Research Status and Prospect for CT Imaging

Computed tomography (CT) is a very valuable imaging method and plays an important role in clinical diagnosis. As people pay more and more attention to radiation doses these years, decreasing CT radiation dose without affecting image quality is a hot direction for research of medical imaging in recent years. This chapter introduces the research status of low-dose technology from following aspects: low-dose scan implementation, reconstruction methods and image processing methods. Furthermore, other technologies related to the development tendency of CT, such as automatic tube current modulation technology, rapid peak kilovoltage (kVp) switching technology, dual-source CT technology and Nano-CT, are also summarized. Finally, the future research prospect are discussed and analyzed

Denoising computed tomography images with 3D-convolution based neural networks

Abstract. Low-dose computed tomography (CT) is an imaging technique used in imaging cross-sectional images of the body that minimizes the radiation dose of the patient. Low-dose CT results in larger amounts of noise in the image and therefore in loss of information. Different denoising methods are used to try to reduce the noise corrupting the images. The aim of this thesis is to research if the temporal correlation of noise between the slices of the computed tomography volumes could be utilized in the denoising of the scans. A convolutional neural network with three-dimensional convolutional layers is trained using publicly available CT images. The images were injected with artificial noise simulating low-dose CT scans. Another network using two-dimensional convolutional layers was also trained for comparison. Different metrics were measured from results of a test dataset to determine the effect of denoising. The results indicate that utilizing the temporal information of the slices by three-dimensional convolutional layers is especially good in denoising of extremely low-dose CT scans. The denoising results between the different methods were closer to each other when the noise level was lower.Tietokonetomografiakuvien kohinanpoisto käyttäen 3D-konvoluutiopohjaisia neuroverkkoja. Tiivistelmä. Matalan säteilyannoksen tietokonetomografia on kuvantamismenetelmä, jolla saadaan kuvattua läpileikkauskuvia kehosta samalla minimoiden potilaan säteilyannosta. Matalan annoksen tietokonetomografia johtaa suurempaan kohinaan kuvassa ja täten informaation katoamiseen. Erilaisia kohinanpoistometodeja käytetään pyrkiessä pienentämään kuvien kohinaa. Tämän työn tarkoituksena oli tutkia, voitaisiinko tietokonetomografiakuvien viipaleiden kohinan välistä temporaalista informaatiota käyttää skannauksien kohinanpoistossa. Kolmiulotteisia konvoluutiotasoja käyttävä konvoluutioneuroverkko koulutettiin julkisesti saatavilla olevilla tietokonetomografiakuvilla. Kuviin oli asetettu keinotekoista kohinaa, simuloidakseen matalan annoksen tietokonetomografiakuvia. Toinen neuroverkko, jossa oli kaksiulotteisia konvoluutiotasoja, koulutettiin vertailua varten. Kohinanpoiston arvioimiseen mitattiin erilaisia metriikoita testidatasetistä saaduista tuloksista. Tulokset osoittavat, että viipaleiden välisen temporaalisen informaation käyttäminen kolmiulotteisten konvoluutiotasojen avulla on erityisen hyvä todella matalan annoksen tietokonetomografiakuvien kohinanpoistossa. Eri metodeilla saatujen kohinanpoiston tulosten väliset erot olivat pienempiä, kun kohinan taso oli matalamp

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging

A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified in low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.Comment: 16 pages, 6 figure

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