PixelenceAI provides an AI‑driven platform that generates contrast‑enhanced brain MRI images from standard non‑contrast T1 scans with over 95% diagnostic accuracy, eliminating the need for gadolinium contrast agents.
Funding
Funding not disclosed
Founders
Product
Problem
Brain MRI scans for tumor, stroke, and trauma assessment typically require gadolinium-based contrast agents, which introduce risks from heavy‑metal exposure, increase procedure cost, and add time to patient visits.
Solution
PixelenceAI offers an AI‑driven imaging platform that synthetically generates contrast‑enhanced MRI images from non‑contrast T1 scans with over 95% diagnostic accuracy. By eliminating the need for gadolinium, the solution reduces patient safety concerns, lowers imaging costs, and shortens appointment times by up to 100 minutes. The system leverages generative adversarial neural networks trained on large clinical datasets to reconstruct high‑contrast visualizations of brain tumors, ischemic or hemorrhagic stroke zones, and traumatic brain injuries. Resulting images are delivered instantly to clinicians through a secure cloud interface, enabling rapid, precise decision‑making without additional contrast administration.
Target Audience
Primary customers are radiology departments, neuro‑oncology and stroke centers, and hospitals seeking to improve MRI efficiency and safety for brain imaging.
Features
- Generative adversarial neural network model that predicts contrast‑enhanced MRI from standard non‑contrast T1 sequences with >95% accuracy
- Automatic synthesis of high‑contrast images for brain tumors, stroke subtypes, and traumatic brain injury assessments
- Elimination of gadolinium contrast agents, removing associated toxicity and allergic reaction risks
- Workflow integration that saves up to 100 minutes per patient by removing contrast injection and waiting periods
- Cloud‑based processing and secure delivery of synthetic images to radiology workstations and PACS systems
- Validation published in a peer‑reviewed JCO Clinical Cancer Informatics article confirming diagnostic quality of generated images