Barnes Lab

Focus

Parametric MRI + DCE-MRI processing

Environment

MATLAB-based workflows

Acceleration

Optional NVIDIA GPU via Gpufit

License

GPL-2.0

• Processes parametric MRI and DCE-MRI files, originally developed at the Biological Imaging Center at Caltech.
• Designed to keep data prep, model fitting, and review steps in a single workflow.

Reference: Barnes et al., “ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic contrast-enhanced MRI studies,” BMC Medical Imaging (2015).

Focus

CUDA accelerated Levenberg-Marquardt curve fitting

Environment

CUDAs

Bindings

Python, MATLAB, Java

License

MIT

• Fork with updated CMake build system for modern compilers and Linux compatibility.
• Approximately 40x faster than the compiled CPU-only implementation and >1000x faster than interpreted MATLAB-based alternatives.
• Added support for bounded fitting and numerical differentiation.

Reference: Przybylski et al., “Gpufit: An open-source toolkit for GPU-accelerated curve fitting,” Scientific Reports (2017).

Focus

Automatic AIF detection for DCE-MRI processing

Environment

Keras/TensorFlow

Model

UNet with pretrained weights

License

MIT

• Keras/TensorFlow implementation for automatic AIF detection in DCE-MRI data and ROCKETSHIP workflows.
• Inference predicts an AIF curve and 3D mask with automated normalization and resampling steps.
• Includes training and inference scripts plus a pretrained model.

Reference: Saca et al., “Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts,” Magnetic Resonance in Medicine (2025).

Focus

DCE-MRI preprocessing + Ktrans mapping

Environment

Python + Docker (FSL, ANTs, FreeSurfer)

Reporting

Automated HTML case + population reports

License

MIT

• Combines VFA-based T1 mapping, bias-field and motion correction (FSL mcflirt), and Ktrans parameter mapping in a single pipeline.
• Neural-network AIF selection with z-axis normalization and gray matter/CSF masking.
• BIDS-compliant data organization and Docker containerization for reproducible processing.

Reference: publication pending (2026), a multi-site aging study of blood-brain barrier permeability.

Focus

Manual AIF ROI annotation for DCE-MRI

Environment

Python + napari

Output

BIDS-style derivatives

License

MIT

• Draw and edit 3D ROIs on BIDS 4D NIfTI data with real-time mean signal-intensity curves and optional normalization.
• Built for high-volume multi-rater review, with rater identifiers embedded in BIDS-derivative filenames.
• Workflow features include auto-queue resumption, image prefetching, and flagging of poor-quality data.

Focus

Abdominal SAT/VAT segmentation

Environment

PyTorch

Models

ACD 3D U-Net + 3D nnU-Net

License

Academic Software License

• Full-volume multi-contrast inputs stack opposed-phase, water, and fat images as channels for volumetric segmentation.
• Outputs SAT, VAT, and background masks for adipose tissue quantification.
• Includes preprocessing and training/inference scripts for the 3D CNN pipelines.

Reference: Kafali et al., “Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs,” Magnetic Resonance Materials in Physics, Biology and Medicine (2024).

Focus

Pediatric VAT/SAT segmentation + quantification

Environment

PyTorch

Model

3D U-Net with Cross-cohort federated learning

License

Academic Software License

• Trains across pediatric and adult cohorts without direct data sharing, retaining local decoders for domain specificity.
• Produces VAT and SAT masks for volumetric quantification and PDFF comparison.
• Achieves strong Dice scores and rapid inference for pediatric abdominal adipose tissue segmentation.

Reference: Wu et al., “Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation on free-breathing 3D Dixon MRI,” Radiology Advances (2025).