Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising

Fan, Fenglei; Shan, Hongming; Kalra, Mannudeep K.; Singh, Ramandeep; Qian, Guhan; Getzin, Matthew; Teng, Yueyang; Hahn, Juergen; Wang, Ge

doi:10.1109/tmi.2019.2963248

Cited by 114 publications

(70 citation statements)

References 34 publications

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“…URL: https://imaging.nci.nih.gov/nbia-search-cover/ [ 3 , 4 , 6 , 13 , 20 , 45 , 50 , 79 , 91 ] D02 AAPM-Mayo Abdomen Mayo clinic AAPM Low- Dose CT Grand Challenge dataset consists of 2378 full and quarter dose CT images from 10 patients of 512 × 512 size. URL: https://www.aapm.org/GrandChallenge/LowDoseCT/ [ 3 , 6 , 12 , 13 , 16 , 23 , 30 , 34 , 36 , 42 , 46 , 58 , 71 , 73 , 75 , 77 , 78 , 81 , 83 , 91 ] D03 Piglet dataset [ 79 ] Whole-body Images were obtained under four dose levels and each dose level consists of 850 images of 512 × 512 size [ 20 , 75 , 79 ] D04 Data Science Bowl 2017 Lung The dataset consists of over a thousand high-resolution LDCT images of high-risk lung cancer patients. https://www.kaggle.com/c/data-science-bowl-2017/data [ 79 ] D05 ...…”

Section: Datasets and Methods To Deal With Data-related Issuesmentioning

confidence: 99%

“…Moreover, the proposed SSDA model did not contain any down-sampling layer and was made up of using a shallow network structure. Different from all the CNN-based DL models published for LDCT restoration, Fan et al [ 16 ] have proposed a stacked autoencoder model based on the quadratic neurons (Q-AE). The replacement of the conventional neurons with quadratic neurons in this Q-AE has motivated to represent complex data, and it has positively influenced to enhance the robustness of LDCT restoration.…”

Section: Architecturesmentioning

confidence: 99%

See 1 more Smart Citation

A review on Deep Learning approaches for low-dose Computed Tomography restoration

Kulathilake

Abdullah

Sabri

et al. 2021

Complex Intell. Syst.

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Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.

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Section: Datasets and Methods To Deal With Data-related Issuesmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

A review on Deep Learning approaches for low-dose Computed Tomography restoration

Kulathilake

Abdullah

Sabri

et al. 2021

Complex Intell. Syst.

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show abstract

“…Image denoising is often the first step in CT image processing and needs to be performed with little signal aberration. Several DL methods for noise reduction are available for diagnostic imaging [98]. Chen et al have described high signal-to-noise ratio (SNR) using DCNN and virtual low dose routine CT images for training (n = 200) and separate testing (n = 100) data [82].…”

Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 99%

“…Although image noise using this method was reduced at even 25% radiation dose for abdomen CT with maintained organ boundaries, radiologists reported qualitative image degradation with denoised image texture. Fan et al proposed a new method by changing the artificial neurons in the inner product with quadratic neurons and showed better efficiency [98]. Fig.…”

Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 99%

Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

Self Cite

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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“…Recently, neural networks gained excessive achievements in the field of medical imaging, due to their self-learning capabilities and high aptitude for automatic feature extraction [16]. Especially, deep neural networks can distinguish infectious and virusrelated pneumonia for chest radiographs [17][18][19][20][21]. Therefore, in this article, we introduce a hybrid deep neural network (HDDNs) for the diagnosis of COVID-19, using CT and X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

Irfan

Iftikhar

Yasin

et al. 2021

IJERPH

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COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.

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Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising

Cited by 114 publications

References 34 publications

A review on Deep Learning approaches for low-dose Computed Tomography restoration

A review on Deep Learning approaches for low-dose Computed Tomography restoration

Artificial intelligence in image reconstruction: The change is here

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

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