Feature
Optical Coherence Refraction Tomography
Combining principles of computed tomography with modern machine-learning tools significantly improves OCT’s resolution while extending imaging depth, reducing noise and reconstructing refractive-index maps of biological samples.
OCT images taken from multiple angles can build an image with superior lateral resolution (inset).
Optical coherence tomography (OCT) has come a long way since its invention nearly three decades ago, as a method for non-invasive microscopy in living biological tissues. Over the years, OCT’s micrometer-scale resolution and continually improving image acquisition speed have led to success in numerous fields, most notably ophthalmology, cardiology, and gastroenterology. Yet OCT has always had a notable shortcoming: It achieves excellent axial resolution over an extended imaging depth only at the expense of inferior lateral resolution.
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