PrecisionMRI.AI — MRI Geometric Distortion Correction Platform
MRI Geometric Reliability in Clinical Workflows
Integrated phantom calibration and patient-specific correction for MR-in-RT.
A platform for MRI quality assurance workflows, combining physics-based validation and patient-specific correction.
Developed by clinical medical physicists for MR-in-RT environments. Supports vendor-neutral implementation.
Hardware Quality Assurance
Patient-anatomy phantoms support geometric calibration for MRI system quality assurance.
Correction Mechanism
Cloud-based platform applies patient-specific algorithms for image correction within MR-in-RT integration.
Geometric Assurance
Supports geometric assurance for MR-based radiation therapy workflows.

Certain components of the platform are currently in pilot and pre-commercial deployment.
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Challenges in MR-in-RT Workflows Due to Geometric Distortion
Geometric distortion affects spatial accuracy in MR-in-RT workflows.
Targeting Challenges
Affects treatment planning accuracy.
Limited Patient-Specific QA
Workflows often lack patient-specific validation.
Periodic System Validation
Periodic checks can result in assessment gaps.
Integrated Hardware and AI for MRI Geometric Reliability
Physics-validated calibration with patient-specific AI for geometric reliability.
SYSTEM ARCHITECTURE
PHYSICS LAYER Phantom calibration establishes baseline system geometry.
AI LAYER Patient-specific algorithms for MRI geometric reliability.
1
Characterize System Geometry
Phantoms characterize baseline MRI system geometry.
2
Quantify Spatial Distortion
Platform quantifies geometric distortion within imaging volume.
3
Apply Patient-Specific Reliability
AI applies patient-specific geometric reliability.
Clinical Workflow Integration
  • Phantom-based system validation
  • AI-driven quality assurance workflows
  • Vendor-neutral implementation
  • MRI geometric reliability
Integrated Platform for MRI Geometric Distortion Management
Two components operate within a unified MRI quality assurance platform.
Hardware supports system validation, and software enables patient-specific geometric reliability.
Ghead Phantom
Hardware Validation
A patient-anatomy phantom for MRI system calibration. Optimized for head and neck MR-in-RT workflows.
  • Patient-anatomy replicating phantom
  • Quantifies geometric distortion
  • Supports patient-specific quality assurance workflows
Patient-Specific AI Software
Platform Functionality
A cloud-based platform for patient-specific geometric distortion correction. Integrates with MR-in-RT workflows.
  • Cloud platform for MRI geometric distortion correction
  • Applies patient-specific AI algorithms
  • Integrates with MR-in-RT workflows

Combined Workflow Support
Workflow consistency: Supports MRI geometric reliability for MR-in-RT workflows.
Workflow operations: Designed to support operational workflows and system validation.
Clinical Workflow Integration
  • Supports geometric confidence in MR-based planning
  • Facilitates patient-specific quality assurance workflows
  • Enables vendor-neutral implementation
  • Maintains geometric reliability
Geometric Distortion Correction for Treatment Planning
Example of MRI with geometric distortion correction supporting SRS MR-in-RT workflows.
Geometric reliability supports consistent radiation beam alignment.

MRI with Geometric Distortion Correction
This image illustrates MRI with geometric distortion correction for SRS treatment planning.
This correction supports tumor-target alignment and treatment margin management.
Tumor-Target Alignment
Supports tumor-target alignment workflows and systematic geometric error reduction.
Organ-at-Risk Protection
Supports treatment margin management and radiation exposure management for healthy tissue.
These workflows support consistent patient care. For cancer centers, geometric reliability supports quality assurance workflows and clinical consistency.
Clinical & Technical Validation
Undergoing pilot-stage clinical evaluation.
PrecisionMRI.AI has completed clinical and technical assessments.
CLINICAL DEVELOPMENT STAGES
1
Technology Validation
  • Geometric reliability assessment initiated.
  • Evaluated across multiple MRI platforms.
  • Reproducibility assessed at pilot sites.
2
Clinical Integration
  • Early engagement agreements established.
  • Active evaluation programs underway.
  • Clinical teams assessing MR-in-RT workflows.
3
Regulatory Activities
  • FDA 510(k) pathway actively pursued.
  • Provisional patents formally filed.
  • Quality Management System (QMS) development underway.
4
Strategic Engagements
  • Engagement initiated for vendor-neutral implementation.
  • Advisory board includes experts from institutions.
  • Collaboration framework established with research institutions.
These milestones demonstrate technical readiness and initial clinical integration.
Competitive Landscape
Comparison reflects available MR-in-RT workflows and vendor capabilities.

Integrated Platform Positioning
PrecisionMRI.AI integrates hardware and AI to support quality assurance workflows and vendor-neutral implementation.
Clinical Partnerships
CLINICAL COLLABORATION
Collaborations facilitate clinical evaluation and MR-in-RT workflow integration. PrecisionMRI.AI partners with radiation oncology centers and MRI manufacturers to support technology validation and clinical pilot programs.
Clinical Pilot Sites
Pilot programs are active at radiation oncology centers.
These collaborations provide clinical evaluation of our platform.
Operational feedback informs platform refinement.
  • Evaluation of geometric reliability in clinical settings
  • Optimization of MR-in-RT integration
  • Assessment of reliability within treatment planning
  • Integration of patient-specific QA workflows
MRI Manufacturer Partnerships
We engage MRI manufacturers to explore integration.
This engagement facilitates platform compatibility across diverse clinical imaging systems.
Partnership opportunities include:
  • Technology integration for geometric distortion correction
  • Joint development initiatives
  • Technical collaboration for system compatibility
  • Clinical research collaborations
These engagements emphasize clinical and technical collaboration, distinct from commercial vendor partnerships. To explore collaboration opportunities, please contact us.
Leadership Team & Advisors
Developed by medical physicists with expertise in MR-in-RT workflows and quality assurance.
PrecisionMRI.AI is led by medical physicists experienced in MR-in-RT workflows, imaging quality assurance, and translational AI. The advisory board includes leaders from academic medical centers.
Executive Leadership
Executive Team
Ali Fatemi, Ph.D.
Founder & Chief Executive Officer
Clinical medical physicist focusing on MR-in-RT workflows and patient-specific quality assurance. Experienced in advanced imaging protocol development.
James Petell, Ph.D.
Co-Founder & Officer, Intellectual Property
Experienced in research, product development, and intellectual property management. USPTO Patent Agent.
Scientific & Clinical Advisors
Advisory Board
Daniel Low, Ph.D.
Vice Chair, Department of Radiation Oncology, UCLA
Recognized contributor to MR-in-RT workflows and medical physics.
His research supports MRI geometric reliability standards.
Liam Ghiam, M.D.
Clinical Associate Professor, UCI
Board-certified radiation oncologist, providing clinical perspective.
Focuses on imaging and treatment planning in clinical settings.
Michael Hoff, Ph.D.
Director of Medical Physics, UCSF
As an MRI physicist, Dr. Hoff guides quality assurance workflows.
Supports clinical MR imaging use in radiation therapy.
A vendor-neutral platform for MRI geometric reliability and MR-in-RT workflows.
Supports commissioning, daily quality assurance workflows, and patient-specific QA across various MRI manufacturers.
Contact: afatemi@precisionmri.ai
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