 Electron beam dosimetry depends on both the patient's surface contour and tissue heterogeneity. Traditionally, clinical electron treatments are often calculated without any volumetric imaging through the use of tables or spreadsheets that assume a flat patient surface and a homogeneous, water-equivalent tissue. These calculations do not account for shape changes, which can create significant local dose variations for treatments of the nose, ear, or lips. To obtain more accurate dosimetry, a treatment-planning CT scan can be performed. However, these scans can be costly, expose patients to unnecessary ionizing radiation, and add extra time and resources for radiation therapy staff. This led us to investigate the feasibility of using a 3D camera to capture the patient's body surface contours for improved electron dose calculations in a quick and non-invasive manner. First, to assess camera spatial accuracy, in this clinical scenario, a CT scan of a head phantom was acquired and imported into a treatment-planning system, and a body contour was automatically generated. Conversely, the 3D camera scans were obtained of the same phantom, and the two surfaces were registered together using a least square sums algorithm. This method assumes that the body structure derived from a CT scan is the true reference. Once registered, a histogram distribution of misalignment between the 3D surface and the CT surface was generated. As one can see, the 3D camera and the CT scan surfaces were found to have 95% of the ports, which were within 1.2 mm of the two body surfaces. To evaluate any improvement on dose calculation, six clinical cases were investigated and compared using three approaches to calculation. One, our ground truth, a full CT scan which contains both surface contour and heterogeneity information. Second, a flat water phantom representing a traditional clinical calculation, which lacks both surface and heterogeneity information. And finally, three, a CT scan with a ounce of unit inside the body set to zero to represent the output of our camera, which does account for surface variation, but not heterogeneities. When comparing the monitor units needed to achieve clinical coverage, our 3D camera based calculations were on average within 1.3% when compared to calculations based on the CT scan, and generally hotspots were modeled very well. In contrast, using a flat phantom for calculations greatly underestimated hotspots with a mean difference of 4.5% and a large standard deviation. Likewise, the monitor unit differences needed to achieve clinical coverage was also much greater and with a larger standard deviation. Next, we'll briefly demonstrate the workflow and use of our system. As in traditional electron simulations, the physician's intended target is marked on the patient's face. Then, 3D images can be captured by moving a 3D camera about the patient. This can be conveniently done in most clinic spaces as the absolute alignment is not important. Finally, the 3D patient image and physician's demarcations are post-processed and imported into the treatment planning system as separate structures. An electron cutout aperture can now be designed to conform to the patient's marks. Before calculating dose, the body structure needs to be assigned a material or ounce field unit representing water or tissue. If desired, a complex bolus can be added and designed here as well. And finally, dose can be calculated and the dose distribution evaluated by the physician for a more realistic representation of what will be delivered to the patient. Thank you for your interest.