Diffuse optical tomography (DOT) is a volumetric optical imaging technique that relies on modeling light transport in tissue using the diffusion approximation, which is generally applicable in scatter dominated systems. The spectral measure of the diffuse transport of near-infrared light through soft tissue can provide the ability to image functional tissue information such as hemoglobin oxygenation and water fraction, which can be useful as a noninvasive means of identifying cancer.1–3 This method has also been proven successful by the use of luminescence probes using, for example, fluorescence markers to allow quantitative molecular imaging of functional exogenous reporters.4,5 Light modeling can be done analytically,6 providing high accuracy and computational speed, but only on simple and dominantly homogeneous geometries. Numerical approaches allow solutions to be computed for more complex geometries, but require more computational time as well as a discrete representation (volume mesh) of the domain.7,8 Due to the generally poor spatial resolution of DOT, the prevailing trend in the field is toward combining it with other imaging modalities and incorporating high-resolution tissue structural information in the image recovery algorithm. Notable examples of this include computed tomography (CT) or magnetic resonance imaging (MRI)-guided DOT, and these techniques provide the potential for increased accuracy.9–12 Although the details of finite-element-based methods for modeling light transport in tissue are well covered in literature,13–21 the computational packages available for such modeling have until now included quite limited mesh creation tools or no mesh creation tools at all. In this work, an integrated and freely available software package is outlined and tested, which allows users to go all the way from import of standard digital imaging and communications in medicine (DICOM) images (and other related formats) to segmentation and meshing, and through to light simulation and property recovery. Image-guided DOT is very dependent on the ability to easily produce high-quality three-dimensional (3-D) volume meshes from medical images, and the process of mesh creation is a significantly underappreciated but complex issue, which is directly solved in many cases by software such as this.