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)
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.
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.
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)
Radiologists individually assessed and documented their findings from the normal dose CT, ultra-low-dose CT and denoised ultra-low-dose CT scans. They were blinded to all patient clinical information.
The deep learning algorithm significantly improved the image quality and clarity of the ultra-low-dose CT scans and reduced false positives. Nodules were also more easily identified on the denoised scans.
The average effective radiation dose for ultra-low-dose scans was 2% of the average effective radiation dose of the standard CT scans.
“This study paves the way for safer, AI-driven imaging that reduces radiation exposure while preserving diagnostic accuracy,” Dr. Klug said.
The researchers note that deep learning-based denoising on ultra-low-dose CT scans can be beneficial in other patient groups, such as young patients.
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)
“This pilot study identified infection with a fraction of the radiation dose,” Dr. Klug said. “This approach could drive larger studies and ultimately reshape clinical guidelines, making denoised ultra-low-dose CT the new standard for young immunocompromised patients.”
Future studies with larger sample sizes will help validate the findings from this study.
More information:
Denoised Ultra-Low-Dose Chest CT to Assess Pneumonia in Individuals Who Are Immunocompromised Individuals, Radiology Cardiothoracic Imaging (2025).
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Radiological Society of North America
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Ultra-low-dose CT can diagnose pneumonia in immunocompromised patients while using far less radiation (2025, March 13)
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