Our simulation strategy focuses on the acquisition factors that shape each modality, including tissue or tracer properties, system response, sampling, noise, and reconstruction.
MRIMagnetic Resonance
MODALITY PROFILE / MRI
Magnetic Resonance
MRI simulation focuses on the physical factors that shape signal formation, spatial encoding, image contrast, and reconstruction across acquisition conditions.
SIMULATION FOCUS
01Tissue properties
02Sequences
03B₀ / RF effects
04Sampling
05Noise
06Reconstruction
CTComputed Tomography
MODALITY PROFILE / CT
Computed Tomography
CT simulation focuses on the factors that shape projection measurements and reconstructed image appearance across acquisition and dose conditions.
SIMULATION FOCUS
01Attenuation
02Acquisition geometry
03Projections
04Photon statistics
05Noise / scatter
06Reconstruction
PETPositron Emission Tomography
MODALITY PROFILE / PET
Positron Emission Tomography
PET simulation focuses on tracer-driven signal generation, detection statistics, system effects, and reconstruction factors that shape quantitative image formation.
SIMULATION FOCUS
01Tracer distribution
02Counting statistics
03Attenuation
04Scatter
05Detector response
06Reconstruction
SPECTSingle-Photon Emission CT
MODALITY PROFILE / SPECT
Single-Photon Emission CT
SPECT simulation focuses on photon transport and the acquisition-system factors that shape detected counts, spatial response, and reconstructed images.
SIMULATION FOCUS
01Photon transport
02Attenuation / scatter
03Collimator response
04Detector response
05Count statistics
06Geometry / reconstruction
Physics-informed synthetic data
From Real Imaging Data to Physics-Informed Data Augmentation
Physics-informed synthetic data can expand training diversity while keeping model evaluation grounded in independent real-world imaging data.
MRICTPETSPECT
SOURCE 01
Real Imaging Data
De-identified medical imaging studies anchor the development process.
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MRICTPETSPECT
SOURCE 02
Physics-Informed Synthetic Data
Conceptual augmentation informed by imaging physics broadens training diversity.
REALMRICTPETSPECT
SYNMRICTPETSPECT
MERGED DATASET
Combined Training Dataset
Real and synthetic examples are brought together for downstream model development.
InputHiddenOutput
MODEL DEVELOPMENT
AI Model Training
Training uses the combined dataset to support reconstruction and analysis research.
Task metricRobustnessGeneralization
FINAL CHECK
Independent Real-Data Evaluation
Performance is assessed on held-out real imaging data to keep validation grounded.
Partnership model
A governed path from source imaging to research-ready data.
Transforming medical imaging into research-ready data requires more than collection. Explore the four connected phases that shape the journey.
IMAGING DATAFrom collaboration to research-ready data
PHASE 01
Partnership Setup
Partner · Govern
01
Partner
Define collaboration scope, data sources, research objectives, and roles with hospitals, imaging centers, universities, or research organizations.
02
Govern
Establish appropriate agreements, permissions, provenance, access controls, and institutional data governance.
PHASE 02
Data Preparation
De-identify · Curate · Harmonize
01
De-identify
Prepare imaging information for permitted research or development use while protecting patient identity.
02
Curate
Organize quality control, metadata, acquisition parameters, and cohort structure.
03
Harmonize
Standardize formats, labels, terminology, and metadata across datasets.
PHASE 03
Research Enrichment
Annotate · Augment
01
Annotate
Add expert labels, anatomical segmentations, or other research annotations where appropriate.
02
Augment
Develop physics-informed synthetic imaging around modality-specific forward models to complement real data.
PHASE 04
Release Readiness
Validate · Deliver
01
Validate
Evaluate real-only and augmented-data approaches on appropriate held-out real datasets.
02
Deliver
Provide research-ready datasets under appropriate agreements, permissions, and usage rights.
Data governance
Partnerships should begin with provenance, permissions, and purpose.
SpinField's data partnerships are designed around responsible data governance, clear provenance, appropriate usage rights, and privacy-conscious preparation of imaging data.
We are interested in conversations with hospitals, imaging centers, universities, medical AI organizations, pharmaceutical research teams, and research consortia.
