KAHIBARO
40.4. Optimizing Geometry
Table of Contents
Repeated volumes
Efficient geometry design is one of the most powerful ways to speed up a GATE simulation without changing the physics. In many medical applications, especially PET, SPECT, and CT, the geometry contains many copies of identical objects, for example crystals, detector blocks, collimator holes, or phantom elements. If you define each copy as a separate volume, the geometry becomes very large and the navigation through it becomes slow. Repeated volumes solve this problem by describing a pattern once and letting Geant4 replicate it internally.
In GATE, repeated volumes are created by specifying a mother volume, a base daughter volume, and a repetition scheme. The most common schemes are linear repetitions for arrays and angular repetitions for rings, but more complex parameterizations are possible. From the perspective of your Python script, you keep a compact description: one logical definition and many placements that the kernel manages efficiently.
Conceptually, when you use repetitions, Geant4 can optimize its geometry navigation tree. The navigator can infer symmetries, reuse bounding volumes, and reduce the number of explicit placement checks. This reduces the number of geometry queries per tracking step, which is particularly important when you simulate many low-energy secondary particles that interact frequently with the detector or phantom.
A clear sign that repetitions might help is when your script contains long sections of almost identical volume definitions that differ only by position, angle, or an index. Detector crystals in a PET ring, parallel-hole collimator channels in SPECT, and regular voxel phantoms are all good candidates for repetition or parameterization rather than individual volumes.
When designing repeated volumes, it is important to separate the logical description of a component from its arrangement. Define the shape, size, and material of a basic module once. Then build higher-level objects by repeating this module. For instance, a PET scanner can be described as a crystal, grouped into a block, repeated along one axis to form a module, and then repeated around the axis to form a ring. Each repetition level reduces the number of separate logical objects that Geant4 must manage and improves performance.
You should avoid overcomplicating the repetition structure when the number of copies is small. For a handful of volumes, the complexity of a repetition setup does not bring large gains. The benefit grows with the number of instances. Always keep the geometry description readable, and only introduce repetition where it replaces many near-identical placements.
It is also important to consider scoring and identification when using repeated volumes. Geant4 can assign copy numbers to repeated volumes, and GATE digitizers can use these indices to recover detector IDs such as ring, module, or crystal numbers. This gives you both efficient navigation and compact detector identification. Proper use of copy numbers also avoids the temptation to create separate logical volumes purely to distinguish detectors for readout, which would increase complexity and reduce performance.
Repeated volumes must still respect the usual geometry rules. Overlaps between repetitions or between repeated structures and other volumes lead to navigation errors and can slow down the simulation significantly. After building a repeated geometry, you should always run the overlap checks and examine the visualization at multiple angles and zoom levels to ensure that no unintended interpenetrations occur.
You should be careful with extremely fine structures, such as very thin septa or tiny air gaps between repeated volumes. If the gap size approaches or falls below the precision of the geometry engine, you can get numerous boundary crossings, step limiters, and potential step size problems. This can counteract the performance benefit of repetition. Where possible, merge tiny gaps into a single material or simplify sub-millimeter structure that has negligible influence on the scoring of interest.
Repeated volumes become particularly powerful when combined with region-based production cuts, which are discussed elsewhere. You can define a region that contains all crystals or all phantom voxels created by repetition and assign them specific cut values. This lets you control the trade-off between accuracy and speed for very large repeated assemblies without touching each instance individually.
Finally, always validate the physical behavior of your repeated geometry. The use of repetition itself does not change the physics, but mistakes in indices, angular steps, or positions can create subtle misalignments or missing elements that affect energy deposition, sensitivity, or dose distributions. Simple test simulations with small numbers of events and dedicated actors, such as energy deposition actors in individual volumes, help confirm that the geometry behaves as intended before you run a large multithreaded job.
Voxel geometry
Voxel geometry is a special case of repeated and parameterized geometry where the volume of interest is divided into a regular 3D grid of small elements, called voxels. Each voxel can have its own material and density, which is essential for realistic simulations of patient anatomy, CT-based phantoms, and detailed dosimetry. Because voxelized models often contain millions of voxels, geometry optimization is crucial for performance.
