An augmented reality navigation platform combining intravascular ultrasound (IVUS), electromagnetic position tracking, and preoperative CT significantly reduced cannulation time, radiation exposure, and cognitive workload compared to conventional fluoroscopy in benchtop phantom and in-vivo ovine studies. The system uses ECG-gated robotic pullback to capture 4D aortic motion, a deep learning model to extract vascular lumen boundaries from artifact-prone IVUS streams, and a non-rigid CT-IVUS fusion pipeline resistant to false-positive landmarks — demonstrated in the complex context of fenestrated endovascular aneurysm repair (FEVAR).
Endovascular procedures are among the fastest-growing surgical disciplines, yet fluoroscopy's 2D imaging and cumulative radiation burden remain stubborn clinical liabilities — particularly for complex branched-vessel repairs like FEVAR, where precise catheter alignment is critical and procedural duration is long. This platform addresses multiple failure points simultaneously: depth perception, real-time deformation correction, and operator cognitive load. The deep learning segmentation approach for noisy IVUS data is a meaningful technical contribution, as artifact rejection has historically hobbled IVUS-guided navigation.
That said, important caveats apply. Testing in ovine models and bench phantoms does not guarantee human translation — aortic anatomy, pathology severity, and intraoperative variability differ substantially in clinical populations. Surgeon sample sizes are not reported in the abstract. Critically, this is a preprint posted on medRxiv and has not yet undergone peer review, meaning methods, effect sizes, and conclusions remain unvalidated. If findings hold through peer review and human trials, this represents a potentially paradigm-shifting step toward radiation-free, depth-aware endovascular guidance.