The Alignment Problem.
Historically, clinical longitudinal analysis is manual. Radiologists spend 50% of read time manually synchronizing scans to detect disease progression. But human anatomy is flexible; standard rigid registration fails to account for the non-linear deformations of a living body.
Beyond Rigid Registration.
Traditional deformable algorithms are computationally prohibitive, taking minutes per case. We are building a learning-based framework that enables clinically accurate, topology-preserving alignment in real-time (<1 second).
Clinical Inductive Priors.
Unlike black-box vision models, our architecture leverages physics-informed constraints to ensure anatomical integrity. By utilizing self-supervised loops, we eliminate the clinical data bottleneck, allowing models to learn from context rather than human labels.
Why It Matters.
For now, our models align soft tissue. Science is the process of taming longitudinal noise. By enabling 'Digital Subtraction Radiology,' we allow clinicians to instantly visualize the delta between scans, reducing cognitive load and accelerating diagnostic throughput.