Articles

CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

Yiming Lei, Chuang Niu, Junping Zhang, Ge Wang and Hongming Shan
IEEE Transactions on Radiation and Plasma Medical Sciences, vol 8, issue 2, February 2024
Deep Learning for PET Image Reconstruction
Andrew J. Reader, Guillaume Corda, Abolfazl Mehranian, Casper da Costa-Luis, Sam Ellis and Julia A. Schnabel
IEEE Transactions on Radiation and Plasma Medical Sciences, vol 5, issue 1, January 2021

Current and Emerging Trends in Medical Image Segmentation With Deep Learning

Pierre-Henri Conze, Gustavo Andrade-Miranda, Vivek Kumar Singh, Vincent Jaouen and Dimitris Visvikis
IEEE Transactions on Radiation and Plasma Medical Sciences, vol 6, issue 3, July 2023
Generative Modeling in Sinogram Domain for Sparse-View CT Reconstruction
Bing Guan, Cailian Yang, Liu Zhang, Shanzhou Niu, Minghui Zhang, Yuhao Wang, Weiwen Wu and Qiegen Liu
IEEE Transactions on Radiation and Plasma Medical Sciences, vol 8, issue 2, February 2024
Low-Temperature Plasma for Biology, Hygiene, and Medicine: Perspective and Roadmap
Mounir Laroussi, Sander Bekeschus, Michael Keidar, Annemie Bogaerts, Alexander Fridman, Xinpei Lu, Kostya Ostrikov, Masaru Hori, Katharina Stapelmann , Vandana Miller, Stephan Reuter, Christophe Laux, Ali Mesbah, James Walsh, Chunqi Jiang, Selma M. Thagard, Hiromasa Tanaka, Dawei Liu, Dayun Yan, and Maksudbek Yusupov
IEEE Transactions on Radiation and Plasma Medical Sciences, vol 6, issue 2, February 2022
IEEE Transactions on Radiation and Plasma Medical Sciences, vol 6, issue 2, February 2022
Artificial Intelligence in Radiation Therapy
Yabo Fu, Hao Zhang, Eric D. Morris, Carri K. Glide-Hurst, Suraj Pai, Alberto Traverso, Leonard Wee, Ibrahim Hadzic, Per-Ivar Lønne, Chenyang Shen, Tian Liu, and Xiaofeng Yang

Review Articles

Photon Counting Detectors for X-Ray Imaging With Emphasis on CT

R. Ballabriga, J. Alozy, F. N. Bandi, M. Campbell, N. Egidos, J. M. Fernandez-Tenllado, E. H. M. Heijne, I. Kremastiotis, X. Llopart, B. J. Madsen, D. Pennicard, V. Sriskaran, and L. Tlustos

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A Review of Dielectric Barrier Discharge Cold Atmospheric Plasma for Surface Sterilization and Decontamination

Kolawole Adesina, Ta-Chun Lin, Yue-Wern Huang, Marek Locmelis and Daoru Han

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Systematic Review on Learning-Based Spectral CT

Alexandre Bousse, Venkata Sai Sundar Kandarpa , Simon Rit, Alessandro Perelli, Mengzhou Li, Guobao Wang, Jian Zhou and Ge Wang

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Technological Developments and Future Perspectives in Particle Therapy: A Topical Review

Aafke Christine Kraan, Alberto Del Guerra

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Featured Articles

Total Body PET: Why, How, What for?

Suleman Surti, Austin R. Pantel and Joel S. Karp

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Current and Emerging Trends in Medical Image Segmentation With Deep Learning

Pierre-Henri Conze, Gustavo Andrade-Miranda, Vivek Kumar Singh, Vincent Jaouen, Dimitris Visvikis

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Photon Counting CT: Clinical Applications and Future Developments

Scott S. Hsie, Shuai Leng, Kishore Rajendran, Shengzhen Tao, Cynthia H. McCollough

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DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation

Tao Lei, Risheng Wang, Yuxiao Zhang, Yong Wan, Chang Liu, Asoke K. Nandi

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Most Read Articles

Deep Learning-Based Image Segmentation on Multimodal Medical Imaging

Zhe Guo, Xiang Li, Heng Huang, Ning Guo and Quanzheng Li

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On Interpretability of Artificial Neural Networks: A Survey

Feng-Lei Fan, Jinjun Xiong, Mengzhou Li, Ge Wang

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Deep Learning for PET Image Reconstruction

Andrew J. Reader, Guillaume Corda, Abolfazl Mehranian, Casper da Costa-Luis, Sam Ellis, Julia A. Schnabel

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Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction

Hoyeon Lee, Jongha Lee, Hyeongseok Kim, Byungchul Cho, Seungryong Cho

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Abstract:
X-ray imaging is a widely used imaging modality in the medical diagnostic field due to its availability, low cost, high spatial resolution, and fast image acquisition. X-ray photons in standard X-ray sources are polychromatic. Detectors that allow to extract the “color” information of the individual X-rays can lead to contrast enhancement, improved material identification or reduction of beam hardening artifacts at the system level, if we compare them with the widely spread energy integrating detectors. Today, in the field of computed tomography (CT), prototypes of clinical grade systems based on spectral photon counting detectors are currently available for clinical research from different companies. One of the key system components in that development is the X-ray photon detector. This article reviews the photon detection hardware, from the conversion of X-rays into electrical signals to the pulse processing electronics. A review of available photon counting application specific integrated circuits (ASICs) for spectroscopic X-ray imaging is presented with emphasis on the CT medical imaging application.

