Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction

• URL: http://arxiv.org/abs/2407.07517v1
• Date: Wed, 10 Jul 2024 10:12:26 GMT
• Title: Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction
• Authors: Yumin Kim, Gayoon Choi, Seong Jae Hwang,
• Abstract summary: Motivated by the potential of. Efficient Fine-Tuning (PEFT), we aim to address issues by effectively leveraging PEFT to improve limited data. In this paper, we introduce PETITE,. Efficient Fine-Tuning for MultI-scanner PET to PET REconstruction that uses fewer than 1% of the parameters.
• Score: 3.74142789780782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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)

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