Daniel Silverman

Respiratory Gated PET Derived in a Fully Automated Manner From Raw PET Data

Respiratory motion in PET degrades image quality and limits detectability of small or low-contrast lesions. Although image quality can be improved using respiratory-gating, this adds to the complexity and expense of acquiring PET data. We aimed to develop a data-driven method, based on individual voxel signal fluctuations, for accomplishing electronic respiratory gating of clinical PET data, requiring no additional hardware or end-user input. We tested our methods using both simulated PET scans and actual human PET acquisitions. For the simulations, our methods correctly identified the start frame of each respiratory cycle defined for the phantom. Resultant gated images demonstrated improved effective resolution and increased measured uptake for lesions located in the thorax. For human PET data, we were able to recover respiratory phase information with a high signal-to-noise ratio. We report here a method to achieve fully automated voxel-based respiratory gating of PET images, without the need for gating hardware or additional user input, capable of improving effective resolution and increasing lesion detectability.