Three-Dimensional Imaging Techniques in Aesthetic Breast Surgery
Summary
Three-dimensional imaging has transformed aesthetic breast surgery by providing objective, reproducible measures of shape, volume and symmetry. Techniques such as structured-light stereophotogrammetry, laser surface scanning and multi-view photogrammetric reconstruction enable rapid acquisition of external breast contours without ionising radiation. These methods support precise preoperative planning, patient consultation and postoperative assessment, enhancing shared decision-making and surgical accuracy. Advanced software algorithms simulate postoperative outcomes, allowing surgeons and patients to visualise implant size, position and breast morphology in real time. In reconstructive contexts, serial 3D images quantify dynamic changes in flap volume and surface contour, informing timing of revisions and long-term outcome stability. Artificial-intelligence-driven tools, including generative neural networks, are emerging to automate image registration and predictive simulation. Customised intraoperative aids, such as patient-specific 3D-printed moulds, further bridge digital planning with surgical execution. Collectively, these approaches have global significance, standardising outcome evaluation across institutions and facilitating comparative research into implant designs, surgical techniques and postoperative therapies.
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Three-Dimensional Imaging Techniques in Aesthetic Breast Surgery publication trend
The graph below shows the total number of articles in three-dimensional imaging techniques in aesthetic breast surgery across all publications each year (not limited to Nature Index journals).
Technical terms
Structured-light stereophotogrammetry: A non-contact imaging method projecting patterned light onto the breast to capture surface geometry from multiple angles.
Volumetric measurement: Calculation of breast or flap volume from a 3D surface mesh by simulating an underlying chest wall.
Digital anthropometry: The automated or semi-automated measurement of anatomical distances and angles from digital images.
Generative neural network: A type of artificial-intelligence model that learns from examples to produce new, realistic images or simulations.
3D-printed moulds: Patient-specific intraoperative templates fabricated from 3D surface scans to guide tissue shaping and implant positioning.
References
- BreastGAN: Artificial Intelligence-Enabled Breast Augmentation Simulation. Aesthetic Surgery Journal Open Forum (2021).
- “Three-dimensional evaluation of breast volume changes following autologous free flap breast reconstruction over six months”. The Breast (2020).
- New software and breast boundary landmarks to calculate breast volumes from 3D surface images. European Journal of Plastic Surgery (2018).
- Applications and limitations of using patient-specific 3D printed molds in autologous breast reconstruction. European Journal of Plastic Surgery (2018).
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