Skin deformation analysis for pre-operative planning of DIEAP flap reconstruction surgery

Deep inferior epigastric artery perforator (DIEAP) flap reconstruction surgeries can potentially benefit from augmented reality (AR) in the context of surgery planning and outcomes improvement. Although three-dimensional (3D) models help visualize and map the perforators, the anchorage of the models to the patient's body during surgery does not consider eventual skin deformation from the moment of computed tomography angiography (CTA) data acquisition until the position of the patient while in surgery. In this work, we compared the 3D deformation registration from supine arms down (CTA position) to supine with arms at 90 degrees degrees (surgical position), estimating the patient's skin deformation. We processed the data sets of 20 volunteers with a 3D rigid registration tool and performed a descriptive statistical analysis and statistical inference. With 2.45 mm of root mean square and 2.89 mm of standard deviation, results include 30% cases of deformation above 3 mm and 15% above 4 mm. Pose transformation deformation indicates that 3D surface data from the CTA scan po-sition differs from data acquired in loco at the surgical table. Such results indicate that research should be conducted to construct accurate 3D models using CTA data to display on the patient, while considering pro-jection errors when using AR technology.

Automated summary

Augmented reality (AR) can be beneficial for deep inferior epigastric artery perforator (DIEAP) flap reconstruction surgeries in terms of surgery planning and outcomes improvement. However, the anchorage of three-dimensional (3D) models to the patient's body during surgery does not consider skin deformation, leading to mismatch between the models and the patient. This study compares the 3D deformation registration from computed tomography angiography (CTA) position to surgical position and estimates the patient's skin deformation. The results show that there are cases of significant deformation, indicating the need for accurate 3D models using CTA data and considering projection errors when using AR technology.