Robust principal component analysis in optical micro-angiography.

Area of Science:

• Biomedical Optics
• Medical Imaging
• Signal Processing

Background:

• Optical micro-angiography (OMAG) uses principal component analysis (PCA) with optical coherence tomography (OCT) to distinguish blood flow from static tissue.
• Traditional PCA is susceptible to outliers, leading to motion artifacts and inaccurate signal separation.
• Robust PCA (RPCA) is proposed to overcome the limitations of PCA in OMAG applications.

Purpose of the Study:

• To introduce and evaluate Robust PCA (RPCA) as an alternative to traditional PCA for optical micro-angiography.
• To compare the performance of PCA and RPCA in analyzing complex OCT data from phantoms and in vivo tissues.
• To assess the impact of outliers, such as tissue motion, on OMAG signal analysis.

Main Methods:

• Theoretical background of PCA and RPCA in the context of coherent OCT signal analysis for blood flow.
• Comparison of PCA and RPCA using 4D-OCT complex data from microfluidic phantoms.
• Evaluation of PCA and RPCA on in vivo nail-fold tissue data.

Main Results:

• PCA exhibited minor tail-noise artifacts in phantom experiments.
• PCA performance degraded in the presence of tissue motion in nail-fold experiments, while RPCA remained unaffected.
• RPCA revealed enhanced dynamic signals, potentially from intercellular fluid, though clinical utility is unproven.

Conclusions:

• Traditional PCA is sensitive to outliers like motion artifacts, compromising OMAG signal analysis.
• RPCA demonstrates robustness against outliers, improving signal separation in OMAG.
• RPCA is currently more computationally demanding than traditional PCA.