Breast shape estimation and correction in CESM biopsy
Ruben Sanchez (GE Healthcare), Cl\'ement Jailin (GE Healthcare),, Ann-Katherine Carton (GE Healthcare), Pablo Milioni de Carvalho (GE, Healthcare), Laurence Casteignau (GE Healthcare), Serge Muller (GE, Healthcare)

TL;DR
This paper presents a novel method for estimating and correcting breast shape deformation in CESM-guided biopsies, enhancing lesion visibility and biopsy accuracy by modeling the breast bump shape using physical and Fourier components.
Contribution
The study introduces a new shape estimation technique that separates breast bump deformation from contrast texture, improving image quality during CESM-guided biopsies.
Findings
Error standard deviation of 0.37 mm in shape estimation
Thickness correction improves lesion identification
Method validated on 3D phantom and clinical images
Abstract
Description of purpose: Contrast-enhanced spectral mammography can be used to guide needle biopsies. However, in vertical approach the compressed breast is deformed generating a so-called bump in the paddle aperture, which may interfere with the visibility of contrast-uptakes. Local thickness estimation would provide an enhanced image quality of the recombined image, increasing the visibility of the contrast-uptakes to be targeted during the biopsy procedure. In this work we propose a method to estimate the shape of the breast bump in biopsy vertical approach. Materials and Methods: Our method consists on two steps: first, we compute a raw thickness which does not take into account the presence of contrast-uptakes; second, we use a physical model to separate the sparse iodine texture from the breast shape. This physical model is composed by a sum of Fourier components, describing the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
