Technologies Enabling Super-Resolution Biomedical Imaging

The adoption of super-resolution imaging systems is sustained by a steady shift in demand for imaging at the nanolevel. Frost & Sullivan’s Medical Devices & Imaging Team tracks emerging technologies across industries and assesses their impact on health care.

Super-resolution refers to the family of techniques used to enhance the resolution of an imaging system. Mostly used in the context of biomedical imaging, super-resolution techniques either augment an imaging system’s optical capabilities (optical super-resolution) or improve the resolution of the image sensors (geometric super-resolution). Optical super-resolution techniques are directed toward improving image resolution by transcending the diffraction limit.

There are two broad approaches to achieving optical super-resolution: true super-resolution or functional super-resolution. The former approach captures information contained in evanescent waves (electromagnetic radiation that is not usually captured by conventional imaging systems), honing the system to be more sensitive to imaging information that emanates from the sample. The latter employs a series of instrumentation and algorithmic enhancements to digitally reconstruct the data into a highly resolved image. Several image capture techniques developed over the last two decades are aligned to these approaches: near-field scanning optical microscopy (NSOM), spatially modulated illumination (SMI) and 4-Pi imaging effect true super-resolution; stimulated emission-depletion (STED), ground state depletion (GSD) and photo-activated localization microscopy (PALM) effect functional super-resolution.

Frost & Sullivan’s Medical Devices & Imaging Team tracks emerging technologies across industries and assesses their impact on health care. Several of these technologies and their commercial impact are discussed below.

Optical Nanoscope by Edinburgh Super-Resolution Imaging Consortium (ESRIC), Scotland

 ESRIC is a joint initiative between Heriot-Watt University and the University of Edinburgh. The research partnership has resulted in one of the most advanced microscopes in Europe. In fact, because of the ability of the imaging system to image objects in the nanometer-scale, it is being dubbed a “nanoscope.” This STED-based system employs two laser lights—one each for excitation and depletion of the fluorescent dyes that are used to tag cells and cell components.

The nanoscope is said to be able to image in four colors, which means that different components and features of the cell can be imaged in different colors. This also gives the sample an appearance of being imaged in three dimensions. The research project was funded by The Wellcome Trust and Leica Microsystems, a proponent of STED technology.

Quantum Dots Microscopy by Julius Maximilian University of Würzburg, Germany  

Quantum dots are nanoscopic semiconductor particles that, because of their size, exhibit physical and optical properties that are considerably different than macro objects. Several quantum dots emit light of distinct frequencies when they are subjected to optical or electrical stimuli. This property is widely leveraged in electronic displays, advanced printing and medical imaging, and in the manufacture of photovoltaic cells.

Researchers from Julius Maximilian and TU Dresden have devised a way of using quantum dots to enhance image quality. The team loaded a large number of quantum particles onto biomolecules known as microtubules. The microtubules were made to pass over immobilized motor proteins, which help carry them. Hence, the quantum dots were effectively scanning the surface of the sample, covering more surface area and revealing more information through distinct emission patterns than what was achieved through the best-in-class microscopy systems. The use of quantum dots greatly increased the signal-to-noise ratio and helped create virtually artifact-free images.

Super-Resolution Ultrasonic Imaging by the Indian Institute of Technology, Madras

Although the term super-resolution is usually attributed to optical imaging, the truth is that any modality used to obtain medical images—ultrasound, magnetic resonance imaging (MRI), computed tomography or optical imaging—requires a good resolution, or what is colloquially understood as clarity. Resolution is quite simply the ability of an imaging system to identify two very closely located points as distinct points rather than as one blurry spot. In that respect, ultrasound images are subject to a considerable amount of noise because of the several interferences during scanning.

A group of researchers led by professors Prabhu Rajagopal and Krishnan Balsubramanian at the Chennai-based institute have developed an ultrasound imaging system that supposedly offers this highest resolution of any ultrasound system. The research group specially designed an acoustic lens using metamaterials, a class of materials that have optical and electromagnetic properties not usually found in naturally occurring materials. Using this lens, the researchers were able to obtain ultrasonic images with resolution in the micron scale. As a comparison, conventional ultrasound imagers produce images with a maximum resolution of 0.5 millimeters.

Ultrasound has several advantages over modalities such as MRI and X-rays: they are less expensive, more portable and—importantly—do not employ harmful ionizing radiation. These advantages are often overshadowed by their relatively poor image quality, but this super-resolution system has the potential to overcome this limitation.

Artificial Intelligence-Based Resolution Enhancement by Nvidia (Santa Clara, Calif.)

A recent trend has been the use of artificial intelligence and advanced analytics to solve business and technological challenges. Nvidia, one of the foremost image processing and graphics companies, has developed an artificial intelligence program that studies an old or suboptimal image and repairs or augments it by removing noise and filling in missing pixels using deep learning memory and extrapolation. Nvidia has tested this program on photographs and macro images; its success opens the possibility that it can be used to improve resolution of cellular and subcellular images.

The Road Ahead

The adoption of super-resolution imaging systems is sustained by a steady shift in demand for imaging at the nanolevel rather than the micro-level, and the tremendous growth of nanotechnology and the importance given to basic research. Innovations in image sensors, electronics and image processing are constantly improving image quality. Artificial intelligence and its numerous subtechnologies will play an important role in the future of biomedical imaging.

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