New computed tomography (CT) technology paired with artificial intelligence (AI)-based noise reduction may offer superior detection of bone disease associated with multiple myeloma at lower radiation doses than conventional CT, according to a recent study published by Baffour et al in Radiology.
The new technology, known as photon-counting detector CT, debuted in the clinic in 2021 after decades of development. By directly converting individual x-ray photons into an electric signal, photon-counting detector CT can decrease the detector pixel size and improve the image’s spatial resolution.
“Additionally, photon-counting CT has demonstrated much better dose efficiency than standard CT, which allows for acquisition of ultra–high-resolution images of large areas of the body,” said lead study author Francis Baffour, MD, a diagnostic radiologist at the Mayo Clinic in Rochester, Minnesota.
This potential for improved image quality in whole-body low-dose scans inspired Dr. Baffour and colleagues to study the technology in people with multiple myeloma. Bone disease characterized by areas of bone destruction known as lytic lesions is found in approximately 80% of patients with multiple myeloma.
The International Myeloma Working Group recommends low-dose whole-body CT to evaluate associated bone disease. Much less is known about photon-counting detector CT in this setting.
Current Study
Dr. Baffour and colleagues compared photon-counting detector CT with conventional low-dose whole-body CT in 27 patients with multiple myeloma (median age = 68 years). The patients underwent whole-body scans with both types of CT, and two radiologists compared the images.
“We felt this was a prime example to showcase the ultra–high-resolution of photon-counting CT at low scanning doses,” Dr. Baffour explained.
The researchers also applied a deep learning AI technique developed at Mayo Clinic’s CT Clinical Innovation Center to reduce the noise in the very sharp photon-counting images. CT noise refers to an unwanted change in pixel values in the image, often loosely defined as the grainy appearance on cross-sectional imaging. The photon-counting detector CT with deep-learning noise reduction demonstrated improvement in visualization and detected more lesions relative to conventional CT.
“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, with deep learning de-noising techniques that allowed us to generate thinner image slices, we were able to detect more lesions than on the standard CT,” said Dr. Baffour.
The researchers hope to conduct follow-up studies on patients with multiple myeloma precursor states to see if photon-counting detector CT finds bone lesions that would upstage these patients to active multiple myeloma.
“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,” added Dr. Baffour.
They also want to look at photon-counting detector CT in other instances in which low-dose protocols are beneficial—for instance, in pediatric or pregnant patients or screening applications.
“Already there are ongoing studies to determine how low we can go with scanning doses while still obtaining diagnostic CT images,” Dr. Baffour said. “So, there is much on the horizon and so much potential for photon-counting detector CT in clinical care.”
Disclosure: For full disclosures of the study authors, visit pubs.rsna.org.
