Recurrent reconstruction network enables real-time and high-resolution PRF thermometry for LITT

Abstract

Motivation: Proton-resonance-frequency (PRF) shift-based thermometry is used to monitor the temperature change during Laser-interstitial-thermal-therapy (LITT). Recent LITT developments call for PRF thermometry to achieve larger volume coverage and higher spatiotemporal resolution for enhanced therapeutic efficacy. Goal(s): Accelerate the PRF thermometry by compressed-sensing (CS) undersampling to enlarge the volume coverage and increase the spatiotemporal resolution. Use a neural network for real-time reconstruction of the undersampled data. Approach: A recurrent reconstruction network (RRN) was proposed to reconstruct the highly undersampled PRF data. Retrospective and prospective undersampling experiments were conducted. Results: RRN demonstrated good image reconstruction quality in retrospective experiments, with promising results in prospective experiments. Impact: The introduction of the RRN offers a solution for real-time and high-resolution PRF thermometry during LITT, potentially improving treatment outcomes.