Quality of Images Reconstructed by Deep Learning Reconstruction Algorithm for Head and Neck CT Angiography at 100 kVp / 中国医学科学院学报

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.

Evaluation on image quality and radiation dose of 640-slice CT coronary arteriography with AIDR3D reconstruction algorithm / 重庆医学

Objective To investigate the image quality and radiation dose of 640-slice CT coronary arteriography(CTCA) with adaptive iterative dose reduction three-dimensional (AIDR3D)reconstrucction algoritym.Methods 640-slice CTCA with auto-matic exposure was performed on 84 consecutive patients.The original image data were reconstructed with AIDR3D and the filtered back-projection (FBP)algorithms at the image postprocessing workstation.Two experienced radiologists without knowing clinical information and reconstruction algorithms independently measured and calculated the image noise,signal-to-noise ratio and contrast-to-noise ratio with AIDR3D and FBP reconstruction algorithms.The qualitative image quality was assessed by using the 4-point scale.The radiation dose was calculated based on dose-length product exported on CT scanner.The quantitative and qualitative im-age quality with two kinds of reconstruction algorithm was analyzed statistically.Results The CTCA image noise was (27.20± 4.40)HU with AIDR3D and (60.00±12.40)HU with FBP,which with AIDR3D was decreased by 46.10% than that with FBP;the signal-to-noise ratio was 21.10 ± 5.10 with AIDR3D and 11.40 ± 2.80 with FBP,which with AIDR3D was increased by 84.70% than that with FBP;the contrast-to-noise ratio was 24.70±5.10 with AIDR3D and 13.50±3.20 with FBP,which with AIDR3D was raised by 82.20% than that with FBP,the differences in 3 indexes between the two kinds of reconstruction algorithm were statistically significant(P < 0.05 ).The CTCA qualitative image quality scores of proximal,middle and distal parts with AIDR3D were (3.90±0.30),(3.70±0.50)and (3.60±0.60)respectively,which all were higher than (2.60±0.60),(2.30± 0.60)and (2.10±0.70)with FBP respectively,the differences in 3 items between 2 kinds of algorithm had statistical significance (P <0.05).The total segments which could be used to diagnose the CTCA images with AIDR3D and FBP algorithms were 1 216 segments (96.50%)and 504 segments (40.00%),respectively,the difference had statistical significance(P <0.05).The mean ef-fective radiation dose was (2.10±1.00)mSv.Conclusion 640-slice CTCA with AIDR3D reconstruction algorithm not only signifi-cantly reduces the image noise than the conventional FBP algorithm,improves the quantitative and qualitative image quality,but also decreases the effective radiation dose.

Adaptive Iterative Dose Reduction Algorithm in CT: Effect on Image Quality Compared with Filtered Back Projection in Body Phantoms of Different Sizes

OBJECTIVE: To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. MATERIALS AND METHODS: Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. RESULTS: Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). CONCLUSION: The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.