Using Quantum Computing to Enhance Cell Imaging

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A new era of computing is right around the corner, and it’s predicted to change everything, profound shifts will occur in every industry as the applications of quantum computing are realised. Medical science is one of the first sectors earmarked to become one of the early adopters of the technology. The enhanced power of computations will be utilised in a multitude of areas, from speeding up research and design of new drugs, to making radiotherapy more accurate and improving diagnosis.

Quantum Computing in Healthcare

One space in healthcare that has already been looking at how quantum computing can help them overcome their challenges is that of medical imaging, particularly for management and tracking of cancer treatment. Current imaging methods for monitoring the prevalence of tumors during treatment all have their limitations. CT scans can give information on the shape and size only, and its resolution is low in comparison to other techniques.

PET scans on the other hand are able to trace the tumor by determining the metabolic activity of body tissues, but it requires injection of a radioactive tracer. MRI does boast a higher resolution, and can be non-invasive, but often an injection of a radioactive substance is also used. While MRI is considered to be high resolution, quantum computing is offering the possibility of seeing even more than we can see with this method.

Algorithm to Improve Medical Imaging

Researchers at Microsoft have teamed up with scientists at Case Western Reserve University in Cleveland to test an algorithm that has been designed to work on quantum computers with the aim of improving medical imaging by enhancing both the speed and quality.

The method is called magnetic resonance fingerprinting (MRF), similar to magnetic resonance imaging (MRI). Like MRI magnetic fields and radio waves are used to generate images, but the difference is, with the help of quantum computing it can look at single molecules or groups of molecules instead of the entire tissues. With MRI the image will only generate light or dark, and a radiologist then ‘translates’ these. The benefit with MRF is that the image generated is already able to differentiate between tissue type, giving a more accurate interpretation of what’s occurring inside the body.

Quantum computing can support this more fine grained analysis due to its ability to process and analyze data in parallel, making it significantly more powerful than conventional computers. It does this through replacing the transistors we find in traditional computers with qubits, which can store data as both 0s and 1s, rather than the traditional binary method of 0s or 1s. With the power to process data in parallel the quantum computer has a far greater capacity for information transfer and manipulation, this key quality allows it to not only make processes at higher speeds, but also allows it to receive more data, which in this case results in a higher definition image.

It’s not the first algorithm to have been designed in anticipation of the quantum computers that will be available to us in the near future. Algorithms have also been created to find better ways to manage the electrical grid, improve delivery routes in urban areas, and manage risks and returns in investment portfolios.

Conclusion

With Microsoft's quantum algorithm scans can be produced in as little as one sixth of the time it currently takes, as well as being 25% more precise, allowing doctors to see smaller changes in the tissue. The development of quantum imaging has the potential to improve treatment for cancer, particularly of breast cancer. Hopefully, it will give doctors the ability to understand whether a tumor is shrinking much faster than they currently can, which sometimes takes weeks or months.

Source

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Sarah Moore

Written by

Sarah Moore

After studying Psychology and then Neuroscience, Sarah quickly found her enjoyment for researching and writing research papers; turning to a passion to connect ideas with people through writing.

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