Implicit Neural Representations for Breathing-compensated Volume Reconstruction in Robotic Ultrasound
arxiv(2023)
摘要
Ultrasound (US) imaging is widely used in diagnosing and staging abdominal
diseases due to its lack of non-ionizing radiation and prevalent availability.
However, significant inter-operator variability and inconsistent image
acquisition hinder the widespread adoption of extensive screening programs.
Robotic ultrasound systems have emerged as a promising solution, offering
standardized acquisition protocols and the possibility of automated
acquisition. Additionally, these systems enable access to 3D data via robotic
tracking, enhancing volumetric reconstruction for improved ultrasound
interpretation and precise disease diagnosis. However, the interpretability of
3D US reconstruction of abdominal images can be affected by the patient's
breathing motion. This study introduces a method to compensate for breathing
motion in 3D US compounding by leveraging implicit neural representations. Our
approach employs a robotic ultrasound system for automated screenings. To
demonstrate the method's effectiveness, we evaluate our proposed method for the
diagnosis and monitoring of abdominal aorta aneurysms as a representative use
case. Our experiments demonstrate that our proposed pipeline facilitates robust
automated robotic acquisition, mitigating artifacts from breathing motion, and
yields smoother 3D reconstructions for enhanced screening and medical
diagnosis.
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