Image quality in children with low-radiation chest CT using adaptive statistical iterative reconstruction and model-based iterative reconstruction.
OBJECTIVE
To evaluate noise reduction and image quality improvement in low-radiation dose chest CT images in children using adaptive statistical iterative reconstruction (ASIR) and a full model-based iterative reconstruction (MBIR) algorithm.


METHODS
Forty-five children (age ranging from 28 days to 6 years, median of 1.8 years) who received low-dose chest CT scans were included. Age-dependent noise index (NI) was used for acquisition. Images were retrospectively reconstructed using three methods: MBIR, 60% of ASIR and 40% of conventional filtered back-projection (FBP), and FBP. The subjective quality of the images was independently evaluated by two radiologists. Objective noises in the left ventricle (LV), muscle, fat, descending aorta and lung field at the layer with the largest cross-section area of LV were measured, with the region of interest about one fourth to half of the area of descending aorta. Optimized signal-to-noise ratio (SNR) was calculated.


RESULTS
In terms of subjective quality, MBIR images were significantly better than ASIR and FBP in image noise and visibility of tiny structures, but blurred edges were observed. In terms of objective noise, MBIR and ASIR reconstruction decreased the image noise by 55.2% and 31.8%, respectively, for LV compared with FBP. Similarly, MBIR and ASIR reconstruction increased the SNR by 124.0% and 46.2%, respectively, compared with FBP.


CONCLUSIONS
Compared with FBP and ASIR, overall image quality and noise reduction were significantly improved by MBIR. MBIR image could reconstruct eligible chest CT images in children with lower radiation dose.
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Jihang
J
Sun
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Yun
Y
Peng
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Xiaomin
X
Duan
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Tong
T
Yu
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Qifeng
Q
Zhang
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Yong
Y
Liu
Imaging Center, Beijing Children's Hospital, Capital Medical University, Beijing, China.
Di
D
Hu
D000273:Adipose Tissue / Q000000981:diagnostic imaging / Q000473:pathology; D000465:Algorithms; D001013:Aorta, Thoracic / Q000000981:diagnostic imaging / Q000473:pathology; D001699:Biometry; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008168:Lung / Q000000981:diagnostic imaging / Q000473:pathology; D008297:Male; D015233:Models, Statistical; D018482:Muscle, Skeletal / Q000000981:diagnostic imaging / Q000473:pathology; D011014:Pneumonia / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000473:pathology; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013899:Thoracic Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000706:statistics & numerical data*