Ultrasound fusion imaging with real-time navigation for the surveillance after endovascular aortic aneurysm repair

Individualizing Surveillance after Endovascular Aortic Repair Using a Modular Imaging Algorithm

OBJECTIVES

Surveillance after endovascular aortic repair (EVAR) and fenestrated EVAR (FEVAR) is mainly directed by one-size-fits-all approaches instead of personalized decision making, even though treatment strategies and often endografts themselves are tailor-made to adjust for individual patients. We propose a modular imaging algorithm that escalates surveillance imaging based on invasiveness and need.

MATERIALS AND METHODS

In this retrospective observational study of single-center data, results of a modular imaging algorithm were analyzed. The algorithm is characterized by initiating the examination with standard B-mode then transitioning to Duplex ultrasound, B-Flow, and CEUS. Additional CT(A) studies are conducted where required. The study population included both patients receiving EVAR or FEVAR. A comparative analysis was conducted regarding endoleak detection.

RESULTS

The study population included 28 patients receiving EVAR and 40 patients receiving FEVAR. They accounted for 101 follow-up visits, which led to 431 distinct imaging studies. CEUS has the highest endoleak detection rate, followed by CTA and B-Flow. Duplex ultrasound and B-Flow resulted in 0 and 1 false positive cases, respectively, considering CEUS the reference standard. In a select group of six patients, CEUS was omitted after endoleaks were displayed by Duplex ultrasound or B-Flow, leading to a successful type II coiling and no aneurysm-related adverse events.

CONCLUSIONS

The proposed modular algorithm showed great potential to incorporate principles of personalized medicine in surveillance after endovascular aortic treatment. Since Duplex ultrasound and B-Flow rarely cause false positive endoleaks, more resource-intensive and invasive imaging studies such as CEUS and CTA can be omitted after positive identification.

Three-Dimensional Surface Point Cloud Ultrasound for Better Understanding and Transmission of Ultrasound Scan Information

Ultrasound is notoriously plagued by high user dependence. There is a steep drop-off in information in going from what the sonographer sees during image acquisition and what the interpreting radiologist is able to view at the reading station. One countermeasure is probe localization and tracking. Current implementations are too difficult and expensive to use and/or do not provide adequate detail and perspective. The aim of this work was to demonstrate that a protocol combining surface three-dimensional photographic imaging with traditional ultrasound images may be a solution to the problem of probe localization, this approach being termed surface point cloud ultrasound (SPC-US). Ultrasound images were obtained of major vessels in an ultrasound training phantom, while simultaneously obtaining surface point cloud (SPC) 3D photographic images, with additional scanning performed on the right forearm soft tissues, kidneys, chest, and pelvis. The resulting sets of grayscale/color Doppler ultrasound and SPC images are juxtaposed and displayed for interpretation in a manner analogous to current text-based annotation or computer-generated stick figure probe position illustrations. Clearly demonstrated is that SPC-US better communicates information of probe position and orientation. Overall, it is shown that SPC-US provides much richer image representations of probe position on the patients than the current prevailing schemes. SPC-US turns out to be a rather general technique with many anticipated future applications, though only a few sample applications are illustrated in the present work.