Ultra-Malin: Robotic Ultrasound Mapping and Localization via Implicit Neural Representation

Robotic ultrasound systems (RUSS) can potentially release the workload of sonographers and overcome operator-dependent limitations. Previous methods of ultrasound image localization without mapping, constrained by position and specific image features, have limited applicability. This article presents Ultra-Malin, a mapping and localization method for autonomous robotic ultrasound using implicit neural representations (INRs). Our method leverages its interimage inference capacity to perform image-based localization without specifying tissue features. Similar to a pinhole camera model, a geometric model is first proposed to determine scanning depth, as ultrasound transducer rays can be analogous to rays from a camera. Subsequently, we retrieve the 5-D coordinates of ultrasonic ray points and employ volume rendering techniques to render the pixel color of the ultrasound image. A global search method with density-guided ray sampling is then presented to locate the ultrasound image by minimizing the error between the target and the rendered images. The experimental results show that the rendered images for probe tilting, rotational, and linear scanning have structural similarity index measures (SSIM) of 0.7788, 0.7999, and 0.8421, respectively, outperforming existing methods using only a small amount of data. The localization experiments verify that the probe can accurately locate the input image and the rendered ultrasound images are highly consistent with the actual images. Furthermore, the probe can still be located at the target position even if disturbances obscure part features, and the search space can be enlarged by the proposed density-guided ray sampling.