blochsimulator 1.0.4

High-performance Bloch equation simulator for MRI

Bloch Equation Simulator for Python

A high-performance Python implementation of the Bloch equation solver originally developed by Brian Hargreaves at Stanford University. This package provides a fast C-based core with Python bindings, parallel processing support, and an interactive GUI for MRI pulse sequence simulation.

Features

• High Performance: C implementation with Cython bindings maintains original speed
• Parallel Processing: OpenMP support for multi-core acceleration
• Interactive GUI: Real-time visualization and parameter adjustment
• Flexible API: Easy-to-use Python interface for scripting
• Comprehensive: Supports arbitrary RF pulses, gradient waveforms, and tissue parameters
• Visualization: 3D magnetization vectors, time evolution plots, frequency spectra, and animation export

Installation

Prerequisites

1. Python 3.7+
2. C Compiler:
   • Linux: gcc (usually pre-installed)
   • macOS: Xcode Command Line Tools (xcode-select --install)
   • Windows: Visual Studio Build Tools
3. OpenMP (optional but recommended):
   • Linux: Included with gcc
   • macOS: brew install libomp
   • Windows: Included with Visual Studio

Quick Setup

To install the simulator for local use or development:

# Clone or download the repository
cd blochsimulator

# Install the package in editable mode
# This builds the C-extension automatically
pip install -e .

Verification

Test the installation:

from blochsimulator import BlochSimulator, TissueParameters

sim = BlochSimulator()
tissue = TissueParameters.gray_matter(3.0)
print(f"T1: {tissue.t1:.3f}s, T2: {tissue.t2:.3f}s")

Usage

🚀 Jupyter Notebook (Recommended)

You can launch the interactive GUI directly from a cell in your Jupyter Notebook.

# 1. Install (run once)
!pip install -e .

# 2. Launch GUI
!blochsimulator-gui

Note: This works if Jupyter is running on your local machine. It will not work on headless remote servers or Google Colab.

1. GUI Application

Once installed, you can launch the GUI from any terminal or shell:

blochsimulator-gui

Features:

• Design RF pulses (rectangular, sinc, Gaussian)
• Configure tissue parameters (T1, T2, proton density)
• Select pulse sequences (spin echo, gradient echo, etc.)
• Real-time 3D magnetization visualization
• Signal analysis and frequency spectra

2. Python API

Basic Simulation

import numpy as np
from blochsimulator import BlochSimulator, TissueParameters

# Create simulator
sim = BlochSimulator(use_parallel=True, num_threads=4)

# Define tissue parameters
tissue = TissueParameters(
    name="Gray Matter",
    t1=1.33,  # seconds
    t2=0.083  # seconds
)

# Create a simple 90-degree pulse
ntime = 100
dt = 1e-5  # 10 microseconds
time = np.arange(ntime) * dt

b1 = np.zeros(ntime, dtype=complex)
b1[0] = 0.0235  # 90-degree hard pulse

gradients = np.zeros((ntime, 3))  # No gradients

# Run simulation
result = sim.simulate(
    sequence=(b1, gradients, time),
    tissue=tissue,
    mode=2  # Time-resolved output
)

# Plot results
sim.plot_magnetization()

Spin Echo Sequence

from blochsimulator import BlochSimulator, SpinEcho, TissueParameters

sim = BlochSimulator()

# Create spin echo sequence
sequence = SpinEcho(te=20e-3, tr=500e-3)  # 20ms TE, 500ms TR

# Simulate white matter
tissue = TissueParameters.white_matter(3.0)

# Run simulation with multiple frequencies (T2* effects)
frequencies = np.linspace(-50, 50, 11)  # Hz
result = sim.simulate(sequence, tissue, frequencies=frequencies)

# Access magnetization components
mx, my, mz = result['mx'], result['my'], result['mz']
signal = result['signal']

Custom Pulse Design

from blochsimulator import design_rf_pulse

# Design a sinc pulse
b1, time = design_rf_pulse(
    pulse_type='sinc',
    duration=2e-3,      # 2 ms
    flip_angle=180,     # degrees
    time_bw_product=4,  # Time-bandwidth product
    npoints=200
)

