HDTV: a high-order directional total variation reconstruction algorithm from sparse and limited-angle data in electron paramagnetic resonance imaging.

Abstract

Background: Electron paramagnetic resonance imaging (EPRI)-based oxygen imaging technology enables adaptive radiation therapy, thereby improving tumor control rates. However, the long scanning time limits the development of EPRI. In this study, we endeavored to reduce the scanning time. The general method is sparse reconstruction; if it can be collected in limited-angle range under sparse conditions, the scanning time can be further shortened.

Methods: Based on the abovementioned theory, we performed sparse acquisition based on limited-angle range to further accelerate scanning. Moreover, high-order constraints were introduced into the directional total variation (DTV) algorithm to suppress staircase artifacts, and we proposed a high-order DTV (HDTV) model and derived the Chambolle-Pock (CP) solving algorithm. We aimed to realize three-dimensional (3D) sparse and limited-angle EPRI with high precision and thus accelerate the scanning time.

Results: The correctness of the HDTV-CP algorithm was validated on simulation data and the limited-angle and sparse reconstruction ability was investigated using real data. The results indicate that the HDTV method effectively suppresses limited-angle artifacts, sparse artifacts, and staircase artifacts while preserving the edge and texture features. Our method showed significant improvements compared to the classic TV method. The normalized root mean square error (nRMSE) decreased from 0.34 to 0.16, and the Pearson correlation coefficient (PCC) increased from 0.93 to 0.98 based on 50 views within half the angular range.

Conclusions: For the first time, we combined the limited-angle and sparse problems. The HDTV method may realize 16 times acceleration while ensuring the imaging quality in certain situations. The findings of this study can also extend to the field of limited-angle computed tomography (CT) image reconstruction.