Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising

Background: Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. Method: We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. Results: Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. Conclusion: We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field. © 2023 The Author(s)ope

딥러닝을 이용한 저선량 전산화 단층촬영의 영상 화질 향상

학위논문 (박사)-- 서울대학교 대학원 : 의과대학 임상의과학과, 2019. 2. 김영훈.서론: 저선량 전산화 단층촬영에서 FBP (filtered back projection) 및 ADMIRE (advanced modeled iterative reconstruction)와 비교하여 딥러닝 기반 알고리즘 (deep learning algorithmDLA)을 이용하였을 때의 영상 화질 향상에 대한 연구이다. 방법: 이 후향적 연구는 기관 검토위원회의 승인을 받았다. FBP를 이용한 정상 선량 (routine dose, RD) 복부 CT를 시행한 총 100 명의 환자를 대상으로 딥러닝 알고리즘의 훈련 세트를 만들었다. RD CT 영상으로부터 13 %, 25 %, 50 %의 선량 수준의 저선량 CT 영상을 시뮬레이션하고 FBP를 이용하여 재구성하였다. 우리는 시뮬레이션 된 저선량 CT 이미지를 입력 데이터로 사용하고 정상선량 CT 이미지를 정답으로 하여 다양한 조건에서 DLA를 훈련시켰다. DLA의 유효성을 확인하기 위해 평균화 제곱 오류 (Mean squared error, MSE)를 의인화 팬텀을 사용하여 측정했다. 훈련과 검증을 거친 DLA를 시험하기 위해 팬텀을 이용한 연구와, 18명의 저선량 복부 CT를 시행한 환자를 대상으로 한 연구를 수행하였다. 각 연구에서 FBP, ADMIRE 및 DLA를 이용하여 팬텀 및 환자의 저선량 CT 영상을 재구성하였다. 각각의 방법으로 재구성된 영상에서 영상 품질을 테스트하고 비교하기 위해서, ACR 팬텀을 사용하여 잡음 전력 스펙트럼 (noise power spectrum, NPS)과 변조 전달 함수 (modulation transfer function, MTF)를 측정하고 환자 데이터를 사용하여 평균 영상 잡음(mean image noise)을 측정했다. 결과: LD-DLA는 팬텀 및 환자 연구 모두에서 LD-FBP 및 LD-ADMIRE보다 낮은 잡음 수준을 보였다. 팬텀 연구에서, LD-DLA의 NPS 곡선의 피크 값과 AUC는 LD-FBP 또는 LD-ADMIRE보다 낮았다. 환자 연구에서 LD-DLA 이미지는 LD-ADMIRE 이미지보다 유의하게 낮은 평균 이미지 노이즈를 보였고 (모두 p <0.001), 추가 인공물도 보이지 않았다. 결론: LD-DLA 영상은 LD-FBP 및 LD-ADMIRE 영상보다 잡음이 적음을 보여주었지만 공간 분해능은 개선시키지 못하였다. 그리고 더 적은 방사선량으로 촬영한 이미지로 훈련한 DLA일수록 잡음이 적게 나타났다.Introduction: To assess the image quality of low-dose (LD) computed tomography (CT) using a deep learning based denoising algorithm (DLA) compared with filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). Materials and Methods: A total of 100 patients who had undergone routine dose (RD) abdominal CT reconstructed with FBP were included to build the DLA training set. CT images at dose levels corresponding to 13%, 25%, and 50% of RD were simulated from RD CT images and reconstructed using FBP. We trained three DLAs using the simulated LD CT images with different dose levels as input data and the RD CT images as the ground truth (DLA-1, 2, 3 for 13%, 25%, and 50% dose levels, respectively). The American College of Radiology (ACR) CT phantom was used together with 18 patients who underwent abdominal LD CT to build a testing set. LD CT images of phantom and patients were reconstructed using FBP, ADMIRE, and processed using DLAs (LD-FBP, LD-ADMIRE, LD-DLA images). To compare the quality of reconstructed and processed images, we measured noise power spectrum (NPS) and modulation transfer function (MTF) for various contrast objects in phantom images, and mean image noises in patient data. Statistical analysis was performed using paired t-tests and repeated measure analysis of variance with Bonferroni correction for pairwise comparisons. In addition, we evaluated the presence of additional artifacts in LD-DLA images. Results: LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE in both phantom and patient studies (all p < 0.001), and LD-DLAs trained with lower radiation doses showed less image noise. There were no additional image artifacts in LD-DLA images. However, the MTFs of the LD-DLAs were significantly lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose, although the differences between the LD-DLAs and LD-FBP were minimal. Conclusions: DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. Lower radiation doses in training images led to less noise.Introduction. 1 Material and methods. 5 Results 13 Discussion. 17 Acknowledgement 24 Reference 25 Abstract in Korean 41Docto

Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201