Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference

The photon-counting detector (PCD) based spectral computed tomography attracts much more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy-bin leads to low signal-noise ratio data. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges. In this paper, we propose an iterative deep reconstruction network to synergize model and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data with an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures with iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstruct better image quality than other state-of-the-art methods

A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising

The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latter relaxes this constraint. The large availability of unpaired datasets has raised the interest in deepening unpaired denoising strategies that, in turn, need for robust evaluation techniques going beyond the qualitative evaluation. To this end, we can use quantitative image quality assessment scores that we divided into two categories, i.e., paired and unpaired measures. However, the interpretation of unpaired metrics is not straightforward, also because the consistency with paired metrics has not been fully investigated. To cope with this limitation, in this work we consider 15 paired and unpaired scores, which we applied to assess the performance of low-dose CT denoising. We perform an in-depth statistical analysis that not only studies the correlation between paired and unpaired metrics but also within each category. This brings out useful guidelines that can help researchers and practitioners select the right measure for their applications

Enhancing Image Quality: A Comparative Study of Spatial, Frequency Domain, and Deep Learning Methods

Image restoration and noise reduction methods have been created to restore deteriorated images and improve their quality. These methods have garnered substantial significance in recent times, mainly due to the growing utilization of digital imaging across diverse domains, including but not limited to medical imaging, surveillance, satellite imaging, and numerous others. In this paper, we conduct a comparative analysis of three distinct approaches to image restoration: the spatial method, the frequency domain method, and the deep learning method. The study was conducted on a dataset of 10,000 images, and the performance of each method was evaluated using the accuracy and loss metrics. The results show that the deep learning method outperformed the other two methods, achieving a validation accuracy of 72.68% after 10 epochs. The spatial method had the lowest accuracy of the three, achieving a validation accuracy of 69.98% after 10 epochs. The FFT frequency domain method had a validation accuracy of 52.87% after 10 epochs, significantly lower than the other two methods. The study demonstrates that deep learning is a promising approach for image classification tasks and outperforms traditional methods such as spatial and frequency domain techniques

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

On the impact of incorporating task-information in learning-based image denoising

A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are typically trained by minimizing loss functions that quantify a distance between the denoised image, or a transformed version of it, and the defined target image (e.g., a noise-free or low-noise image). They have demonstrated high performance in terms of traditional image quality metrics such as root mean square error (RMSE), structural similarity index measure (SSIM), or peak signal-to-noise ratio (PSNR). However, it has been reported recently that such denoising methods may not always improve objective measures of image quality. In this work, a task-informed DNN-based image denoising method was established and systematically evaluated. A transfer learning approach was employed, in which the DNN is first pre-trained by use of a conventional (non-task-informed) loss function and subsequently fine-tuned by use of the hybrid loss that includes a task-component. The task-component was designed to measure the performance of a numerical observer (NO) on a signal detection task. The impact of network depth and constraining the fine-tuning to specific layers of the DNN was explored. The task-informed training method was investigated in a stylized low-dose X-ray computed tomography (CT) denoising study for which binary signal detection tasks under signal-known-statistically (SKS) with background-known-statistically (BKS) conditions were considered. The impact of changing the specified task at inference time to be different from that employed for model training, a phenomenon we refer to as "task-shift", was also investigated. The presented results indicate that the task-informed training method can improve observer performance while providing control over the trade off between traditional and task-based measures of image quality

Residual Back Projection With Untrained Neural Networks

Background and Objective: The success of neural networks in a number of image processing tasks has motivated their application in image reconstruction problems in computed tomography (CT). While progress has been made in this area, the lack of stability and theoretical guarantees for accuracy, together with the scarcity of high-quality training data for specific imaging domains pose challenges for many CT applications. In this paper, we present a framework for iterative reconstruction (IR) in CT that leverages the hierarchical structure of neural networks, without the need for training. Our framework incorporates this structural information as a deep image prior (DIP), and uses a novel residual back projection (RBP) connection that forms the basis for our iterations. Methods: We propose using an untrained U-net in conjunction with a novel residual back projection to minimize an objective function and achieve high-accuracy reconstruction. In each iteration, the weights of the untrained U-net are optimized, and the output of the U-net in the current iteration is used to update the input of the U-net in the next iteration through the aforementioned RBP connection. Results: Experimental results demonstrate that the RBP-DIP framework offers improvements over other state-of-the-art conventional IR methods, as well as pre-trained and untrained models with similar network structures under multiple conditions. These improvements are particularly significant in the few-view, limited-angle, and low-dose imaging configurations. Conclusions: Applying to both parallel and fan beam X-ray imaging, our framework shows significant improvement under multiple conditions. Furthermore, the proposed framework requires no training data and can be adjusted on-demand to adapt to different conditions (e.g. noise level, geometry, and imaged object)