Cellulose-Based Cryogel Microspheres with Nanoporous and Controllable Wrinkled Morphologies for Rapid Hemostasis.

First-aid hemostatic agents for acute bleeding can save lives in emergency situations. However, rapid hemostasis remains challenging when uncontrolled hemorrhage occurs on lethal noncompressible and irregular wounds. Herein, cellulose-based cryogel microspheres with deliberately customized micromorphologies for ultrafast water transportation and diffusion, including the shark skin riblet-inspired wrinkled surface with low fluid drag and the hydrophilic nanoporous 3D networks, are developed to deal with the acute noncompressible bleeding within seconds. These cryogel microspheres can rapidly absorb a large amount of blood over 6 times their own weight in 10 s and form a robust barrier to seal a bleeding wound without applying pressure. Remarkably, massive bleeding from a cardiac penetrating hole is effectively stopped using the microspheres within 20 s and no blood leakage is observed after 30 min. Additionally, these microspheres could be readily removed without rebleeding and capillary thrombus, which is highly favorable to rapid hemostasis in emergency rescue.

Abdominal computed tomography localizer image generation: A deep learning approach.

BACKGROUND AND OBJECTIVE: Computed Tomography (CT) has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localization scans, which are used for patient positioning and diagnostic scan parameter definition. These two localization scans contribute to the patient dose but are not used for diagnosis purposes. In this study, we investigate the possibility of using deep learning models to reconstruct one localization scan image from the other, thus reducing the patient dose and simplifying the clinical workflow. METHODS: We propose a modified encoder-decoder network and a scaled mixture loss function specifically for the focal task. In this study, 12,487 clinical abdominal exams were retrieved from a clinical medical imaging storage system and randomly split for training, validation, and test in the ratio of 7:1:2. Reconstructed images were compared with the ground truth in terms of location prediction error, profile prediction error, and attenuation prediction error. RESULTS: The average location error, profile error, and attenuation error were 1.02±3.37 mm, 4.43±2.02%, and 6.2 ± 2.94% for lateral prediction, and 6.46±6.43 mm, 3.9 ± 2.32%, and 7.12±3.54% for AP prediction, respectively. CONCLUSIONS: We conclude that although the reconstructed abdominal CT localization images may lack some details on the internal organ structures, they could be used effectively for tube current modulation calculation and patient positioning purposes, leading to a reduction of radiation dose and scan time in clinical CT exams.