Principal component analysis enhances SNR for dynamic electron paramagnetic resonance oxygen imaging of cycling...

Low oxygen concentration (hypoxia) in tumors strongly affects their malignant state and resistance to therapy. These effects may be more deleterious in regions undergoing cycling hypoxia. Electron paramagnetic resonance imaging (EPRI) has provided a noninvasive, quantitative imaging modality to investigate static pO2 in vivo. However, to image changing hypoxia, EPRI images with better temporal resolution may be required. The tradeoff between temporal resolution and signal-to-noise ratio (SNR) results in lower SNR for EPRI images with imaging time short enough to resolve cycling hypoxia.

Principal component analysis allows for accelerated image acquisition with acceptable SNR by filtering noise in projection data, from which pO2 images are reconstructed. Principal component analysis is used as a denoising technique by including only low-order components to approximate the EPRI projection data.

Simulated and experimental studies show that principal component analysis filtering increases SNR, particularly for small numbers of sub-volumes with changing pO2 , enabling an order of magnitude increase in temporal resolution with minimal deterioration in spatial resolution or image quality.

The SNR necessary for dynamic EPRI studies with temporal resolution required to investigate cycling hypoxia and its physiological implications is enabled by principal component analysis filtering.