PRIMIS: Privacy-preserving medical image sharing via deep sparsifying transform learning with obfuscation.

Objective: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images.

Differential privacy preserved federated transfer learning for multi-institutional Ga-PET image artefact detection and disentanglement.

Purpose: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately.

Multi-institutional PET/CT image segmentation using federated deep transformer learning.

Background And Objective: Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets between different centers. To overcome these challenges, we developed a federated learning (FL) framework for multi-institutional PET/CT image segmentation.

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.

Purpose: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers.

Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework.

Purpose: The generalizability and trustworthiness of deep learning (DL)-based algorithms depend on the size and heterogeneity of training datasets. However, because of patient privacy concerns and ethical and legal issues, sharing medical images between different centers is restricted. Our objective is to build a federated DL-based framework for PET image segmentation utilizing a multicentric dataset and to compare its performance with the centralized DL approach.

Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation.

Purpose: We assess the performance of a recurrent frame generation algorithm for prediction of late frames from initial frames in dynamic brain PET imaging.

Methods: Clinical dynamic F-DOPA brain PET/CT studies of 46 subjects with ten folds cross-validation were retrospectively employed. A novel stochastic adversarial video prediction model was implemented to predict the last 13 frames (25-90 minutes) from the initial 13 frames (0-25 minutes).