Breast Deformation Modeling Based on MRI Images, Preliminary Results

Danch–Wierzchowska, Marta; Borys, Damian; Świerniak, Andrzej

doi:10.1007/978-3-319-39904-1_20

Cited by 3 publications

(1 citation statement)

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“…The deformation was caused by patient movement. Prone to supine registration of MRI and Computer Tomography was presented in [6]. However, the multimodal registration of US images acquired under the pressure of the probe and MRI images for a breast biopsy procedure is still a research subject.…”

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Autonomous Robotic System for Breast Biopsy With Deformation Compensation

Ferrari

Tagliabue

Maris

et al. 2023

IEEE Robot. Autom. Lett.

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Image-guided biopsy is the most common technique for breast cancer diagnosis. Although magnetic resonance imaging (MRI) has the highest sensitivity in breast lesion detection, ultrasound (US) biopsy guidance is generally preferred due to its non-invasiveness and real-time image feedback during the insertion. In this work, we propose an autonomous robotic system for US-guided biopsy of breast lesions identified on pre-operative MRI. After initial MRI to breast registration, the US probe attached to the robotic manipulator compresses the breast tissues until a pre-determined force level is reached. This technique, known as preloading, will allow to minimize lesion displacement during the needle insertion. Our workflow integrates a deformation compensation strategy based on patient-specific biomechanical model to update the US probe orientation keeping the target lesion on the US image plane during compression. By relying on a deformation model, the proposed system does not require lesion visibility on US. Experimental evaluation is performed to assess the performance of the system on a realistic breast phantom with 15 internal lesions, considering different preloading forces. The deformation compensation strategy allows to improve localization accuracy, and as a consequence final probe positioning, for all considered lesions. Median lesion localization error is 3.3 mm, which is lower than the median lesion radius, when applying a preloading of 2 N, which guarantees both minimal needle insertion error and tissue stress.

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confidence: 99%