis that undergo carotid surgery afterwards experience cognitive decline. Identifying these patients before surgery would allow targeting of therapies to minimize disability. We hypothesized that structural connectivity graphs could identify these patients. We performed T1, DTI, and neuropsychological testing prior to surgery. Repeat neuropsychological testing was then performed 1 month later. FreeSurfer 5.3 whole brain segmentation, whole brain HARDI tractography, and connectivity analysis were then performed. The graph analysis methods “weighted optimal community structure” & “binary connected component sizes metrics” both predicted patients that would experience cognitive decline with 81% sensitivity 83% and specificity (FDR .05).

15:06 0595.   Calibrated MRI in patients with occlusive cerebrovascular disease.
J. B. De Vis1, E. T. Petersen1, N. S. Hartkamp1, A. Bhogal1, C. J.M. Klijn2, L. J. Kappelle2, and J. Hendrikse1
1Radiology, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands, 2Neurology, University Medical Center Utrecht, Utrecht, Utrecht, Netherlands

Calibrated MRI is an upcoming non-invasive technique to evaluate brain metabolism. So far, it has only been evaluated in healthy volunteers and has not been used in patients yet. In this study we investigate the potential of calibrated MRI to study hemodynamic impairment in patients with cerebrovascular disease. Our patients demonstrate lower BOLD reactivity but equal oxygen extraction fraction suggesting impaired vascular reactivity. In addition, we find that blood flow through collateral pathways introduces artefacts in 27% of our patients limiting the use of calibrated MRI in patients with cerebrovascular disease.

15:18 0596.   Functional MRI in stroke patients following brain-computer interface-assisted motor imagery rehabilitation
Fatima Nasrallah1, Zhong Kang Lu2, Hong Xin3, Guan Cuntai2, Kai Keng Ang2, Kok Soon Phua2, Irvin The4, Wei peng Teo5, Zhao Ling Yun6, Ning Tang6, Effie Chew6, and Kai-Hsiang Chuang3
1Clinical Imaging Research Center, Singapore, Singapore, Singapore, 2Institute for Infocomm Research, A*STAR, Singapore, Singapore, 3Singapore Bioimaging Consortium, Singapore, Singapore, 4Department of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom, 5Central Queensland University, Queensland, Australia, 6The Division of Neurology and Rehabilitation Medicine, National University Hospital System, Singapore, Singapore

We have combined robot-assisted motor imagery and brain-computer interface (MI-BCI) and transcranial direct current stimulation (tDCS) for the rehabilitation of stroke patients due to the potential of these combined methods to modulate the cortical excitability, and hence the potential to improve motor function recovery. The primary aim of this study was to investigate the structural and functional changes of the brain after rehabilitation training of MI-BCI combined with or without tDCS in stroke patients.