Ultra-low-dose CT can diagnose pneumonia in immunocompromised patients while using far less radiation
Denoised ultra-low-dose CT can effectively diagnose pneumonia in immunocompromised patients using only 2% of the radiation dose of standard CT, according to a study published in Radiology: Cardiothoracic Imaging.
"For patients with weakened immune systems, lung infections can be life threatening," said lead study author Maximiliano Klug, M.D., a radiologist in the division of diagnostic imaging at the Sheba Medical Center in Ramat Gan, Israel. "CT scans are the gold standard for detecting pneumonia, but repeated scans can expose patients to significant radiation."
While the early diagnosis of lung infections in immunocompromised patients is important, the risks of cumulative radiation dose exposure from frequent CT scans is a concern.
While the early diagnosis of lung infections in immunocompromised patients is important, the risks of cumulative radiation dose exposure from frequent CT scans is a concern.
Ultra-low-dose CT reduces radiation exposure but can result in poor image quality due to added "noise," which manifests as a grainy texture throughout the image. This reduction in image quality can affect the accuracy of diagnosis. Therefore, Dr. Klug and colleagues sought to test the denoising capabilities of a deep learning algorithm on ultra-low-dose CT scans.
From September 2020 to December 2022, 54 immunocompromised patients with fevers were referred to Dr. Klug's division to undergo two chest CT scans: a normal-dose scan and an ultra-low-dose scan. A deep learning algorithm was applied to denoise all 54 of the ultra-low-dose CT scans.
From September 2020 to December 2022, 54 immunocompromised patients with fevers were referred to Dr. Klug's division to undergo two chest CT scans: a normal-dose scan and an ultra-low-dose scan. A deep learning algorithm was applied to denoise all 54 of the ultra-low-dose CT scans.
Axial noncontrast chest CT lung window images in a 42-year-old male participant with normal lungs. (A) Normal-dose CT, (B) ultra-low-dose CT (ULDCT), and (C) denoised ULDCT images. Normal lungs were observed on normal-dose CT image. However, due to inherent image noise at ULDCT, the lung pattern was falsely classified as positive viral infection by both readers. The denoising technique of the denoised ULDCT corrected this artifact, and the participant was correctly categorized as having no infection. Credit: Radiological Society of North America (RSNA)
Axial noncontrast chest CT lung window images in a 61-year-old female participant. (A) Normal-dose CT, (B) ultra-low-dose CT (ULDCT), and (C) denoised ULDCT show focal ground-glass opacity (yellow arrow). Ground-glass opacity was correctly identified with both normal- dose CT and denoised ULDCT, but it was missed by both readers at ULDCT due to decreased signal-to-noise ratio. Credit: Radiological Society of North America (RSNA)
Axial noncontrast chest CT lung window images in a 70-year-old male participant. (A) Normal-dose CT, (B) ultra-low-dose CT (ULDCT), and (C) denoised ULDCT images show tree-in-bud opacities (yellow arrow). The tree-in-bud opacities can be observed on normal-dose CT image. However, due to the increased image noise at ULDCT, the linear branching pattern was missed and classified incorrectly by both readers as nodules with no tree-in-bud opacities. Denoised ULDCT allowed better appreciation of centrilobular nodules with a linear branching pattern, and the image was classified correctly as positive for tree-in-bud opacities. Credit: Radiological Society of North America (RSNA)