Avoid equipment graveyards: rigorous process to improve identification and procurement of effective, affordable, and usable newborn devices in low-resource hospital settings.

Background: Millions of newborns die annually from preventable causes, with the highest rates occurring in Africa. Reducing neonatal mortality requires investment to scale hospital care, which includes providing hospitals with appropriate technology to care for small and sick newborns. Expensive medical devices designed for high-resource settings often fail to withstand conditions in low-resource hospitals, including humidity, dust, frequent user turnover, complex maintenance, lack of stable power, or difficulty sourcing expensive consumables.

Unsupervised deep learning framework for data-driven gating in positron emission tomography.

Background: Physiological motion, such as respiratory motion, has become a limiting factor in the spatial resolution of positron emission tomography (PET) imaging as the resolution of PET detectors continue to improve. Motion-induced misregistration between PET and CT images can also cause attenuation correction artifacts. Respiratory gating can be used to freeze the motion and to reduce motion induced artifacts.

Nail Mycosis Fungoides: A New Case.

Ungueotropic mycosis is a rare form of mycosis fungoides. We present the case of a 32-year-old female patient with advanced tumor stage mycosis fungoides, presenting a phanerial involvement with lymphoid infiltration of the nails and scalp confirmed by histology and immunohistochemistry.

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation.

Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion can be estimated simultaneously from respiratory gated data through a joint estimation framework. However, conventional motion estimation methods based on registration of a pair of images are sensitive to noise.

Artificial Intelligence in PET: An Industry Perspective.

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET.