Breast cancer affects one out of eight women. Ultrasound (US) plays an important role in the diagnostic workflow, especially during the biopsy phase, in which tissue is extracted from the lesion for further analysis. The extension from 2D to 3D US acquisitions has multiple benefits including enhanced lesion localization and improved registration with MRI data. Current commercial 3D US systems lack the ability to preserve the breast’s original shape. Robotic US scanners follow tailored trajectories and produce high quality volumes by accurate localization of 2D slices captured with a conventional linear probe. Current methods require a patient specific model to plan the scanning trajectory.In this study we investigate how to change the direction of the scanning trajectory based on US feedback, such that no patient specific model is required to perform a scan. In our method, the scanning trajectory is kept tangent to the breast based on confidence maps of the US images and an estimation of current radius of curvature of the surface. We evaluated our approach on a realistic breast phantom. The robot revolves around the breast without prior knowledge of its shape. In ten scans, the RMS error between the probe’s scanning plane and the breast’s surface normal is 12.6° out-of-plane, and 4.3° in-plane. A 3D US reconstruction shows the acquired data. This is a step forward to fully autonomous, high quality robotic US volume acquisitions.

Current challenges in automated robotic breast ultrasound (US) acquisitions include keeping acoustic coupling between the breast and the US probe, minimizing tissue deformations and safety. In this paper, we present how an autonomous 3D breast US acquisition can be performed utilizing a 7DOF robot equipped with a linear US transducer. Robotic 3D breast US acquisitions would increase the diagnostic value of the modality since they allow patient specific scans and have a high reproducibility, accuracy and efficiency. Additionally, 3D US acquisitions allow more flexibility in examining the breast and simplify registration with preoperative images like MRI. To overcome the current challenges, the robot follows a reference- based trajectory adjusted by a visual servoing algorithm. The reference trajectory is a patient specific trajectory coming from e.g. an MRI. The visual servoing algorithm commands in-plane rotations and corrects the probe contact based on confidence maps. A safety aware, intrinsically passive framework is utilised to actuate the robot. The approach is illustrated with experiments on a phantom, which show that the robot only needs minor pre-procedural information to consistently image the phantom while relying mainly on US feedback.

One out of eight women will get breast cancer during their lifetime. A biopsy, a procedure in which a tissue sample is acquired from the lesion, is required to confirm the diagnosis. A biopsy is preferably executed under ultrasound (US) guidance because it is simple, fast, and cheap, gives real-time image feedback and causes little patient discomfort. However, Magnetic Resonance (MR)-detected lesions may be barely or not visible on US and difficult to find due to deformations of the breast. This paper presents a robotic setup and workflow that assists the radiologist in targeting MR-detected breast lesions under US guidance, taking into account deformations and giving the radiologist robotic accuracy. The setup consists of a seven degree-of-freedom robotic serial manipulator equipped with an end-effector carrying a US transducer and a three degree-of-freedom actuated needle guide. During probe positioning, the US probe is positioned on the patient's skin while the system tracks skin contact and tissue deformation. During the intervention phase, the radiologist inserts the needle through the actuated guide. During insertion, the tissue deformation is tracked and the needle path is adjusted accordingly. The workflow is demonstrated on a breast phantom. It is shown that lesions with a radius down to 2.9 mm can be targeted. While MRI is becoming more important in breast cancer detection, the presented robot-assisted approach helps the radiologist to effectively and accurately confirm the diagnosis utilizing the preferred US-guided method.