Comparative Effectiveness Research Award

This award was created to support investigators in the generation and synthesis of evidence that compares the benefits and harms of diagnostic imaging methods or interventions to prevent, diagnose, treat and monitor a clinical condition or to improve the delivery of care. Comparative effectiveness research (CER) is valuable to clinicians, patients, purchasers, and policy makers in making informed decisions that will improve health care at both the individual and population levels. CER includes the direct comparison of diagnostic imaging modalities or interventions for a specific disease in a study population. Neuroradiologists have an important role in the development of new imaging technologies and image-guided procedures, as well as in the assessment of these techniques in the clinical care of patients.

Grant Recipients


Sam Payabvash, MD
Yale School of Medicine

Cost-Effectiveness of Perfusion Imaging in Thrombectomy Treatment Triage of Stroke Patients in Extended Time Window


Nadja Kadom, MD
Emory University School of Medicine

Comparative Effectiveness of Imaging Techniques for Children with Headaches: Fast Brain MRI, Conventional Brain MRI, Head CT and “No Imaging”


Lea Alhilali, MD
Barrow Neurological Institute

Evaluation of a Single Bolus, Multi-Echo Dynamic Susceptibility Contrast Protocol in Patients with Glioblastoma


Akash Kansagra, MD
Washington University School of Medicine

Amount Funded: $60,000.00

Science of Systems of Care: Simulating Care Delivery and Patient-Centered Health Outcomes in Acute Ischemic Stroke


Timothy J. Amrhein, MD
Duke University Medical Center

Comparison of Cervical Transforaminal Epidural Corticosteroid Injections with lateralized Interlaminar Epidural Corticosteroid Injections for Treatment of Cervicogenic Upper Extremity Radiculopathy


Falgun Chokshi, MD, MS
Emory University School of Medicine

Diagnostic Yield & Cost of Repeat Emergency Department Head Computed Tomography for Non-Traumatic, Non-Localizing, & Non-Painful Neurologic Symptoms Using Machine Learning


Kristen Yeom, MD
Stanford University- Stanford, California

Detecting Abnormalities in Patients Who Otherwise Cannot be Reliably Scanned with MRI, by Reducing the Scan Failure Rate through a Fast Motion-Corrected Brain Protocol with High Diagnostic Quality, and with Efficacy That Is Comparable to or Superior Than the Conventional MRI Protocol


Vivek Prabhakaran, MD, PhD
UW Health UW Hospital Clinics, Univ. of Wisconsin, Madison, Wisconsin

Utility of fMRI as a Pre-surgical Planning Tool in Brain Tumor Patients


No Award Given


Ari M. Blitz, MD
Johns Hopkins Hospital, Baltimore, MD

Comparative Effectiveness of High Resolution 3D vs. Standard 2D Protocol Pituitary MRI for Cushing’s Disease


Ajay Gupta, MD
NY Presbyterian Hospital, New York, NY

Comparison of CT Perfusion and Digital Subtraction Angiography in the Evaluation of Delayed Cerebral Ischemia Using Bayesian Analysis