Bettina Baeßler

Tobias Wech, Oliver Schad, Simon Sauer et al.

A joint image reconstruction and segmentation approach based on disentangled representation learning was trained to enable cardiac cine MR imaging in real-time and under free-breathing. An exploratory feasibility study tested the proposed method in undersampled real-time acquisitions based on an in-house developed spiral bSSFP pulse sequence in eight healthy participants and five patients with intermittent atrial fibrillation. Images and predicted LV segmentations were compared to the reference standard of ECG-gated segmented Cartesian cine in repeated breath-holds and corresponding manual segmentation. On a 5-point Likert scale, image quality of the real-time breath-hold approach and Cartesian cine was comparable in healthy participants (RT-BH: 1.99 $\pm$ .98, Cartesian: 1.94 $\pm$ .86, p=.052), but slightly inferior in free-breathing (RT-FB: 2.40 $\pm$ .98, p<.001). In patients with arrhythmia, image quality from both real-time approaches was favourable (RT-BH: 2.10 $\pm$ 1.28, p<.001, RT-FB: 2.40 $\pm$ 1.13, p<.001, Cartesian: 2.68 $\pm$ 1.13). Intra-observer reliability was good (ICC=.77, 95%-confidence interval [.75, .79], p<.001). In functional analysis, a positive bias was observed for ejection fractions derived from the proposed model compared to the clinical reference standard (RT-BH mean EF: 58.5 $\pm$ 5.6%, bias: +3.47%, 95%-confidence interval [-.86, 7.79%], RT-FB mean: 57.9 $\pm$ 10.6%, bias: +1.45%, [-3.02, 5.91%], Cartesian mean: 54.9 $\pm$ 6.7%). The introduced real-time MR imaging technique is capable of acquiring high-quality cardiac cine data in 1-2 minutes without the need for ECG gating and breath-holds. It thus offers a promising alternative to the current clinical practice of segmented acquisition, with shorter scan times, higher patient comfort and increased robustness to arrhythmia and patient incompliance.

Salman Ul Hassan Dar, Marvin Seyfarth, Isabelle Ayx et al.

AI models present a wide range of applications in the field of medicine. However, achieving optimal performance requires access to extensive healthcare data, which is often not readily available. Furthermore, the imperative to preserve patient privacy restricts patient data sharing with third parties and even within institutes. Recently, generative AI models have been gaining traction for facilitating open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise, some of these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples. Considering the importance of the problem, surprisingly it has received relatively little attention in the medical imaging community. To this end, we assess memorization in unconditional latent diffusion models. We train latent diffusion models on CT, MR, and X-ray datasets for synthetic data generation. We then detect the amount of training data memorized utilizing our novel self-supervised copy detection approach and further investigate various factors that can influence memorization. Our findings show a surprisingly high degree of patient data memorization across all datasets. Comparison with non-diffusion generative models, such as autoencoders and generative adversarial networks, indicates that while latent diffusion models are more susceptible to memorization, overall they outperform non-diffusion models in synthesis quality. Further analyses reveal that using augmentation strategies, small architecture, and increasing dataset can reduce memorization while over-training the models can enhance it. Collectively, our results emphasize the importance of carefully training generative models on private medical imaging datasets, and examining the synthetic data to ensure patient privacy before sharing it for medical research and applications.