Hybrid iterative reconstruction in ultra-low-dose CT for accurate pulmonary nodule assessment: A Phantom study

This study evaluated hybrid iterative reconstruction in ultra-low-dose computed tomography (ULDCT) for solid pulmonary nodule detection. A 256-slice CT machine operating at 120 kVp imaged a chest phantom with 5 mm nodules. The imaging process involved adjusting low-dose computed tomography (LDCT) settings and conducting 3 ULDCT scans (A-C) with varied minimum and maximum mA settings (10/40 mA). Images were processed using iDose4 iterative reconstruction at levels 5 to 7. Measurements were taken for noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise power spectrum (NPS), and detectability index (D') to assess image quality, noise texture, and detectability. Analysis of variance (ANOVA) was used to compare the protocols. Noise levels varied significantly across iDose4 iterative reconstruction levels, with the highest noise at 178 HU in iDose4 L5 (protocol C) and the lowest at 54.85 HU in level 7 (protocol A). ULDCT scans showed noise increases of 38.5%, 104.2%, and 118.7% for protocols A, B, and C, respectively, compared to LDCT. Protocol A (iDose4 level 7) significantly improved SNR and CNR (P < .001). The mean volume CT dose index was 2.4 mGy for LDCT and 2.0 mGy, 1.2 mGy, and 0.7 mGy for ULDCT protocols A, B, and C, respectively. Increasing iDose4 levels reduced noise magnitude in the NPS and improved the D'. ULDCT with iDose4 level 7 provides diagnostically acceptable image quality for solid pulmonary nodule assessment at significantly reduced radiation doses. This approach, supported by advanced metrics like NPS and D', demonstrates a potential pathway for safer, effective lung cancer screening in high-risk populations. Further clinical studies are needed to validate these findings in diverse patient populations.