Objective:To evaluate the effectiveness of deep learning reconstruction (DLR) compared with hybrid iterative reconstruction (Hybrid IR) in improving the image quality in chest low-dose CT (LDCT).Methods:Seventy-seven patients who underwent LDCT scan for physical examination or regular follow-up in Peking Union Medical College Hospital from October 2020 to March 2021 were retrospectively included. The LDCT images were reconstructed with Hybrid IR at standard level (Hybrid IR Stand) and DLR at standard and strong level (DLR Stand and DLR Strong). Regions of interest were placed on pulmonary lobe, aorta, subscapularis muscle and axillary fat to measure the CT value and image noise. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated. Subjective image quality was evaluated using Likert 5-score method by two experienced radiologists. The number and features of ground-glass nodule (GGN) were also assessed. If the scores of the two radiologists were inconsistent, the score was determined by the third radiologist. The objective and subjective image evaluation were compared using the Kruskal-Wallis test, and the Bonferroni test was used for multiple comparisons within the group.Results:Among Hybrid IR Stand, DLR Stand and DLR Strong images, the CT value of pulmonary lobe, aorta, subscapularis muscle and axillary fat had no significant differences (all P>0.05), but the image noise and SNR of pulmonary lobe, aorta, subscapularis muscle and axillary fat had significant differences(all P<0.05), and the CNR of images had significant difference( P<0.05), too. The CNR of Hybrid IR Stand images, DLR stand images and DLR strong images were 0.71 (0.49, 0.88), 1.06 (0.78, 1.32) and 1.14 (0.84, 1.48), respectively. Compared with Hybrid IR images, DLR images had lower objective and subjective image noise,higher SNR and CNR (all P<0.05). The scores of DLR images were superior to Hybrid IR images in identifying lung fissures, pulmonary vessels, trachea and bronchi, lymph nodes, pleura, pericardium and GGN (all P<0.05). Conclusions:DLR significantly reduced the image noise, and DLR images were superior to Hybrid IR images in identifying GGN in chest LDCT while maintaining superior image quality at relatively low radiation dose levels. Thus DLR images can improve the safety of lung cancer screening and pulmonary nodule follow-up by CT.

Objective:To explore the effect of deep learning reconstruction (DLR) on radiation dosage reduction and image quality of CTPA compared with hybrid iterative reconstruction (HIR).Methods:A total of 100 patients with suspected pulmonary embolism (APE) or indications for CTPA due to other pulmonary artery diseases in Peking Union Medical College Hospital from December 2020 to April 2021 were prospectively enrolled and divided into HIR group and DLR group according to block randomization, with 50 cases in each group. The patient′s gender, age and body mass index (BMI) were recorded. HIR group and DLR group underwent standard deviation (SD)=8.8 and SD=15 CTPA protocols in combination with HIR and DLR algorithm respectively. Other scanning parameters and contrast medium injection plan were the same. The effective dose (ED) and size-specific dose estimate (SSDE) were calculated. Regions of interest (ROIs) were drawn in the lumen of Grade 1-3 pulmonary arteries and bilateral paravertebral muscles. The corresponding CT and SD values were recorded to acquire signal to noise ratio (SNR) and contrast noise ratio (CNR). Based on a double-blind method, two radiologists evaluated the subjective noise, visualization of pulmonary arteries, and diagnostic confidence of the two groups by 5-point Likert scales. The inconsistent results were judged comprehensively by the third radiologist. Independent samples t-test was used to compare the demographic data, radiation dosage and quantitative image quality of the two groups. Mann-Whitney U test was used to compare the subjective noise, visualization of pulmonary arteries and diagnostic confidence between the two groups. Linear weighted Kappa coefficient was calculated to analyze the consistency of the qualitative scores between the two radiologists. Results:There were no significant differences in gender, age and BMI between the two groups ( P>0.05). The CT values of Grade1-3 pulmonary arteries and paravertebral muscle had no significant differences ( P>0.05). Compared with HIR group, the ED and SSDE in DLR group decreased by about 35% to 1.3 mSv and 4.20 mGy respectively, while the SNR (30±5) and CNR (26±5) of CTPA images were higher in DLR group than those in HIR group (23±5 and 20±5, with t=-6.60 and -5.90, respectively, both P<0.001). The subjective noise score was higher in DLR group than that in HIR group ( Z=-7.34, P<0.001). In addition, two radiologists showed excellent interobserver agreement in DLR group (Kappa=0.847, 95%CI 0.553-1.000). No significant differences were found in visualization of pulmonary arteries and diagnostic confidence between the two groups ( P>0.05). Conclusion:DLR further reduced the radiation dosage and improved the image quality of CTPA, with no detriment to diagnostic confidence. Thus DLR is worthy of clinical promotion.

Objective:To investigate the feasibility of chest ultra-low dose CT (ULDCT) using deep learning reconstruction (DLR) for lung cancer screening, and to compare its image quality and nodule detection rate with ULDCT iterative reconstruction (Hybrid IR) and conventional dose CT (RDCT) Hybrid IR.Methods:The patients who underwent chest CT examination for pulmonary nodules in Peking Union Medical College Hospital from October 2020 to March 2021 were prospectively included and underwent chest RDCT (120 kVp, automatic tube current), followed by ULDCT (100 kVp, 20 mA). The RDCT images were reconstructed with Hybrid IR (adaptive iterative dose reduction 3D,AIDR 3D), and ULDCT was reconstructed with AIDR3D and DLR. Radiation dose parameters and nodule numbers were recorded. Image quality was assessed using objective noise, signal-to-noise ratio (SNR) of the main trachea and left upper lobe, subjective image scores of the lung and nodules. Subjective scores were scored by 2 experienced radiologists on a Likert 5-point scale. The difference of radiation dose was compared with paired t-test between ULDCT and RDCT.The differences of quantitative indexes, objective image noise and subjective scores of the three reconstruction methods were compared with one-way analysis of variance or Friedman test. Results:Forty-five patients were enrolled, including 17 males and 28 females, aged from 32 to 74 (55±11) years. The radiation dose of ULDCT was (0.17±0.01) mSv, which was significantly lower than that of RDCT [(1.35±0.41) mSv, t=15.46, P<0.001]. There were significant differences in the image noise and SNR in the trachea and lung parenchyma and in the CT value of the trachea among ULDCT-AICE, ULDCT-AIDR 3D and RDCT-AIDR 3D images ( P<0.05). Image noise in the trachea and lung parenchyma and CT value in the trachea of ULDCT-AICE were significantly lower than those of ULDCT-AIDR 3D ( P<0.05) and comparable to RDCT-AIDR 3D ( P>0.05). There were significant differences in subjective image scores of the lung and nodules among ULDCT-AICE, ULDCT-AIDR 3D and RDCT-AIDR 3D images (χ2=50.57,117.20, P<0.001). Subjective image scores of the lung and nodules for ULDCT-AICE were significantly higher than those of ULDCT-AIDR 3D ( P<0.05), and non-inferior to RDCT-ADIR 3D ( P>0.05). All 72 clinically significant nodules detected on RDCT-ADIR 3D were also noted on ULDCT-AICE and ULDCT-AIDR 3D images. Conclusions:Chest ULDCT using DLR can significantly reduce the radiation dose, and compared with Hybrid IR, it can effectively reduce the image noise and improve SNR, and display the pulmonary nodules well. The image quality and nodule detection are not inferior to RDCT Hybrid IR routinely used in clinical practice.