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심현보(Hyeon-Bo Shim)이광현(Kwang-Hyun Lee),김령희(Ryeon-Hee Kim),박순규(Soon-Kyoo Park),심지나(Ji-Na Shim) 대한CT영상기술학회 2021 대한CT영상기술학회지 Vol.23 No.1
기존 반복적 재구성법의 단점을 보안하고 보다 낮은 선량으로 좋은 질의 영상을 얻기 위해 인공 신경망(Artificial Neural Network)으로 학습시킨 딥러닝 반복적 재구성법(Deep Learning Iterative Reconstruction, DLIR)이 개발되었으며 복부 CT에서 GE사의 딥러닝 반복적 재구성법(True Fidelity, TF)과 Siemens사의 모델기반 반복적 재구성법(Advanced Modeled Iterative Reconstruction, ADMIRE)을 적용한 영상의 화질평가와 선량감소 효과를 비교해 보고자 하였다. Phantom study는 CTDIvol(mGy)을 9.5로 동일하게 설정한 TF(TF-L, TF-M, TH-H)와 ADMIRE(1,2,3,4,5)이 적용된 단면영상으로 복부(A), 뼈(B) ROI를 설정하였고, Patient study는 본원에서 검사를 시행한 환자 30명의 복부조영 CT 검사에서 TF(TF-M, TF-H), ADMIRE 2 적용된 단면영상으로 복부대동맥(A), 간실질(B), 근육(C), 백그라운드(D) ROI를 설정하여 HU 및 SD값을 측정하고 SNR, CNR을 비교 평가하였다. Phantom study에서 T F-M은 ADMIRE 1, 3, 5 보다 Noise는 ROI A에서 221%, 139%, 40%, B는 104%, 66%, 19% 낮았고,(p<0.05) SNR은 ROI A에서 70%, 59%, 33%, B는 53%, 42%, 19% 높았다.(p<0.05) Patient study에서 TF-M은 ADMIRE 2 보다 Noise는 ROI A에서 7% 낮았지만 통계적으로 유의하지 않았고,(p=0.28) B는 21%, C는 45% 낮았다.(p<0.05) SNR은 ROI A에서 동일하였지만 통계적으로 유의하지않았고,(p=0.70) B는 20%, C는 28% 높았다.(p<0.05) CNR은 ROI A에서 22%, B는 25%, C는 22% 높았다.(p<0.05) 선량평가에서 TF가 적용된 환자의 평균 CTDIvol(mGy)은 4.73 ± 1.28, DLP(mGy⋅cm)는 281.43 ± 79.22, ADMIRE가 적용된 환자의 평균 CTDIvol(mGy)은 6.06 ± 1.22, DLP(mGy⋅cm)는 343.3 ± 81.34 이였고,(p<0.05) TF가 ADMIRE가 적용된 환자보다 CTDIvol(mGy)은 28%, DLP(mGy⋅cm)는 21% 낮았음을 알 수 있다.(p<0.05) 복부 CT 검사에서 딥러닝 반복적 재구성법이 적용된 TF 영상이모델기반 반복적 재구성법이 적용된 ADMIRE 영상보다 HU값의 변화는 없지만, Noise가 감소하며 SNR과 CNR은 증가하는 것으로 나타났다. 또한 선량이 같다면 딥러닝 반복적 재구성법이 적용된 TF 영상이 모델기반 반복적 재구성법이 적용된 ADMIRE영상보다 화질이 향상되고 이는 다시 말해 환자의 피폭 선량을 저감하는데 큰 기여를 한다고 할 수 있다 Deep Learning Iterative Reconstruction (DLIR), which was trained with an artificial neural network, was developed to secure the shortcomings of the traditional iterative reconstruction method and obtain a good quality image at a lower dose. The purpose of this study was to compare the effects of dose reduction and image quality evaluation using deep learning iterative reconstruction (True Fidelity; TF) and Siemens Advanced Modeled Iterative Reconstruction (ADMIRE). Phantom study is a axial image with TF (TF-L, TF-M, TH-H) a nd ADMIRE (1,2,3,4,5) with CTDIvol (mGy) set e qual to 9.5. Abdomen (A), Bone (B) ROI w as set, and the patient study was a axial image applied with TF (TF-M, TF-H) and ADMIRE 2 in the abdominal contrast CT scan of 30 patients who performed the examination in our hospital. Abdomen aorta (A), Hepatic parenchyma (B), muscle (C), background (D) ROI w as s et to measure HU, SD values, a nd SNR , CNR were compared. In t he P hantom study, TF-M w as c ompared with ADMIRE 1, 3, 5. Noise was 221%, 139%, 40% lower in ROI A, 104%, 66%, 19% lower in B.(p<0.05) SNR was 70%, 59%, 33% higher in ROI A, 53, 42%, 19% higher in B.(p<0.05) In patient study, TF-M was compared with ADMIRE 2. Noise was 7% lower in ROI A but it was not statistically significant.(p=0.28) B was 21%, C was 45% lower.(p<0.05) SNR was the same in ROI A but not statistically significant.(p=0.70) B was 20%, C was 28% higher.(p<0.05) CNR was ROI A 22%, B 25%, C 22% higher.(p<0.05) In dose assessment, the average CTDIvol (mGy) of patients with TF applied was 4.73 ± 1.28, DLP (mGy cm) was 281.43 ± 79.22, and the average CTDIvol (mGy) of patients with ADMIRE was 6.06 ± 1.22, DLP (mGy cm) was 343.3 ± 81.34.(p<0.05) It can be seen that TF was lower in CTDIvol (mGy) by 28% and DLP (mGy cm) by 21% than in patients with ADMIRE.(p<0.05) In conclusion, TF images on abdominal CT showed no change in HU values than ADMIRE images, but Noise decreased and SNR and CNR increased. If the dose is the same, the TF image is applied has better image quality than the ADMIRE image. It can be said that it contributes significantly to reducing the patient s exposure dose.
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