Evaluation of a direct motion estimation/correction method in respiratory-gated PET/MRI with motion-adjusted attenuation.

Purpose: Respiratory motion compensation in PET/CT and PET/MRI is essential as motion is a source of image degradation (motion blur, attenuation artifacts). In previous work, we developed a direct method for joint image reconstruction/motion estimation (JRM) for attenuation-corrected (AC) respiratory-gated PET, which uses a single attenuation-map (μ-map). This approach was successfully implemented for respiratory-gated PET/CT, but since it relied on an accurate μ-map for motion estimation, the question of its applicability in PET/MRI is open. The purpose of this work is to investigate the feasibility of JRM in PET/MRI and to assess the robustness of the motion estimation when a degraded μ-map is used.

Methods: We performed a series of JRM reconstructions from simulated PET data using a range of simulated Dixon MRI sequence derived μ-maps with wrong attenuation values in the lungs, from -100% (no attenuation) to +100% (double attenuation), as well as truncated arms. We compared the estimated motions with the one obtained from JRM in ideal conditions (no noise, true μ-map as an input). We also applied JRM on 4 patient datasets of the chest, 3 of them containing hot lesions. Patient list-mode data were gated using a principal component analysis method. We compared SUV values of the JRM reconstructed activity images and non motion-corrected images. We also assessed the estimated motion fields by comparing the deformed JRM-reconstructed activity with individually non-AC reconstructed gates.

Results: Experiments on simulated data showed that JRM-motion estimation is robust to μ-map degradation in the sense that it produces motion fields similar to the ones obtained when using the true μ-map, regardless of the attenuation errors in the lungs (< 0.5% mean absolute difference with the reference motion field). When using a μ-map with truncated arms, JRM estimates a motion field that stretches the μ-map in order to match the projection data. Results on patient datasets showed that using JRM improves the SUV values of hot lesions significantly and suppresses motion blur. When the estimated motion fields are applied to the reconstructed activity, the deformed images are geometrically similar to the non-AC individually reconstructed gates.

Conclusion: Motion estimation by JRM is robust to variation of the attenuation values in the lungs. JRM successfully compensates for motion when applied to PET/MRI clinical datasets. It provides a potential alternative to existing methods where the motion fields are pre-estimated from separate MRI measurements.