Fibre-based fluorescence-lifetime imaging microscopy: a real-time biopsy guidance tool for suspected lung cancer.

Lung cancer is the most common cause of cancer-related deaths worldwide. Early detection improves outcomes, however, existing sampling techniques are associated with suboptimal diagnostic yield and procedure-related complications. Autofluorescence-based fluorescence-lifetime imaging microscopy (FLIM), a technique which measures endogenous fluorophore decay rates, may aid identification of optimal biopsy sites in suspected lung cancer.

Full spectrum fluorescence lifetime imaging with 0.5 nm spectral and 50 ps temporal resolution.

The use of optical techniques to interrogate wide ranging samples from semiconductors to biological tissue for rapid analysis and diagnostics has gained wide adoption over the past decades. The desire to collect ever more spatially, spectrally and temporally detailed optical signatures for sample characterization has specifically driven a sharp rise in new optical microscopy technologies. Here we present a high-speed optical scanning microscope capable of capturing time resolved images across 512 spectral and 32 time channels in a single acquisition with the potential for ~0.

Solitary pulmonary nodule imaging approaches and the role of optical fibre-based technologies.

Solitary pulmonary nodules (SPNs) are a clinical challenge, given there is no single clinical sign or radiological feature that definitively identifies a benign from a malignant SPN. The early detection of lung cancer has a huge impact on survival outcome. Consequently, there is great interest in the prompt diagnosis, and treatment of malignant SPNs.

Deep Learning in ex-vivo Lung Cancer Discrimination using Fluorescence Lifetime Endomicroscopic Images.

Fluorescence lifetime is effective in discriminating cancerous tissue from normal tissue, but conventional discrimination methods are primarily based on statistical approaches in collaboration with prior knowledge. This paper investigates the application of deep convolutional neural networks (CNNs) for automatic differentiation of ex-vivo human lung cancer via fluorescence lifetime imaging. Around 70,000 fluorescence images from ex-vivo lung tissue of 14 patients were collected by a custom fibre-based fluorescence lifetime imaging endomicroscope.

The relationship of nightmare frequency and nightmare distress to well-being.

Nightmares can be defined as very disturbing dreams, the events or emotions of which cause the dreamer to wake up. In contrast, unpleasant dreams can be defined in terms of a negative emotional rating of a dream, irrespective of whether or not the emotions or events of the dream woke the dreamer. This study addresses whether frequency of unpleasant dreams is a better index of low well-being than is frequency of nightmares.