  Reducing scan time in Positron Emission Tomography (PET) imaging while
maintaining high-quality images is crucial for minimizing patient discomfort
and radiation exposure. Due to the limited size of datasets and distribution
discrepancy across scanners in medical imaging, fine-tuning in a
parameter-efficient and effective manner is on the rise. Motivated by the
potential of Parameter-Efficient Fine-Tuning (PEFT), we aim to address these
issues by effectively leveraging PEFT to improve limited data and GPU resource
issues in multi-scanner setups. In this paper, we introduce PETITE,
Parameter-Efficient Fine-Tuning for MultI-scanner PET to PET REconstruction
that uses fewer than 1% of the parameters. To the best of our knowledge, this
study is the first to systematically explore the efficacy of diverse PEFT
techniques in medical imaging reconstruction tasks via prevalent
encoder-decoder-type deep models. This investigation, in particular, brings
intriguing insights into PETITE as we show further improvements by treating
encoder and decoder separately and mixing different PEFT methods, namely,
Mix-PEFT. Using multi-scanner PET datasets comprised of five different
scanners, we extensively test the cross-scanner PET scan time reduction
performances (i.e., a model pre-trained on one scanner is fine-tuned on a
different scanner) of 21 feasible Mix-PEFT combinations to derive optimal
PETITE. We show that training with less than 1% parameters using PETITE
performs on par with full fine-tuning (i.e., 100% parameter)
