Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change

Abstract

Background: The strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable. Methods: This study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images. During the registration process, the mechanical properties of the breast tissues were individually assigned using an optimization process, which allowed the model to become patient specific. Evaluation and results: The proposed method has been extensively tested on two datasets collected from two independent institutions, one from America and another from Hong Kong. Conclusions: Our method can accurately predict the deformation of breasts from the supine to prone position for both the Hong Kong and American samples, with a small target registration error of lesions.

Breast Tumor Localization by Prone to Supine Landmark Driven Registration for Surgical Planning

“…The use of surgical surface information combined with biomechanical models produced similar results (average landmarks distance of 15.3 mm) [22] or worse results (average cutaneous distance of 20.8 mm) [20]. Recent research using a hybrid approach and combining biomechanical models and supine surface information has obtained very competitive results (average tumor distance of 8.05 mm), although the dataset is still limited (25 cases) and with a smaller percentage of medium and large breast volume cases [23].…”

“…[20], [21]. The patient surface extracted from a preoperative computerized tomography (CT) image in supine position has also been proposed as a reference image in the surgical position to guide surface-based registration algorithms in conjunction with FEM-based biomechanical models [22], [23].…”

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

“…Other AI models are able to computerise charting from radiographs, detect caries lesions and other oral lesions, such as dentigerous cysts and periapical cysts (Reyes et al, 2021). In addition, AI has been implemented in computational simulation via the finite element method to facilitate dental implant design optimisation and the prediction of success in implant dentistry (Revilla-León et al, 2021), as well as permit biomechanical simulations and tissue stress response evaluations in various medical and dental fields (Ammarullah et al, 2022; Jain et al, 2021; Phellan et al, 2021; Vurtur Badarinath et al, 2021; Xue et al, 2021).…”