RadFiled3D 1.0.4

# RadFiled3D

RadFiled3D

This Repository holds the Fileformat and API according to the Paper: "RadField3D: A Data Generator and Data Format for Deep Learning in Radiation-Protection Dosimetry for Medical Applications".

The aim of this library is, to provide a simple to use API for a structured, binary file format, that can store all relevant information from a three dimensional radiation field calculated by applications that use algorithms like Monte-Carlo radiation transport simulations. Such a binary file format is useful, when one needs to process a huge amount of radiation field files like when training a neural network. With that use-case in mind, RadFiled3D also provides a python interface with a pyTorch integration.

Building and Installing

Installing from PyPi

Prebuilt versions of this module for python 3.11 and 3.12 for Windows and most Linuxsystems can be installed directly by using pip.

pip install RadFiled3D

Installing from Source

You can build and install this library and python module from source by using CMake and a C++ compiler. The CMake Project will be built automatically, but will take some time.

Prerequisites

• C++ Compiler
  • g++ or clang for Linux
  • MSVC or clang from Visual Studio 2022 for Windows
• CMake >= 3.30
• Python >= 3.11

CMake

In order to use the module directly from another C++ Project, you can integrate it by adding the local location of this repository via add_submodule() and then link against the target libRadFiled3D. All classes are then available from the namespace RadFiled3D. Check the Example or the First Test File as a first reference.

Python

In order to use the Module from Python, we provide a setup.py file that handles the compilation and integration automatically from the python setuptools.

Installing locally

python -m pip install .

Building a wheel

python -m build --wheel

Getting Started

From C++

Simple example on how to create and store a radiation field. Find more in the example file: Example

#include <RadFiled3D/storage/RadiationFieldStore.hpp>
#include <RadFiled3D/RadiationField.hpp>

using namespace RadFiled3D;
using namespace RadFiled3D::Storage;

void main() {
    auto field = std::make_shared<CartesianRadiationField>(glm::vec3(2.5f), glm::vec3(0.05f)); // field extents: 2.5 m x 2.5 m x 2.5 m and voxel extents: 5 cm x 5 cm x 5 cm

    auto metadata = std::make_shared<RadFiled3D::Storage::V1::RadiationFieldMetadata>(
        // learn about the existing data fields from the example file in ./examples/cxx/examples01.cpp
    )

    FieldStore::store(field, metadata, "test_field.rf3", StoreVersion::V1);

    auto field2 = FieldStore::load("test_field.rf3");
}

From Python

Simple example on how to create and store a radiation field. Find more in the example file: Example

from RadFiled3D.RadFiled3D import CartesianRadiationField, FieldStore, StoreVersion, DType

# Creating a cartesian radiation field
field = CartesianRadiationField(vec3(2.5, 2.5, 2.5), vec3(0.05, 0.05, 0.05))
# defining a channel and a layer on it
field.get_channel("channel1").add_layer("layer1", "unit1", DType.FLOAT32)

# accessing the voxels by using numpy arrays
array = field.get_channel("channel1").get_layer_as_ndarray("layer1")
assert array.shape == (50, 50, 50)
# modify voxels content by using numpy array as no data is copied, just referenced
array[2:5, 2:5, 2:5] = 2.0

# addressing a voxel by providing a point in space
voxel = field.get_channel("channel1").get_voxel_by_coord("layer1", 0.1, 2.4, 5)

# Store changes to a file
metadata = RadiationFieldMetadataV1(...)
FieldStore.store(field, metadata, "test01.rf3", StoreVersion.V1)

# load data
field2 = FieldStore.load("test01.rf3")
metadata2 = FieldStore.load_metadata("test01.rf3")

Integrating with pyTorch

RadFiled3D comes with a submodule at RadFiled3D.pytorch. This module provides some dataset classes to support the usage. Datasets can be loaded from folders or .zip-Files.

from RadFiled3D.pytorch import MetadataLoadMode, CartesianFieldSingleLayerDataset, DatasetBuilder
from RadFiled3D.pytorch.helpers import load_tensor_from_layer
from RadFiled3D.RadFiled3D import VoxelGrid
from torch import Tensor


# Extend one of the provided dataset classes to match the output to the current needs
# The argument type of 'field' may vary depending on the dataset type between RadiationField (Whole field), VoxelGridBuffer (Channel), VoxelGrid (Layer) and Voxel (Single Voxel)
# The argument idx will contain the requested index from the dataset just in case someone wants to alter the return value based on it.
class MyLayerDataset(CartesianFieldSingleLayerDataset):
    def transform_field(self, field: VoxelGrid, idx: int) -> Tensor:
        return load_tensor_from_layer(field)


# Pass the dataset class and other options to the DatasetBuilder
builder = DatasetBuilder(
    "./test_dataset.zip",
    train_ratio=0.7,
    val_ratio=0.15,
    test_ratio=0.15,
    dataset_class=MyLayerDataset
)
# Build and finalize the training dataset
train_ds = builder.build_train_dataset()
# define the channel and layer to load from each field and spare out all other data
train_ds.set_channel_and_layer("test_channel", "test_layer")
# Load the metadata header for each radiation field, but not the dynamic metadata to speed up the loading
train_ds.metadata_load_mode = MetadataLoadMode.HEADER

# iterate over the dataset
for field, metadata in train_ds:
    pass

Field Structure

RadFiled3D defines a field structure, that provides the user with the possibility to first define in which kind of space he wants to operate. Therefore one can choose between CartesianRadiationField and PolarRadiationField.

• CartesianRadiationField: Segments a room defined by an extent of the room itself and each cuboid voxel into a set of voxels. Each voxel can be addressed by a 3D position (coordinate: x, y, z), a 3D index (number of the voxel in each dimension) or a flat 1D index.
• PolarRadiationField: Segements the surface of a unit sphere into surface segments. Each segment (voxel) can be addressed by a 2D position (coordinate: theta, phi), a 2D index (number of the segment in each dimension) or a flat 1D index.

Fields are then partitioned into channels (VoxelGridBuffer/PolarSegmentsBuffer). All channels share the same size and resolution. A channel is again partitioned into layers (VoxelGrid/PolarSegment). Each layer holds the actual voxel data and can be constructed from various data types (int, float, double, uint32_t, uint64_t, glm::vec2, glm::vec3, glm::vec4, N-D-Histogram). Additionally, a layer has a unit string assigned to it as well as a statistical uncertainty to perserve those information.

Dependencies

RadFiled3D comes with a possibly low amount of dependencies. We integrated the OpenGL Math Library (GLM) just to provide those datatypes out of the box and as GLM is a head-only library we suspect no issues by doing so.

All C++ dependencies:

• GLM

All python dependencies:

• PyBind11
• rich
• numpy
• Optional:
  • pyTorch