Non-invasive quantification of pressure drops in stenotic intracranial vessels: using deep learning-enhanced 4D flow MRI to characterize the regional haemodynamics of the pulsing brain

Stenosis of major intracranial arteries is a significant cause of stroke, with assessment of trans-stenotic pressure drops being a key marker of functional stenosis severity. Non-invasive methods for quantifying intracranial pressure changes are hence crucial; however, the narrow and tortuous cerebrovascular network poses challenges to traditional assessment methods such as transcranial Doppler. This study investigates the use of novel deep learning-enhanced super-resolution (SR) four-dimensional (4D) flow magnetic resonance imaging (MRI) in combination with a physics-informed virtual work-energy relative pressure technique to quantify pressure drops across stenotic intracranial arteries. Performance was validated in intracranial-mimicking <i>in vitro</i> experiments using pulsatile flow before being transferred into an <i>in vivo</i> cohort of patients with intracranial atherosclerotic disease. Conversion into sub-millimetre SR imaging significantly improved the accuracy of regional relative pressure estimations in the pulsing brain arteries, mitigating biases observed at >1 mm resolution imaging, and agreeing strongly with reference catheter-based invasive measurements across both moderate and severe stenoses. The <i>in vivo</i> analysis also revealed a significant increase in pressure drops when converting into sub-millimetre SR data, underlining the importance of apparent image resolution in a clinical setting. The results highlight the potential of SR 4D flow MRI for non-invasive quantification of cerebrovascular pressure changes in pulsing intracranial arteries across stenotic vessel segments.