Hydrodynamics of Cerebrospinal Fluid in Spinal Canal with Chiari Malformation and Syringomyelia
نویسندگان
چکیده
Magnetic resonance imaging (MRI) is has great potential as a tool for diagnosis of neurological diseases such as Chiari malformation (CM) and syringomyelia (SM). Its extended capability to obtain in-vivo velocities of blood or cerebrospinal fluid (CSF) allowed engineers to perform studies with engineering analysis applications such as computational fluid dynamics and physical modeling. Recently, a new MR technique called balanced steady-state free procession (bSSFP) cine imaging was developed for analysis of geometrical changes during the cardiac cycle due to compliance [1]. This study used MRI utilities to investigate hydrodynamic environment of CSF in the sub-cranial subarachnoid space (SAS). A model was constructed to simulate fluid dynamics of CSF in SAS. This model will allow investigation of detailed pressure and velocity using laser Doppler anemometry and pressure transducers. The MRI data from patient, healthy volunteers, the flow model were compared. INTRODUCTION CSF fluid moves in a pulsatile manner within the SAS of the spinal canal and cranial cavity. This fluid cushions the brain from impact on the cranial bone due to sudden motions. CSF also delivers nutrients and protein to and from the central nervous system and removes waste products. CM is a disorder that consists of a downward displacement of the lower section of the brain (cerebellum tonsils) into the spinal canal. This disorder disrupts the normal hydrodynamics of CSF flow in the head and spinal canal. SM, which is a syrinx inside the spinal cord filled with CSF, can also develop in patients with CM or spine trauma. This study is directed toward a better understanding of the hydrodynamic environment within SAS with CM and SM. Since geometry and flow rate can be obtained from the MRI, the pressure and velocity can be computed, as demonstrated by Kalata [2] and Loth et al. [3]. The characterization of the hydrodynamic environment using modeling may lead to greater understanding of pathogenesis of craniospinal disorders such as
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