Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (p < 0.001). For both datasets and most label-limited scenarios, scaling the size of unlabeled pretraining data resulted in improved segmentation performance. SSL models pretrained with this amount of data outperformed baseline supervised models in the computation of clinically-relevant metrics, especially when the performance of supervised learning was low. Our results demonstrate that SSL pretraining using inpainting-based pretext tasks can help increase the robustness of models in label-limited scenarios and reduce worst-case errors that occur with supervised learning.

Optimal Broadband Noise Matching to Inductive Sensors: Application to Magnetic Particle Imaging.

Inductive sensor-based measurement techniques are useful for a wide range of biomedical applications. However, optimizing the noise performance of these sensors is challenging at broadband frequencies, owing to the frequency-dependent reactance of the sensor. In this work, we describe the fundamental limits of noise performance and bandwidth for these sensors in combination with a low-noise amplifier. We also present three equivalent methods of noise matching to inductive sensors using transformer-like network topologies. Finally, we apply these techniques to improve the noise performance in magnetic particle imaging, a new molecular imaging modality with excellent detection sensitivity. Using a custom noise-matched amplifier, we experimentally demonstrate an 11-fold improvement in noise performance in a small animal magnetic particle imaging scanner.

Quantitative Magnetic Particle Imaging Monitors the Transplantation, Biodistribution, and Clearance of Stem Cells In Vivo.

Stem cell therapies have enormous potential for treating many debilitating diseases, including heart failure, stroke and traumatic brain injury. For maximal efficacy, these therapies require targeted cell delivery to specific tissues followed by successful cell engraftment. However, targeted delivery remains an open challenge. As one example, it is common for intravenous deliveries of mesenchymal stem cells (MSCs) to become entrapped in lung microvasculature instead of the target tissue. Hence, a robust, quantitative imaging method would be essential for developing efficacious cell therapies. Here we show that Magnetic Particle Imaging (MPI), a novel technique that directly images iron-oxide nanoparticle-tagged cells, can longitudinally monitor and quantify MSC administration in vivo. MPI offers near-ideal image contrast, depth penetration, and robustness; these properties make MPI both ultra-sensitive and linearly quantitative. Here, we imaged, for the first time, the dynamic trafficking of intravenous MSC administrations using MPI. Our results indicate that labeled MSC injections are immediately entrapped in lung tissue and then clear to the liver within one day, whereas standard iron oxide particle (Resovist) injections are immediately taken up by liver and spleen. Longitudinal MPI-CT imaging also indicated a clearance half-life of MSC iron oxide labels in the liver at 4.6 days. Finally, our ex vivo MPI biodistribution measurements of iron in liver, spleen, heart, and lungs after injection showed excellent agreement (R(2) = 0.943) with measurements from induction coupled plasma spectrometry. These results demonstrate that MPI offers strong utility for noninvasively imaging and quantifying the systemic distribution of cell therapies and other therapeutic agents.