Intracranial vessel wall imaging framework - Data acquisition, processing, and visualization.

Magn Reson Imaging

Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany; Department of Experimental Physics 5 (Biophysics), University of Würzburg, Würzburg, Germany. Electronic address:

Published: November 2021

Objective: Assessment of vessel walls is an integral part in diagnosis and disease monitoring of vascular diseases such as vasculitis. Vessel wall imaging (VWI), in particular of intracranial arteries, is the domain of Magnetic Resonance Imaging (MRI) - but still remains a challenge. The tortuous anatomy of intracranial arteries and the need for high resolution within clinically acceptable scan times require special technical conditions regarding the hardware and software environments.

Materials And Methods: In this work a dedicated framework for intracranial VWI is presented offering an optimized, black-blood 3D T1-weighted post-contrast Compressed Sensing (CS)-accelerated MRI sequence prototype combined with dedicated 3D-GUI supported post-processing tool for the CPR visualization of tortuous arbitrary vessel structures.

Results: Using CS accelerated MRI sequence, the scanning time for high-resolution 3D black-blood CS-space data could be reduced to under 10 min. These data are adequate for a further processing to extract straightened visualizations (curved planar reformats - CPR). First patient data sets could be acquired in clinical environment.

Conclusion: A highly versatile framework for VWI visualization was demonstrated utilizing a post-processing tool to extract CPR reformats from high-resolution 3D black-blood CS-SPACE data, enabling simplified and optimized assessment of intracranial arteries in intracranial vascular disorders, especially in suspected intracranial vasculitis, by stretching their tortuous course. The processing time from about 15-20 min per patient (data acquisition and further processing) allows the integration into clinical routine.

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Source
http://dx.doi.org/10.1016/j.mri.2021.08.004DOI Listing

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