A Computational Tool for Pre-operative Breast Augmentation Planning in Aesthetic Plastic Surgery

Abstract—Breast augmentation was the most commonly performed cosmetic surgery procedure in 2011 in the United States. Although aesthetically pleasing surgical results can only be achieved if the correct breast implant is selected from a large variety of different prosthesis sizes and shapes available on the market, surgeons still rely on visual assessment and other subjective approaches for operative planning because of lacking objective evaluation tools. In this paper we present the development of a software prototype for augmentation mammaplasty simulation solely based on 3D surface scans, from which patient-specific finite element models are generated in a semi-automatic process. The finite element model is used to pre-operatively simulate the expected breast shapes using physical soft tissue mechanics. Our approach uses a novel mechanism based on so-called displacement templates, which, for a specific implant shape and position, describe the respective internal body forces. Due to a highly efficient numerical solver we can provide immediate visual feedback of the simulation results, and thus the software prototype can be integrated smoothly into the medical workflow. The clinical value of the developed 3D computational tool for aesthetic breast augmentation surgery planning is demonstrated in patientspecific use cases

A patient-specific FE-based methodology to simulate prosthesis insertion during an augmentation mammoplasty

[EN] Breast augmentation surgery is a widespread practice for aesthetic purposes. Current techniques, however, are not able to reliably predict the desired final aspect of the breast after the intervention, whose success relies almost completely on the surgeon's skill. In this way, patient-specific methodologies capable of predicting the outcomes of such interventions are of particular interest. In this paper, a finite element biomechanical model of the breast of a female patient before an augmentation mammoplasty was generated using computer tomography images. Prosthesis insertion during surgery was simulated using the theory of finite elasticity. Hyperelastic constitutive models were considered for breast tissues and silicone implants. The deformed geometry obtained from finite element analysis was compared qualitatively and quantitatively with the real breast shape of the patient lying in supine position, with root-mean-squared errors less than 3. mm. The results indicate that the presented methodology is able to reasonably predict the aspect of the breast in an intermediate step of augmentation mammoplasty, and reveal the potential capabilities of finite element simulations for visualization and prediction purposes. However, further work is required before this methodology can be helpful in aesthetic surgery planning. © 2011 IPEM.The support of Instituto de Salud Carlos III (ISCIII) through the CIBER initiative, and the support of Platform for Biological Tissue Characterization of the Centro de Investigacion Biomedica en Red de Bioingenieria, Biomateriales y Nanomedicina (CIBER-BBN) are highly appreciated. The translation of this paper was funded by the Universitat Politecnica de Valencia, Spain.Lapuebla-Ferri, A.; Perez Del Palomar, A.; Herrero, J.; Jimenez Mocholi, AJ. (2011). A patient-specific FE-based methodology to simulate prosthesis insertion during an augmentation mammoplasty. Medical Engineering & Physics. 33(9):1094-1102. https://doi.org/10.1016/j.medengphy.2011.04.014S1094110233

Multiscale Mechano-Biological Finite Element Modelling of Oncoplastic Breast Surgery-Numerical Study towards Surgical Planning and Cosmetic Outcome Prediction

Surgical treatment for early-stage breast carcinoma primarily necessitates breast conserving therapy (BCT), where the tumour is removed while preserving the breast shape. To date, there have been very few attempts to develop accurate and efficient computational tools that could be used in the clinical environment for pre-operative planning and oncoplastic breast surgery assessment. Moreover, from the breast cancer research perspective, there has been very little effort to model complex mechano-biological processes involved in wound healing. We address this by providing an integrated numerical framework that can simulate the therapeutic effects of BCT over the extended period of treatment and recovery. A validated, three-dimensional, multiscale finite element procedure that simulates breast tissue deformations and physiological wound healing is presented. In the proposed methodology, a partitioned, continuum-based mathematical model for tissue recovery and angiogenesis, and breast tissue deformation is considered. The effectiveness and accuracy of the proposed numerical scheme is illustrated through patient-specific representative examples. Wound repair and contraction numerical analyses of real MRI-derived breast geometries are investigated, and the final predictions of the breast shape are validated against post-operative follow-up optical surface scans from four patients. Mean (standard deviation) breast surface distance errors in millimetres of 3.1 (±3.1), 3.2 (±2.4), 2.8 (±2.7) and 4.1 (±3.3) were obtained, demonstrating the ability of the surgical simulation tool to predict, pre-operatively, the outcome of BCT to clinically useful accuracy

Development and implementation of a web-enabled 3D consultation tool for breast augmentation surgery based on 3D-image reconstruction of 2D pictures

Producing a rich, personalized Web-based consultation tool for plastic surgeons and patients is challenging

Towards an in-plane methodology to track breast lesions using mammograms and patient-specific finite-element simulations

