Document Type : Original Article


1 Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran

2 Retired Scientist from Indian Institute of Astrophysics, Bangalore, India

3 Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India


Background: The present study was conducted to examine the possibility of detecting different types of lung lesions, such as cancer, using ultra-low dose (ULD) chest computed tomography (CT) images.
Method: In this basic (experimental) study with CT images, 20 patients with different lung disease indications were scanned with ULD and routine dose chest CT protocols. ULD and routine dose CT images were reconstructed utilizing iDose and iterative model reconstruction. CT images were evaluated by two expert radiologists. Volume CT dose index (CTDIvol), dose length product, and effective dose were used for dose assessment in both protocols.
Results: CTDIvol and dose length product for ULD protocol were 98% less compared to those for routine chest CT. The chest CT images for ULD and routine dose were diagnosed as normal in three patients with lung lesions, such as nodules, masses, plural effusion, fibrosis, diffuse ground glass opacities, bronchiectasis, and infiltration, in 17 patients. Patient dose of ULD chest CT (0.11mSv) is comparable to Poster-Anterior plus Lateral (0.1 mSv) chest radiograph, while the effective dose due to routine chest CT is about 5.1 mSv.
Conclusion: Diagnostic findings regarding ULD chest CT images with 98% of dose reduction were compared to those for routine dose. We concluded that it may be utilized as a very useful tool for screening and the follow-up of different lung diseases, malignancy for instance. ULD chest CT with 98% of dose reduction could be a suitable substitute for chest radiograph, with higher diagnostic values.


How to cite this article:

Jalli R, Zarei F, Chatterjee S, Ravanfar Haghighi R, Novshadi A, Iranpour P, et al. Evaluation of ultra-low-dose chest CT images to detect lung lesions. Middle East J Cancer. 2022;13(2):299-307. doi: 10.30476/mejc.2021.87355.1410.

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