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Novel Photon-Counting CT Improves Myeloma Bone Disease Detection

By MedImaging International staff writers
Posted on 07 Sep 2022
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Image: New CT technology paired with AI-based noise reduction improves myeloma bone disease detection (Photo courtesy of Pexels)
Image: New CT technology paired with AI-based noise reduction improves myeloma bone disease detection (Photo courtesy of Pexels)

Multiple myeloma is a disease that forms in a type of white blood cell found in the bone marrow called a plasma cell. Bone disease characterized by areas of bone destruction known as lytic lesions is found in approximately 80% of multiple myeloma patients. Low-dose, whole-body CT is recommended to evaluate associated bone disease. Now, a study has found that new CT technology paired with artificial intelligence (AI)-based noise reduction offers superior detection of bone disease associated with multiple myeloma at lower radiation doses than conventional CT. The new technology, known as photon-counting detector CT, debuted in the clinic in 2021 after decades of development, although much less is known about the technology to evaluate associated bone disease.

The potential for improved image quality in whole-body low-dose scans inspired researchers at the Mayo Clinic (Rochester, MN, USA) to study the technology in people with multiple myeloma. 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. For their study, the researchers compared photon-counting detector CT with conventional low-dose, whole-body CT in 27 multiple myeloma patients, median age 68 years. The patients underwent whole-body scans with both types of CT and two radiologists compared the images. 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. The researchers now 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. 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.

“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,” said study lead author Francis Baffour, M.D., diagnostic radiologist at the Mayo Clinic, “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.”

“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 Baffour.

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