CT image noise reduction software; Why PixelShine outshines the competition!
Many facilities are interested in improving their CT scans’ image quality at a lower dose of radiation—and with good reason. Achieving clear imaging at lower doses may reduce the scanning process’s risks for patients, and there is an upside for facilities, as well: If they can acquire better images at lower radiation levels, they can extend the life of their X-ray tubes, making scans more cost-effective.
Some might assume that buying a new CT scanner is the only way to achieve these benefits and get better image quality, but that is not the case. Noise reduction software developed using machine learning, such as PixelShine® from AlgoMedica, can improve image quality at a fraction of the price of a new CT scanner, and implementation is simple. Because of this, large and small facilities alike have turned their attention to such software as alternatives to purchasing a new CT scanner.
But once facilities have decided to use such software to enhance their CT scanners’ imaging capability, they face another question: Which to choose?
Some common options include GE’s TrueFidelity, Canon’s AiCE, and AlgoMedica’s PixelShine. These comprise a third generation of CT reconstruction tools, different from previous technologies in their use of machine learning-based reconstruction techniques. Earlier generations of tools used the iterative reconstruction approach, which can produce a waxy appearance when applied to noisy, low-dose images.
Whether they are considering second- or third-generation software, imaging organizations looking to improve CT image quality typically consider the following questions:
- Is the technology compatible with the CT scanners they already own? Right now, the only two universally compatible noise reduction technologies are PixelShine and SafeCT. Similar technologies offered by Cannon and GE are only available on select models in their product lines, so facilities seeking flexibility should focus on the two former technologies—and of these, PixelShine is the only one that was developed using machine learning technology.
- Is it easy to use and implement? PixelShine is also uniquely fast and easy to use. There is no user interface or configuration, and it works on refurbished CT scanners as well as new ones. While CT scanner vendors may eventually offer technologies that match PixelShine’s image quality, the software will likely only be available on their highest-end scanners. Pairing this technique with affordable and refurbished scanners will enable facilities to provide their radiologists and their patients the highest-quality images at the lowest possible dose. Indeed, these facilities may see the best return on their investment in PixelShine, since noisy, low-dose scans tend to see the most quality improvement when used with this software.
- Is it cost-effective across a range of clinical applications? Evidence suggests that PixelShine stacks up favorably against competing technologies. This may be due to its unique noise reduction capability, which was developed using an artificial intelligence neural network to learn from large volumes of high-dose and low-dose CT scan data. It uses this data to learn how to reduce noise without iterative reconstruction, making it categorically different from previous second-generation technologies.
Ultimately, PixelShine offers greater value than its competitors, both to the facilities that use it and to their patients. As an evidence-based solution that leads the market in providing clear imaging, it empowers radiologists to be more productive and enjoy higher clinical confidence. It also offers full compatibility with all CT scanners and works automatically, saving facilities time and effort to harmonize image quality across all their CT scanners.