In GATE, voxel geometry is designed so that you do not create one logical volume per voxel. Instead, you typically define a single replicated or parameterized structure that covers the full image volume. Geant4 then uses the index of each voxel in the 3D grid to derive position and material on the fly. This approach drastically reduces memory use and speeds up navigation, since the navigator only needs to determine which voxel index contains the current point rather than checking millions of separate placements.
Voxel sizes and the total number of voxels have a direct impact on performance. Smaller voxels increase spatial resolution, but they also increase the number of boundary crossings a particle will encounter as it traverses the phantom. Each crossing triggers geometry and physics checks. For dose calculations or imaging simulations, it is rarely necessary to go below the resolution of the available image data. You should align the voxel grid with the original CT or phantom resolution rather than artificially refining it unless you have a strong reason to do so.
You must also balance voxel resolution across directions. Anisotropic voxels, such as very thin slices in one direction and coarse spacing in the others, can create many boundaries in a single dimension and slow down the simulation. If possible, avoid extreme anisotropy and keep voxel dimensions reasonably similar along all axes, or at least verify that the axis with the smallest spacing does not dominate the number of steps.
Mapping CT or activity data to materials and densities is part of voxel geometry setup, and although the detailed mapping belongs in dedicated chapters, its performance aspect is important here. The number of distinct materials that appear in the voxel phantom affects performance. A large number of unique materials increases initialization time and slightly increases per-step computations. Using a small, physically meaningful set of tissue classes, created from Hounsfield ranges, is usually a good compromise between realism and performance.
When using voxel geometry, the choice of world size, world material, and placement of the voxelized phantom can also influence performance. The world should be just large enough to contain the voxel phantom and any necessary detectors or sources, with some margin to avoid boundary issues, but not excessively larger. An oversized world increases the volume that Geant4 must consider for particle tracking, especially for particles that leave the phantom.
You should pay attention to the relative position of the voxel phantom and any surrounding detectors. If a detector is extremely close to the phantom boundary, tiny gaps may lead to many rapid boundary crossings. It can be more efficient to slightly separate or merge materials at the interface if that does not affect the physics objective. Always check visualization to ensure that the phantom is centered and oriented as intended and that voxel slices match the anatomical directions of the input images.
For very large voxelized phantoms, multithreading and voxel geometry optimizations must work together. Because each worker thread builds its own copy of the geometry, a naive implementation could increase memory use considerably. GATE and Geant4 mitigate this by sharing immutable geometry descriptions across threads, but materials and voxel parameterizations still cost memory. Keeping the number of distinct voxel volumes low, reusing standard materials, and avoiding unnecessary duplicated structures around the phantom help control per-thread memory overhead.
The step size of particles inside a voxel phantom is controlled by both physics processes and geometry. In highly heterogeneous regions, such as lung near bone, some processes may impose small step sizes, which multiply with the dense grid of voxel boundaries. If you notice that your simulation spends most of its time stepping inside the phantom, you can reconsider the voxel size or partially homogenize structures that are not critical for your scoring objectives.
It is important to understand that voxel geometry is not just a representation of images but also a discretization of the scoring space. If your goal is organ-level dose, you may not need very fine voxels everywhere. In some projects, it is more efficient to use a coarser voxel phantom and define separate scoring grids that are tailored to the dose reporting needs. This separation allows you to limit voxel resolution to what is required for geometry and use specialized dose actors for high-resolution scoring in specific regions if needed.
Finally, voxel geometry must be validated both visually and numerically. Visual checks should confirm that the phantom looks correct, with anatomical structures in the expected locations and the orientation consistent with your coordinate conventions. Numerical checks, using simple attenuation or dose tests, help verify that the overall mass and composition are reasonable. If the voxel phantom is misaligned or contains incorrect materials, the simulation will still run, but the physics results will be wrong, and any performance optimizations in the geometry will not compensate for such fundamental errors.
Efficient voxel geometry uses one parameterized or replicated structure for many voxels, keeps voxel sizes close to the original image resolution, limits the number of distinct materials, and avoids unnecessary small gaps or extreme anisotropy. Always validate the mapping and alignment of voxel phantoms before running large multithreaded simulations.
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