Quantitative Assessment of Myocardial Infarction Scarring using Optical Coherence Tomography: towards data-driven...

Reliable identification of fibrotic regions is essential for targeted catheter ablation therapy, as current imaging modalities such as cardiac magnetic resonance imaging face technical and clinical limitations, particularly in resolution and compatibility with implanted devices. This work presents the quantitative assessment of optical coherence tomography (OCT) images to classify myocardium into fibro-elastic versus normal.

We acquired ultrahigh resolution OCT images from a sheep model with chronic myocardial infarction and performed pixelwise depth-resolved analysis to generate attenuation coefficient maps. In addition, we extracted radiomic features from three dimensional subvolumes to train a XGBoost classifier and validated our results against histological ground truth using Masson's trichrome staining histology to assess diagnostic accuracy.

Attenuation and prediction probabilities effectively highlighted fibro-elastic regions. Widefield en face representations offered fast three dimensional screening of cardiac fibrosis. The radiomics-based XGBoost classifier achieved an area under the curve of 0.97 for binary classification.

Combining ultrahigh resolution OCT with a straightforward attenuation coefficient and a robust radiomics pipeline for optical property extraction and high throughput radiomic feature analysis enables label-free assessment of fibrotic microstructures in the myocardium.

The proposed quantitative framework enhances the detection and characterization of fibrotic myocardial tissue, offering potential for improved diagnostic precision and clinical integration of OCT in cardiology workflows towards data-driven catheter therapy guidance.