Low-Dose CT Image Reconstruction With a Deep Learning Prior

Park, Hyoung Suk; Kim, Kyungsang; Jeon, Kiwan

doi:10.1109/access.2020.3020040

Cited by 7 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By competing with each other, the generator enables the generation of samples in the target domain. In medical imaging, different variants of GANs have been applied to LDCT image denoising [14][15][16], in which the generator maps LDCT images to SDCT images. However, paired datasets were used to train these networks.…”

Section: Introductionmentioning

confidence: 99%

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objectives To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images. Materials and methods LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set. Results The noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images. Conclusion The deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.

show abstract

Section: Introductionmentioning

confidence: 99%

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, in the fidelity-embedded GAN, the consideration of high-accuracy data fidelity considering CT physics can improve the network performance in terms of shading correction. For example, using data fidelity that considers photon noise in low-dose CT images can be combined with GAN [25]. Second, manual selection in the second stage of the proposed network can be time-consuming and subjective.…”

Section: Discussionmentioning

confidence: 99%

“…Here, for trained G u and G p , the distances dist(p G u (z) , p x ) and dist(p G p (z) , p x ) are approximately calculated using finite samples S z , S ,G u z and S x [20], [25]:…”

Section: B Stage 2: Paired Modelmentioning

confidence: 99%

See 1 more Smart Citation

Unpaired-Paired Learning for Shading Correction in Cone-Beam Computed Tomography

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging applications. However, CBCT suffers from shading artifacts owing to several factors, including photon scattering and data truncation. This paper presents a deep-learning-based method for eliminating the shading artifacts that interfere with the diagnostic and treatment processes. The proposed method involves a two-stage generative adversarial network (GAN)-based image-to-image translation, where it operates on unpaired CBCT and multidetector computed tomography (MDCT) images. The first stage uses a generic GAN along with the fidelity difference between the original CBCT and MDCT-like images generated by the network. Although this approach is generally effective for denoising, at times, it introduces additional artifacts that appear as bone-like structures in the output images. This is because the weak input fidelity between the two imaging modalities can make it difficult to preserve the morphological structures from complex shadowing artifacts. The second stage of the proposed model addresses this problem. In this stage, paired training data, excluding inappropriate data, were collected from the results obtained in the first stage. Subsequently, the fidelity-embedded GAN was retrained using the selected paired samples. The results obtained in this study reveal that the proposed approach substantially reduces the shadowing and secondary artifacts arising from incorrect data fidelity while preserving the morphological structures of the original CBCT image. In addition, the corrected image obtained using the proposed method facilitates accurate bone segmentation compared to the original and corrected CBCT images obtained using the unpaired method.

show abstract

“…\end{align}In this framework, g ( x , p ) in () can be viewed as the proximal operator of x . Prior works 29–31 have shown that the deep learning regularizer outperforms handcraft priors such as total variation, Markov random field, and nonlocal mean.…”

Section: Methodsmentioning

confidence: 99%

A fidelity‐embedded learning for metal artifact reduction in dental CBCT

et al. 2022

Self Cite

View full text Add to dashboard Cite

Purpose: Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metal artifacts. Methods: The metal artifacts, appearing as streaks and shadows, are nonlocal and highly associated with various factors, including the geometry of metallic inserts, energy-dependent attenuation, and energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures directly. To provide a step-by-step environment in which deep learning can be trained, we propose an iterative learning approach in which the network at each iteration step learns the correction error caused by the previous network, while enforcing the data fidelity in the projection domain. To generate a realistic paired training dataset, metal-free CBCT scans were collected from patients without metallic inserts, and then simulated metal projection data were added to generate the corresponding metal-corrupted projection data. Results: The feasibility of the proposed method was investigated in clinical metal-affected CBCT scans, as well as simulated metal-affected CBCT scans. The results show that the proposed method significantly reduces metal artifacts while preserving the morphological structures near metallic objects and outperforms direct image domain learning. Conclusion:The proposed fidelity-embedded learning can effectively reduce metal artifacts in dental CBCT compared with direct image domain learning.

show abstract

Low-Dose CT Image Reconstruction With a Deep Learning Prior

Cited by 7 publications

References 24 publications

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

Unpaired-Paired Learning for Shading Correction in Cone-Beam Computed Tomography

A fidelity‐embedded learning for metal artifact reduction in dental CBCT

Contact Info

Product

Resources

About