Phase‐Contrast MRI Detection of Ventricular Shunt CSF Flow: Proof of Principle

Abstract

The evaluation of a suspected malfunction of a ventricular shunt is a common procedure in neurosurgery. The evaluation relies on either the interpretation of the ventricular width using cranial imaging or invasive techniques. Several attempts have been made to measure the flow velocity of cerebrospinal fluid (CSF) utilizing different phasecontrast magnet resonance imaging (PC MRI) techniques. In the present study, we evaluated 3 T (Tesla) MRI scanners for their effectiveness in determining of flow in the parenchymal portion of ventricular shunt systems with adjustable valves containing magnets. METHODS: At first, an MRI phantom was used to measure the phase-contrasts at different constant low flow rates. The next step was to measure the CSF flow in patients treated with ventricular shunts without suspected malfunction of the shunt under observation. RESULTS: The measurements of the phantom showed a linear correlation between the CSF flow and corresponding phase values. Despite many artifacts resulting from the magnetic valves, the ventricular catheter within the parenchymal portion of shunt was not superimposed by artifacts at each PC MRI plane and clearly distinguishable in 9 of 12 patients. Three patients suffering from obstructive hydrocephalus showed a clear flow signal. CONCLUSION: CSF flow detected within the parenchymal portion of the shunt by PC MRI may reliably provide information about the functional status of a ventricular shunt. Even in patients whose hydrocephalus was treated with magnetic adjustable valves, the CSF flow was detectable using PC MRI sequences at 3 T field strength.

Volumetric effect of shunt adjustments in normal pressure hydrocephalus: a randomized, double-blind trial

“…Phase-contrast MRI on the flow in the catheter has been considered for noninvasive shunt function testing, although this method seemed less sensitive in iNPH than in obstructive hydrocephalus. 35,36 Ultrasound 37 and thermal sensing 38 have been presented as other possible methods of noninvasive shunt testing, but to our knowledge have not been tried in patients with iNPH, in which flow-based methods are potentially less reliable due to lower flow rates when compared with obstructive hydrocephalus. Despite the 1-month run-in period after surgery at PL 1.5, 8 participants developed subdural effusions, all of them at PL 1.0.…”

Measuring CSF shunt flow with MRI using flow enhancement of signal intensity (FENSI)

“…21 A recent phase contrast method using quantitative flow encoding, with the velocity encoding tuned to target very slow flow, had a feasible and promising quantitative phantom test result but showed degradation in performance under in vivo conditions, resulting in only qualitative indications of flow in patient samples. 22 Quantitative flow enhancement of signal intensity (qFENSI), 23 a blood flow measurement technique to measure slow blood flow or flux in the microcirculation, was previously developed and tested for functional imaging of reactive hyperemia. The current study expands this technique to measure coherent slow flow in CSF shunts.…”

“…With the use of specialized coils to provide highly localized scanning at the site of the shunt, there have been successful approaches that have achieved quantitative accuracy in particular geometries 19,20 or with special flow‐encoding settings 21 . A recent phase contrast method using quantitative flow encoding, with the velocity encoding tuned to target very slow flow, had a feasible and promising quantitative phantom test result but showed degradation in performance under in vivo conditions, resulting in only qualitative indications of flow in patient samples 22 …”

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

“…Furthermore, a crucial factor in parallel imaging is the smoothness of the coil estimation methods, which greatly influence the quality of the complex-valued imagery. Phase singularity has appeared in the recent literature, such as deep-learning frameworks [26] or phase-contrast MRI [27], forcing one to include the second dominant eigenvector [28]. Some networks effectively mitigate the phase singularity of eigen-decomposition [29,30].…”