Artificial Intelligence (AI): The Next Step Toward Safer, Clearer CT Imaging

Computed tomography (CT) imaging requires high doses of radiation to produce clear images — and many patients know the risks this poses to their health–their providers do as well. But while simply lowering the radiation dose may seem like a prudent safety measure, it tends to produce ‘noisier’ CT images, which are grainy or blurry and make it more difficult for clinicians to diagnose subtle pathologies. Failing to identify these problems in their early stages can be far more dangerous for the patient than a higher radiation dose, so radiologists are left to navigate a delicate calculus, conducting diagnostic imaging at dosages “as low as reasonably achievable.” However, this is not their only challenge: After the exam is completed, reading CT images produced at the lowest possible dose can harm radiologists’ efficiency, clinical confidence, and ability to produce reports in a timely manner. All these challenges can compound each other to leave a diagnostic imaging clinic operating at a level of efficiency and diagnostic accuracy that falls far short of its potential.

However, there are other ways to improve CT images’ quality without increasing the radiation dose. Introduced in the late 2000s, iterative reconstruction (IR) is one technique that reduces image noise. Although they are far clearer than conventional CT images, IR images come with trade-offs: They have a longer processing time, there are limits to how much the radiation dose can be reduced, and they are not appropriate for all use cases. The algorithms are also proprietary to specific vendors and scanners, meaning that facilities hoping to take advantage of IR may need to fully replace their equipment, slowing clinics’ adoption of this valuable technology. To keep building on IR’s advantages and avoid its drawbacks, it is necessary to move on to newer technologies to improve CT image quality in the reconstruction and post-processing phases.

Artificial Intelligence (AI) Based Software: The Next Step Forward

Artificial intelligence-based machine learning tools process CT images in seconds, and they reduce noise at many different doses and for many different exam types, a versatility that IR has never been able to offer. This advance is transformational: For the first time, clinicians may not need to choose between clear images and a low radiation dose, resulting in better health outcomes for the patients and better operational and financial ones for healthcare providers. Even better, some new AI-based software are vendor-neutral, meaning that they can be used with older CT scanners across multiple vendors. This makes image quality improvement accessible to all clinics, including those for whom IR was difficult to adopt. Indeed, these smaller, under-resourced clinics have the most to gain, because the ability to produce better images from older scanners lengthens the scanners’ overall diagnostic life. In addition, they can optimize the CT imaging workflow to shrink patient backlogs and unburden radiologists of the challenge of reading unclear images.

Facilities that adopt Artificial intelligence-based post-processing technologies are laying the groundwork to excel in employee satisfaction, patient outcomes, CT image quality, and diagnostic accuracy. These benefits will translate into long-term financial and operational sustainability for the clinic, and long-term health benefits for patients.

PixelShine Deep Learning Imaging Solutions is available through Radiology Oncology Systems, Inc. (“ROS”).  PixelShine allows any refurbished CT Scanner sold by ROS to deliver clearer images with lower doses to help keep patients safe.  For more information, e-mail us at Info@RadiologySystems.com.