DYNAPEX

Multimodal imaging that reveals invisible changes in the brain.

DYNAPEX combines structural MRI and physiological MRI through AI-driven multimodal imaging analysis, transforming subtle functional and perfusion changes into objective evidence.

DYNAPEX by the numbers

40+AI Models
20+SCI(E) Publications
30+Tertiary Hospitals Installed
ZeroVC Investment

Products

Disease-specific AI solutions

Disease-specific AI products for precision neurological and oncological imaging.

DYNAPEX BT

Glioblastoma

Habitat mapping for voxel-level tumor microenvironment quantification via rCBV and ADC clustering.

DYNAPEX METS

Brain Metastasis

Multi-sequence MRI support for detection and longitudinal tracking of brain metastases.

DYNAPEX AD

ARIA / Dementia

Regional volumetry and imaging biomarker support for neurodegenerative disease workflows.

DYNAPEX MS

Multiple Sclerosis

Lesion burden, PRL, QSM, and longitudinal monitoring for MS imaging.

DYNAPEX PD

Parkinson's Disease

Quantitative neuromelanin and substantia nigra analysis support.

DYNAPEX HN

Head and Neck Cancer

CT and multimodal imaging support for tumor profiling in head and neck cancer.

Technology

Quantifying what the eye cannot see.

Most conventional medical AI starts with structural images. DYNAPEX combines structural and physiological signals to surface perfusion, diffusion, susceptibility, and quantitative biomarkers that are hard to read consistently by eye.

The problem we solve

Subtle physiologic changes can precede visible structural findings, while inter-reader variability slows consistent follow-up across institutions.

Our approach

A modular pipeline integrates CT/MR inputs, disease-specific AI tasks, quantitative maps, structured evidence, and PACS-ready reporting.

Publications

Selected research

  1. Deep learning-based metastasis detection in patients with lung cancerPark YW et al. · Cancer Imaging · 2024 · mets
  2. Mapping Tumor Habitats in IDH-Wild Type GlioblastomaPark JE et al. · Neuro Oncol · 2025 · gbm
  3. Prospective Longitudinal Analysis of Physiologic MRI-based Tumor Habitat Predicts Short-term Patient OutcomesMoon HH et al. · Neuro Oncol · 2025 · gbm
  4. Reducing false positives in DL-based brain metastasis detectionYun S et al. · Eur Radiol · 2024 · mets
  5. Prospective longitudinal analysis of imaging-based spatiotemporal tumor habitatsMoon HH et al. · BMC Cancer · 2024 · gbm
  6. Deep Learning-based Detection and Quantification of Brain MetastasesJeong H et al. · Eur Radiol · 2024 · mets