Chiari Malformation type 1 (CM1) is a pathology characterized by structural defects in the cerebellum and a vast associated symptomatology which can include recurrent headaches, muscle weakness, sleep disorders and, in the most extreme cases, even paralysis. So far, diagnosis is based on an assessment of the patient's neurological history combined with an MRI or CT examination. However, the lack of a uniform and clear symptomatology among patients is so pronounced that an estimated 3.2 million of the patients affected do not show symptoms significant enough to lead to a diagnosis. Increasing diagnostic accuracy would therefore be of crucial importance, given that early diagnosis of Chiari malformation and subsequent surgical treatment can lead to highly improved clinical outcomes. One overlooked element that is thought of as a prime candidate for diagnosing obstructive brain disorders such as Chiari Malformation is brain motion. As the heart contracts and relaxes during the cardiac cycle, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in relatively localized motions and deformations of the brain, which are very subtle and difficult to see and quantify on traditional cine MRI images. Such motions, however, are expected to follow different spatial and temporal patterns in patients suffering from obstructive brain malformations. We have recently developed a novel method called amplified Magnetic Resonance Imaging (aMRI), which uses a video magnification algorithm to amplify the subtle spatial variations in cardiac-gated brain MRI scans. This approach reveals deformations of the brain parenchyma, and displacements of arteries and CSF due to cardiac pulsatility. We hypothesize that the anatomy of CM1 patients causes an increased cerebellar, spinal cord, and pons motion which cannot be reliably captured with standard imaging methods but can be assessed with our aMRI technique. To test this hypothesis, we propose to extend our aMRI method to to capture and quantitatively track 3D brain motion during the cardiac cycle. We will first validate the 3D-aMRI method with computational phantom models, consisting of deformable solids of varying properties. In parallel, we will test the 3D-aMRI method in a healthy adult population and obtain age and gender specific normal ranges of brain motion in different regions of the brain. Finally, the potential diagnostic value of aMRI will be tested in patients with CM1, where we will compare the aMRI-derived CM1 brain motion data against those of healthy volunteers. aMRI has the potential for widespread clinical use and significant impact since it can amplify and characterize small, often barely perceptible motion and can visualize the biomechanical response of tissues using the heartbeat as an endogenous mechanical driver. Further development of this method could enable earlier diagnosis and intervention of brain pathologies other than CM1 such as traumatic brain injury, hydrocephalus, Alzheimer's disease, and other neurodegenerative diseases; may remove the need for unnecessary invasive brain surgery; and may provide a reliable method to monitor progress following therapeutic intervention.

Public Health Relevance

The proposed research is strongly relevant to public health because of the development of an imaging method, amplified MRI, which will be used for diagnosis of Chiari Malformation I and potentially other neurological diseases in the near future. This will specifically improve the prognosis and diagnosis of Chiari Malformation I, thereby informing surgeons about which patient to operate on or not. The strong interdisciplinary efforts of combining medical imaging and bioengineering to fundamentally improve the detection and treatment of disease is directly relevant to one of the key missions of the NIH.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Riddle, Robert D
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Stevens Institute of Technology
United States
Zip Code