SIRF-SIMIND-Connection

A Python wrapper for SIRF and SIMIND integration for SPECT imaging.

Quick Links

• Full Documentation
• Installation
• Backend Support - SIRF and STIR Python compatibility

Key Features

• Dual Backend Support - Works with both SIRF and STIR Python
• SIRF integrated Monte Carlo SPECT Simulation using SIMIND
• Dual Scoring Routines (SCATTWIN/PENETRATE)
• DICOM to STIR Conversion
• Advanced Schneider2000 Density Conversion - Clinically validated HU-to-density mapping with 44 tissue segments

Installation

Basic Installation

pip install sirf-simind-connection

Backend Requirements

SIRF-SIMIND-Connection requires either SIRF or STIR Python as a backend. The backend is auto-detected at runtime, with SIRF preferred if both are available. See the backend guide for details.

Option 1: STIR Python (Recommended for basic usage)

Install from conda:

conda install -c conda-forge stir "numpy<2.0"

Important: As of November 2025, the STIR conda package (v6.3.0) requires NumPy < 2.0 due to binary compatibility issues with NumPy 2.x. The package was compiled against NumPy 1.x and will crash with memory errors if NumPy 2.x is installed. This should be resolved in future STIR releases.

Or build from source:

git clone https://github.com/UCL/STIR.git
cd STIR
# Follow build instructions in the repository

Option 2: SIRF (Required for advanced features)

SIRF is required for:

• Coordinator/Projector functionality
• CIL integration
• OSEM reconstruction (examples 07-08)

Install from source:

git clone https://github.com/SyneRBI/SIRF.git
cd SIRF
# Follow build instructions in the repository

Note: SIRF includes STIR, so you don't need to install STIR separately if using SIRF.

Quick Start

from sirf_simind_connection import SimindSimulator, SimulationConfig
from sirf_simind_connection.configs import get
from sirf_simind_connection.utils.stir_utils import create_simple_phantom, create_attenuation_map

# Create phantom and attenuation map
phantom = create_simple_phantom()
mu_map = create_attenuation_map(phantom)

# Load pre-configured scanner settings
config = SimulationConfig(get("AnyScan.yaml"))
simulator = SimindSimulator(config, output_dir='output')

# Set inputs and run
simulator.set_source(phantom)
simulator.set_mu_map(mu_map)
simulator.set_energy_windows([126], [154], [0])  # Tc-99m ± 10%
simulator.run_simulation()

# Access results as native SIRF/STIR objects when needed
native_outputs = simulator.get_outputs(native=True)
sirf_tot = simulator.get_total_output(native=True)

Advanced Density Conversion

from sirf_simind_connection.converters.attenuation import hu_to_density_schneider
import numpy as np

# Convert HU image to densities using Schneider2000 model
hu_image = np.array([[-1000, 0, 500], [800, 1200, 2000]])
density_map = hu_to_density_schneider(hu_image)  # 44-segment clinical model

Contributing

Please see our Contributing Guide.

License

Apache License 2.0