Photon-counting CT/AI combo improves multiple myeloma detection
When combined with artificial intelligence-based noise reduction techniques, new photon-counting CT technology can increase the detection of bone disease while also decreasing radiation exposure.
These findings were published recently in Radiology, a journal of the Radiological Society of North America. In the paper, researchers detail how they were able to use the innovative method on patients with multiple myeloma to obtain high resolution images of lytic lesions that are more difficult to identify on standard, whole-body CT scans.
The experts compared the imaging of 27 patients who had undergone imaging with both the photon-counting detector and a conventional low-dose, whole-body CT. Following reconstructions, the images were denoised using a convolutional neural network developed by Mayo Clinic’s CT Clinical Innovation Center, and later scored by two radiologists on their ability to increase the detection of findings indicative multiple myeloma.
The experts found that the denoising of the photon-counting detector (PCD) 2 mm images allowed for better visualization of lytic lesions, intramedullary lesions, fatty metamorphosis and pathologic fractures. The 0.6 mm PCD images also enabled the radiologists to identify more lytic lesions—on 21 out of 27 patients—relative to the standard images.
Lead author Francis Baffour, MD, diagnostic radiologist at the Mayo Clinic in Rochester, Minnesota, and colleagues noted that this new research offers yet another great example of the many benefits of photon-counting CT technology, citing the “ultra-high” resolution images obtained as further validation.
“We were excited to see that not only were we able to detect these features of multiple myeloma disease activity more clearly on the photon-counting scanner,” Baffour said in a statement, “with deep learning denoising techniques that allowed us to generate thinner image slices, we were able to detect more lesions than on the standard CT.”
In the future, the experts plan to test the technology’s efficacy on patients with multiple myeloma precursor states to determine whether the image would result in upstaging.
“Our excitement as scientists and radiologists in these results stems from our realization that this scanner could make a difference in the staging of disease, potentially impact therapy choice, and ultimately, patient outcomes.”
The study abstract can be viewed here.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She joined Innovate Healthcare in 2021 and has since put her unique expertise to use in her editorial role with Health Imaging.