# Apply phase
phase = np.pi/4  # 45 degrees
b1_phased = b1 * np.exp(1j * phase)

Parallel Simulation

# Simulate multiple positions and frequencies in parallel
positions = np.random.randn(100, 3) * 0.01  # Random positions in 1cm cube
frequencies = np.linspace(-200, 200, 41)     # 41 frequencies

result = sim.simulate(
    sequence=sequence,
    tissue=tissue,
    positions=positions,
    frequencies=frequencies,
    mode=0  # Endpoint only (faster)
)

# Result shape: (100 positions, 41 frequencies)
print(f"Signal shape: {result['signal'].shape}")

3. Sequence Library

Pre-defined sequences are available:

from blochsimulator import SpinEcho, GradientEcho

# Spin Echo
se = SpinEcho(te=30e-3, tr=1.0)

# Gradient Echo  
gre = GradientEcho(te=5e-3, tr=10e-3, flip_angle=30)

# Compile to waveforms
b1, gradients, time = se.compile(dt=1e-6)

4. Tissue Parameter Library

Common tissues at different field strengths:

from blochsimulator import TissueParameters

# 3T parameters
gm = TissueParameters.gray_matter(3.0)
wm = TissueParameters.white_matter(3.0)
csf = TissueParameters.csf(3.0)

# 7T parameters
gm_7t = TissueParameters.gray_matter(7.0)

# Custom tissue
liver = TissueParameters(
    name="Liver",
    t1=0.812,
    t2=0.042,
    t2_star=0.028,
    density=0.9
)

Desktop app build (PyInstaller)

One build per OS is required (macOS build won’t run on Windows/Linux).

Prereqs

• macOS: Xcode CLT; brew install libomp.
• Windows: Python 3.8+ and MSVC Build Tools (for C extension).
• Linux: gcc/g++; ensure libgomp available.

Quick build (any OS)

python -m pip install -r requirements.txt
python -m pip install pyinstaller
python setup.py build_ext --inplace
PYINSTALLER_CONFIG_DIR=.pyinstaller pyinstaller bloch_gui.spec --noconfirm

Artifact: dist/BlochSimulator (single binary; .exe on Windows).

One-liner helper

./scripts/build_pyinstaller.sh   # creates a venv, installs deps, builds, packages

Run the packaged app

• macOS/Linux: ./dist/BlochSimulator
• Windows: dist\\BlochSimulator.exe

Runtime data/exports

• rfpulses/ is bundled automatically.
• Exports default to per-user data dirs:
  • macOS: ~/Library/Application Support/BlochSimulator/exports
  • Windows: %APPDATA%\\BlochSimulator\\exports
  • Linux: ~/.local/share/BlochSimulator/exports
• Override with BLOCH_APP_DIR or BLOCH_EXPORT_DIR if you need a custom location.

Theory

The simulator solves the Bloch equations:

dM/dt = γ(M × B) - [Mx/T2, My/T2, (Mz-M0)/T1]

Using:

• Rotation matrices for RF and gradient effects
• Exponential decay for relaxation
• Cayley-Klein parameters for efficient rotation calculation

Troubleshooting

Build Issues

1. Missing compiler: Install gcc (Linux), Xcode (macOS), or Visual Studio (Windows)
2. OpenMP not found: The code will still work but without parallelization
3. Import error: Ensure the .so/.pyd file is in the same directory

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

Citation

If you use this simulator in your research, please cite:

@software{blochsimulator_python,
  title={Python Bloch Equation Simulator GUI and API},
  author={Luca Nagel},
  year={2025},
  url={https://github.com/LucaNagel/bloch_sim_gui}
}

Acknowledgments

This project is based on code originally developed by Brian Hargreaves at Stanford University. Currently (11/2025) it is unfortunately not available. A python adaption of this code can be found here.

• Original Bloch simulator by Brian Hargreaves, Stanford University
• NumPy and SciPy communities
• PyQt/PySide developers
• OpenMP project
• Built partially with codex, claude code and gemini cli

Contact

Luca Nagel

Appendix: File Structure

blochsimulator/
├── src/
│   └── blochsimulator/
│       ├── __init__.py
│       ├── simulator.py            # Core Python API
│       ├── gui.py                  # PyQt5 GUI
│       ├── bloch_core_modified.c   # C implementation
│       ├── bloch_core.h            # C header
│       ├── bloch_wrapper.pyx       # Cython wrapper
│       └── ...
├── tests/                          # Unit tests
├── docs/                           # Sphinx documentation
├── pyproject.toml                  # Modern build config
├── setup.py                        # C-extension build config
├── MANIFEST.in                     # Source dist manifest
└── README.md