Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Li, Ziheng; Cai, Ailong; Wang, Linyuan; Zhang, Wenkun; Tang, Chao; Li, Lei; Liang, Ningning; Yan, Bin

doi:10.3390/s19183941

Cited by 55 publications

(32 citation statements)

References 43 publications

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“…: After spine straightening, the next task in the framework is to replace the fractured vertebra with its healthy equivalent. This is done using inpainting, which has previously been used in medicine for predicting missing information [1], [15], removing lesions [18] or correcting limited-angle acquisitions [7], [16], [19]. None of these works tackles a similar problem to ours.…”

Section: Methodsmentioning

confidence: 99%

Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

Bukas¹,

Jian²,

Rodriguez³

et al. 2021

Preprint

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Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore the vertebral body height and alleviate pain. Leakage is a common complication and may occur due to too much cement being injected. In this work, we propose an automated patient-specific framework that can allow physicians to calculate an upper bound of cement for the injection and estimate the optimal outcome of osteoplasty. The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach. Firstly, the fractured spine is segmented with a three-step Convolution Neural Network (CNN) architecture. Next, a per-vertebra rigid registration to a healthy spine atlas restores its curvature. Finally, a GAN-based inpainting approach replaces the fractured vertebra with an estimation of its original shape. Based on this outcome, we then estimate the maximum amount of bone cement for injection. We evaluate our framework by comparing the virtual vertebrae volumes of ten patients to their healthy equivalent and report an average error of 3.88±7.63%. The presented pipeline offers a first approach to a personalized automatic high-level framework for planning osteoplasty procedures.

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Section: Methodsmentioning

confidence: 99%

Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

Bukas¹,

Jian²,

Rodriguez³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…We can also regard the method in [73] as a projection model in measurement space. Similar to [73,74] used a U-Net to reconstruct under-sampled sinogram. Besides, a discriminator and adversarial training was exploited in this work.…”

Section: Neural Network As Image Projectionsmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

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Compressed sensing (CS) and its medical applications are active areas of research. In this paper, we review recent works using deep learning method to solve CS problem for images or medical imaging reconstruction including computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission tomography (PET). We propose a novel framework to unify traditional iterative algorithms and deep learning approaches. In short, we define two projection operators toward image prior and data consistency, respectively, and any reconstruction algorithm can be decomposed to the two parts. Though deep learning methods can be divided into several categories, they all satisfies the framework. We built the relationship between different reconstruction methods of deep learning, and connect them to traditional methods through the proposed framework. It also indicates that the key to solve CS problem and its medical applications is how to depict the image prior. Based on the framework, we analyze the current deep learning methods and point out some important directions of research in the future.

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“…To verify the performance of the algorithm, PSNR and SSIM were compared when the FDK [20] and GAN [21] algorithms were run in the scanning range of [0 °, 89 °] and [0 °, 119 °]. e results were shown in Figure 2 and Figure 3.…”

Section: Algorithm Performance Analysismentioning

confidence: 99%

Impact Evaluation of Comfort Care on the Prognosis of Giant Thoracic Tumor Patients with Computerized Tomography Imaging under Intelligent Reconstruction Algorithm

Yan

2022

Scientific Programming

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The reconstruction algorithm based on the network of generative adversarial and contextual coding (RANGC) was proposed in this study to analyze the impacts on the prognosis of patients with giant thoracic tumors with CT imaging under artificial intelligence algorithms. The algorithms of Feldkamp–Davis–Kress (FDK) and the generative adversarial network (GAN) were introduced. The patients were divided into the test group with comfort care and the control group with conventional care. Three sets of indicators below were also compared between the patients in two groups, including the pain level and complication incidence, the self-rating anxiety scale (SAS), the self-rating depression scale (SDS), and the patients’ satisfaction and average duration of hospital stay. When the scanning range was [0°, 89°], the peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) under the RANGC algorithm were 45.6 dB and 0.99, respectively. When the scanning range changed to [0°, 119°], the PSNR and SSIM were 39.21 dB and 0.98, respectively. The results were significantly higher than those under the FDK and GAN algorithms, and the difference was obviously of statistical significance P < 0.05 . The average postoperative pain level of the patients in the control group was 3.12 points, and the postoperative complication incidence was 36.13%, while those of the test group patients were 2.27 points and 20.02%, respectively, which was greatly lower than those of the control group patients, and such a difference was of statistical significance P < 0.05 . There was no statistically significant difference in the SDS and SAS scores between the patients in the two groups before surgery. However, the SAS and SDS scores of the test group patients were 41.23 and 43.25, respectively, after surgery, which are obviously lower than those of the control group patients, with a statistically significant difference P < 0.05 . The average duration of hospital stay of the test group patients was 6.31 days, which was lower than that of the control group patients, with a statistically significant difference P < 0.05 . The overall satisfaction of the test group patients was 83.33%, which was remarkably higher than that of the control group patients, and the difference had statistical significance P < 0.05 . All these showed that the performance of the RANGC algorithm was relatively better, and comfort care did good to improve the negative mood, satisfaction, and life quality of patients after surgery.

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Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Cited by 55 publications

References 43 publications

Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Impact Evaluation of Comfort Care on the Prognosis of Giant Thoracic Tumor Patients with Computerized Tomography Imaging under Intelligent Reconstruction Algorithm

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