WFUMB Liver Ultrasound Fusion Imaging Technical Review and Position Statement: Focus on CT/MRI-Based Fusion.
Ultrasound fusion imaging enabled successful liver lesion ablation in up to 90%-95% of cases.
WFUMB Liver Ultrasound Fusion Imaging Technical Review and Position Statement: Focus on CT/MRI-Based Fusion.
This technology is increasingly used for liver imaging and interventions, especially when conventional B-mode ultrasonography fails to provide adequate lesion visualization.
The aim of this technical review and position statement is to evaluate the technical accuracy (target registration errors) and lesion visibility of ultrasound fusion imaging based on published evidence and expert consensus.
A systematic review was conducted using a PICO framework focused on two core questions: (i) to evaluate electromagnetic-tracked fusion target registration errors (PICO T1), and (ii) whether fusion improves visibility of lesions in difficult-to-image liver lesions (PICO T2).
The technical accuracy of fusion imaging was consistently high, with target registration errors (target registration errors) of ∼1-3 mm in ideal phantom settings and ∼4-14 mm in clinical studies.
Automatic registration methods were faster and similarly accurate as manual registration, possibly reducing operator dependence.
Safety profiles across studies were favorable, with major complication rates generally below 2%.
Furthermore, fusion imaging might prove especially beneficial for treating tumors in difficult locations (e.g., caudate lobe, peribiliary lesions).
Ultrasound fusion imaging significantly enhances the spatial accuracy of liver interventions by aligning real-time ultrasonography with computed tomography/magnetic resonance imaging datasets.
It improves interventional procedures guidance and maintains a low complication profile as compared to conventional ultrasonography alone.
Advancements in artificial intelligence (artificial intelligence) and augmented reality (augmented reality) are expected to further optimize image co-registration workflows and clinical outcomes.