In breast cancer screening or diagnosis, it is usual to combine different images in order to locate a lesion as accurately as possible. These images are generated using a single or several imaging techniques. As x-ray-based mammography is widely used, a breast lesion is located in the same plane of the image (mammogram), but tracking it across mammograms corresponding to different views is a challenging task for medical physicians. Accordingly, simulation tools and methodologies that use patient-specific numerical models can facilitate the task of fusing information from different images. Additionally, these tools need to be as straightforward as possible to facilitate their translation to the clinical area. This paper presents a patient-specific, finite-element-based and semi-automated simulation methodology to track breast lesions across mammograms. A realistic three-dimensional computer model of a patient''s breast was generated from magnetic resonance imaging to simulate mammographic compressions in cranio-caudal (CC, head-to-toe) and medio-lateral oblique (MLO, shoulder-to-opposite hip) directions. For each compression being simulated, a virtual mammogram was obtained and posteriorly superimposed to the corresponding real mammogram, by sharing the nipple as a common feature. Two-dimensional rigid-body transformations were applied, and the error distance measured between the centroids of the tumors previously located on each image was 3.84 mm and 2.41 mm for CC and MLO compression, respectively. Considering that the scope of this work is to conceive a methodology translatable to clinical practice, the results indicate that it could be helpful in supporting the tracking of breast lesions

A physically based trunk soft tissue modeling for scoliosis surgery planning systems

One of the major concerns of scoliotic patients undergoing spinal correction surgery is the trunk's external appearance after the surgery. This paper presents a novel incremental approach for simulating postoperative trunk shape in scoliosis surgery. Preoperative and postoperative trunk shapes data were obtained using three-dimensional medical imaging techniques for seven patients with adolescent idiopathic scoliosis. Results of qualitative and quantitative evaluations, based on the comparison of the simulated and actual postoperative trunk surfaces, showed an adequate accuracy of the method. Our approach provides a candidate simulation tool to be used in a clinical environment for the surgery planning process.IRSC / CIH

Multi-Modality Breast MRI Segmentation Using nn-UNet for Preoperative Planning of Robotic Surgery Navigation

Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first architecture has a 0.95 DCS, while the second has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively

A review of bioengineering techniques applied to breast tissue: Mechanical properties, tissue engineering and finite element analysis

Female breast cancer was the most prevalent cancer worldwide in 2020, according to the Global Cancer Observatory. As a prophylactic measure or as a treatment, mastectomy and lumpectomy are often performed at women. Following these surgeries, women normally do a breast reconstruction to minimize the impact on their physical appearance and, hence, on their mental health, associated with self-image issues. Nowadays, breast reconstruction is based on autologous tissues or implants, which both have disadvantages, such as volume loss over time or capsular contracture, respectively. Tissue engineering and regenerative medicine can bring better solutions and overcome these current limitations. Even though more knowledge needs to be acquired, the combination of biomaterial scaffolds and autologous cells appears to be a promising approach for breast reconstruction. With the growth and improvement of additive manufacturing, three dimensional (3D) printing has been demonstrating a lot of potential to produce complex scaffolds with high resolution. Natural and synthetic materials have been studied in this context and seeded mainly with adipose derived stem cells (ADSCs) since they have a high capability of differentiation. The scaffold must mimic the environment of the extracellular matrix (ECM) of the native tissue, being a structural support for cells to adhere, proliferate and migrate. Hydrogels (e.g., gelatin, alginate, collagen, and fibrin) have been a biomaterial widely studied for this purpose since their matrix resembles the natural ECM of the native tissues. A powerful tool that can be used in parallel with experimental techniques is finite element (FE) modeling, which can aid the measurement of mechanical properties of either breast tissues or scaffolds. FE models may help in the simulation of the whole breast or scaffold under different conditions, predicting what might happen in real life. Therefore, this review gives an overall summary concerning the human breast, specifically its mechanical properties using experimental and FE analysis, and the tissue engineering approaches to regenerate this particular tissue, along with FE models

Biomechanical properties of breast tissue, a state-of-the-art review

This paper reviews the existing literature on the tests used to determine the mechanical properties of women breast tissues (fat, glandular and tumour tissue) as well as the different values of these properties. The knowledge of the mechanical properties of breast tissue is important for cancer detection, study and planning of surgical procedures such as surgical breast reconstruction using pre-surgical methods and improving the interpretation of clinical tests. Based on the data collected from the analysed studies, some important conclusions were achieved: (1) the Young’s modulus of breast tissues is highly dependent on the tissue preload compression level, and (2) the results of these studies clearly indicate a wide variation in moduli not only among different types of tissue but also within each type of tissue. These differences were most evident in normal fat and fibroglandular tissues