Data-Driven Respiration-Gated SPECT for Liver Radioembolization

Respiratory motion leads to blur and artifacts in single-photon emission computed tomography (SPECT) images. This may be an issue in some treatments such as liver radioembolization. The purpose of this work was to reconstruct 4-D respiration-gated SPECT images using a data-driven approach. A respiratory signal was extracted from SPECT list-mode data using Laplacian eigenmaps and used for retrospective phase gating. The method was evaluated on various SPECT datasets: Monte Carlo simulation of a patient acquisition, several acquisitions of a dynamic phantom, and 20 acquisitions of patients treated with liver radioembolization. We observed an increase of the mean activity inside the tumor (A) and of the tumor-to-normal liver ratio (T/N) in the respiration-gated SPECT image compared to the 3-D image without motion correction. For the patient acquisitions, an average 3.3% (up to 11.9%) and 3.2% (up to 9.7%) increase was observed for A and T/N, respectively. For the simulation, the activity was within 2.6% of the reference without movement and around 3% for the three patterns of tumor motion simulated with the mechanical phantom. Overall, the proposed respiration-gated SPECT reconstruction improves the estimation of the activity in the tumor without the need for extra hardware.

Automated summary

The purpose of this work was to reconstruct 4-D respiration-gated SPECT images using a data-driven approach. The proposed method improves the estimation of the activity in the tumor without the need for extra hardware.