Ultra-low-dose CT reconstructed with the artificial intelligence iterative reconstruction algorithm (AIIR) in 18F-FDG total-body PET/CT examination: a preliminary study

Abstract

Purpose To investigate the feasibility of ultra-low-dose CT (ULDCT) reconstructed with the artificial intelligence iterative reconstruction (AIIR) algorithm in total-body PET/CT imaging. Methods The study included both the phantom and clinical parts. An anthropomorphic phantom underwent CT imaging with ULDCT (10mAs) and standard-dose CT (SDCT) (120mAs), respectively. ULDCT was reconstructed with AIIR and hybrid iterative reconstruction (HIR) (expressed as ULDCT-AIIRphantom and ULDCT-HIRphantom), respectively, and SDCT was reconstructed with HIR (SDCT-HIRphantom) as control. In the clinical part, 52 patients with malignant tumors underwent the total-body PET/CT scan. ULDCT with AIIR (ULDCT-AIIR) and HIR (ULDCT-HIR), respectively, was reconstructed for PET attenuation correction, followed by the SDCT reconstructed with HIR (SDCT-HIR) for anatomical location. PET/CT images’ quality was qualitatively assessed by two readers. The CTmean, as well as the CT standard deviation (CTsd), SUVmax, SUVmean, and the SUV standard deviation (SUVsd), was recorded. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and compared. Results The image quality of ULDCT-HIRphantom was inferior to the SDCT-HIRphantom, but no significant difference was found between the ULDCT-AIIRphantom and SDCT-HIRphantom. The subjective score of ULDCT-AIIR in the neck, chest and lower limb was equivalent to that of SDCT-HIR. Besides the brain and lower limb, the change rates of CTmean in thyroid, neck muscle, lung, mediastinum, back muscle, liver, lumbar muscle, first lumbar spine and sigmoid colon were −2.15, −1.52, 0.66, 2.97, 0.23, 8.91, 0.06, −4.29 and 8.78%, respectively, while all CTsd of ULDCT-AIIR was lower than that of SDCT-HIR. Except for the brain, the CNR of ULDCT-AIIR was the same as the SDCT-HIR, but the SNR was higher. The change rates of SUVmax, SUVmean and SUVsd were within $$\pm$$ ± 3% in all ROIs. For the lesions, the SUVmax, SUVsd and TBR showed no significant difference between PET-AIIR and PET-HIR. Conclusion The SDCT-HIR could not be replaced by the ULDCT-AIIR at date, but the AIIR algorithm decreased the image noise and increased the SNR, which can be implemented under special circumstances in PET/CT examination.

Long Axial Field-of-View PET/CT Could Answer Unmet Needs in Gynecological Cancers

“…Moreover, it is now being hypothesized that diagnostic PET images alone can be obtained with no or very low-dose CT (currently needed for attenuation correction) through deep learning and new reconstruction protocols [3,67,[70][71][72].…”

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

“…In this case, the associated attenuation correction CT scan becomes the primary source of ionizing radiation (2-10 mSv of radiation dose vs. 1 mSv from the PET). Therefore, low-dose CT acquisition [41] or a CT-less fully quantitative PET imaging method is desired, and would be particularly suitable for long-axial-FOV PET scanners. Montgomery et al [42] proposed a deep learning-driven synthetic CT generation procedure iRADIOLOGY where the image is produced directly from the PET nonattenuation corrected images, thereby eliminating the need for a separate CT scan on Quadra.…”

“…In this case, the associated attenuation correction CT scan becomes the primary source of ionizing radiation (2–10 mSv of radiation dose vs. 1 mSv from the PET). Therefore, low‐dose CT acquisition [41] or a CT‐less fully quantitative PET imaging method is desired, and would be particularly suitable for long‐axial‐FOV PET scanners. Montgomery et al.…”