Principal component analysis enhances SNR for dynamic electron paramagnetic resonance oxygen imaging of cycling hypoxia in vivo

Abstract

Purpose 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 non-invasive, 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. Methods Principal component analysis (PCA) allows for accelerated image acquisition with acceptable SNR by filtering noise in projection data, from which pO2 images are reconstructed. PCA is used as a denoising technique by including only low-order components to approximate the EPRI projection data. Results Simulated and experimental studies show that PCA filtering increases SNR, particularly for small numbers of sub-volumes with changing pO2enabling an order of magnitude increase in temporal resolution with minimal deterioration in spatial resolution or image quality. Conclusion The SNR necessary for dynamic EPRI studies with temporal resolution required to investigate cycling hypoxia and its physiological implications is enabled by PCA filtering.

Clinical oxygen enhancement ratio of tumors in carbon ion radiotherapy: the influence of local oxygenation changes

“…However, another type of reoxygenation, resulting from changes in acute hypoxia between fractions, should also be considered [9–11]. In a recent study by Lindblom [12], the influence of such local oxygenation changes (LOCs) on the outcome of stereotactic body radiotherapy (SBRT) using photons was investigated.…”

EPR Image Based Oxygen Movies for Transient Hypoxia

“…PCA is suited for data with large portions having common features and a small number of such features. Studies suggest that this is the case for tumors exhibiting transient hypoxia 22 . PCA filtering of Dynamic EPRI data is demonstrated in an experiment with a mouse breathing air (21% O 2 ) followed by carbogen (95% O 2 ) and then air again.…”

“…Filtering of the data can be achieved with a low-order approximation by projection onto the first q PCs, thus selectively retaining relevant temporal patterns while discarding unwanted noise. PCA filtering is applied to dynamic EPRI studies as a pre-reconstruction spatiotemporal filter for projection data 22 .…”

Implementation of GPU-accelerated back projection for EPR imaging

“…The oxygenation status of a tumor is an important determinant for the outcome of radiation therapy. EPRI images of pO 2 may provide necessary information for image guided dose painting, in which an appropriate spatial distribution of radiation dose is chosen to focus more strongly on radio-resistant hypoxic tumor regions [5]. …”