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Anthropomorphic and zoomorphic phantoms with integrated tumor tissues


In our simulations packages such as Musiré, a single object description (phantom, atlas) can be used for the simulation of all imaging modalities. Whereas voxelized phantoms are generally employed for SPECT, PET, CT and MRI simulations, mesh-based representation of anatomical structures is preferred for the simulation of optical photons (BLI, FMI) since the imaged object's boundary contributes crucial information on the emitted photon flux which constitutes a decisive factor for image reconstruction or inverse light field mapping accuracy.

Our phantoms include (i) the three-dimensional (x, y, z) voxelized tissue/material atlas encoded as MetaIO (mhd) and (ii) the three-dimensional polygonal (triangle) mesh for tissue atlas geometry representing general anatomy (and objects) encoded in the Stanford triangle format (ply). Our programs are also compatible with three-dimensional point clouds encoded as ply. A multitude of meshes and point clouds can be encapsulated within a MeshLab project (mlp) container. From these, our frameworks automatically convert the object description into the appropriate format for every simulator. The mechanism of passing parameter files or scripts also allows for use of simulator-specific phantom representation models. Physical attributes such as the definition of materials, densities, source activities, dye concentrations, relaxation times and others are assigned to mhd or ply represented atlas regions by simple look-up table (dat) files. Point clouds are intended for representing (multi-lesion) heterogeneous tumour cell distributions such as generated by biologically derived models for tumour growth dynamics. Anatomical phantoms can be extended with arbitrarily placeable tumour inserts. These inserts can represent arbitrarily solid, multi-lesion, necrotic or heterogeneous entities with millions of cells per cubic centimetre of tissue. Depending on the simulated imaging modality, point clouds can be used at their native resolution. This is implemented for optical imaging where bioluminescence light distribution is sampled directly from the spatial tumour cell distribution. However, considering the size of tumour cells to be of the order of 10 μm to 50 μm it would be inefficient to use this degree of spatial resolution, e.g. in nuclear imaging simulation. Hence, point cloud tumour inserts are being down-sampled automatically for other modalities into the spatial resolution of the host phantom. By employing specific command-line options tumour cell density per voxel can be scaled into a source activity range or further physical material entities, such as T1 and T2 relaxation times.

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