Abstract:
Numerous investigations have shown that nonequilibrium discharges at atmospheric pressure, also known as “cold atmospheric plasma” (CAP), are efficient to remove biological contaminants from surfaces of a variety of materials. Recently, CAP has quickly advanced as a technique for microbial cleaning, wound healing, and cancer therapy due to the chemical and biologically active radicals it produces, known collectively as reactive oxygen and nitrogen species (RONS). This article reviews studies pertaining to one of the atmospheric plasma sources known as dielectric barrier discharge (DBD) which has been widely used to treat materials with microbes for sterilization, disinfection, and decontamination purposes. To advance research in CAP applications, this review discusses various types and configurations of barrier discharge, the role played by reactive species and other DBD-CAP agents leading to its antimicrobial efficacy, a few collection of DBD-CAP past studies specifically on surface, and emerging applications of DBD-CAP technology. Our review showed that nonthermal/equilibrium plasma generated from DBD could sterilize or disinfect surface of materials without causing any thermal damage or environmental contamination.

Abstract:
Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: 1) dual-energy CT (DECT) and 2) photon-counting CT (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Abstract:
In the last decades, important technological progress has been made to enhance the quality and efficiency of particle therapy treatments. Continuous improvements in dose delivery, treatment planning and verification techniques have lead to higher dose conformity and better sparing of healthy tissue. At the same time, particle therapy treatments are complex and much more expensive than conventional radiotherapy, and only highly specialized facilities can offer these treatments. Cost reduction is thus a strong drive behind technological developments in the field. The number of treatment facilities offering proton and carbon therapy has strongly grown in the last decades, and the amount of research efforts and innovations have increased continuously. From a technological perspective, advances in hardware are often accompanied by innovations in software and computation, and vice versa. In this review we will present a basic overview of technological advances in particle therapy hardware (accelerators, gantries, applications of superconductivity, treatment verification techniques), software (Monte Carlo simulations, treatment planning calculations), and studies towards clinical applications. By combining a broad selection of topics into a single review and by covering both proton and carbon therapy, we aim at providing the reader a unique overview of the evolution of various technologies developed for particle therapy.

Abstract:
PET instruments are now available with a long axialfield-of-view (LAFOV) to enable imaging the total-body, or atleast head and torso, simultaneously and without bed translation. This has two major benefits, a dramatic increase in systemsensitivity and the ability to measure kinetics with wider axialcoverage so as to include multiple organs. This article presents a review of the technology leading up to the introduction ofthese new instruments, and explains the benefits of an LAFOVPET-CT instrument. To date there are two platforms developed for total-body PET (TB-PET), an outcome of the EXPLORER Consortium of the University of California at Davis (UC Davis) and the University of Pennsylvania (Penn). The uEXPLORERat UC Davis has an AFOV of 194 cm and was developed byUnited Imaging Healthcare. The PennPET EXPLORER was developed at Penn and is based on the digital detector fromPhilips Healthcare. This multiring system is scalable and hasbeen tested with 3 rings but is now being expanded to 6 ringsfor 140 cm. Initial human studies with both EXPLORER systems have demonstrated the successful implementation and benefitsof LAFOV scanners for both clinical and research applications.Examples of such studies are described in this article.

Abstract:
In recent years, the segmentation of anatomical or pathological structures using deep learning has experienced a widespread interest in medical image analysis. Remarkably successful performance has been reported in many imaging modalities and for a variety of clinical contexts to support clinicians in computer-assisted diagnosis, therapy, or surgical planning purposes. However, despite the increasing amount of medical image segmentation challenges, there remains little consensus on which methodology performs best. Therefore, we examine in this article the numerous developments and breakthroughs brought since the rise of U-Net-inspired architectures. Especially, we focus on the technical challenges and emerging trends that the community is now focusing on, including conditional generative adversarial and cascaded networks, medical Transformers, contrastive learning, knowledge distillation, active learning, prior knowledge embedding, cross-modality learning, multistructure analysis, federated learning, or semi-supervised and self-supervised paradigms. We also suggest possible avenues to be further investigated in future research efforts.

Abstract:
The use of a photon counting detector in CT (PCD CT) is currently the subject of intense investigation and development. In this review article, we will describe potential clinical applications of this technology with a particular focus on the experience of our own institution with a prototype PCD CT scanner. Photon counting detectors (PCDs) have three primary advantages over conventional, energy integrating detectors (EIDs): 1) they provide spectral information without need for a dedicated dual-energy protocol; 2) they are immune to electronic noise; and 3) they can be made very high resolution without significant compromises to quantum efficiency. These advantages translate into several clinical applications. Metal artifacts, beam hardening artifacts, and noise streaks from photon starvation can be better mitigated using PCD CT. Certain incidental findings can be better characterized using the spectral information from PCD CT. High-contrast, high-resolution structures, such as the temporal bone can be better visualized using PCD CT and at greatly reduced dose. We also discuss new possibilities on the horizon, including new contrast agents, and how anticipated improvements in PCD CT will translate to performance in these applications.

Abstract:
Deep convolutional neural networks have been widely used for medical image segmentation due to their superiority in feature learning. Although these networks are successful for simple object segmentation tasks, they suffer from two problems for liver and liver tumor segmentation in CT images. One is that convolutional kernels of fixed geometrical structure are unmatched with livers and liver tumors of irregular shapes. The other is that pooling and strided convolutional operations easily lead to the loss of spatial contextual information of images. To address these issues, we propose a deformable encoder-decoder network (DefED-Net) for liver and liver tumor segmentation. The proposed network makes two contributions: 1) the deformable convolution is used to enhance the feature representation capability of DefED-Net, which can help the network to learn convolution kernels with adaptive spatial structuring information and 2) we design a ladder-atrous-spatial-pyramid-pooling (Ladder-ASPP) module using multiscale dilation rate (Ladder-ASPP) and apply the module to learn better context information than the atrous spatial pyramid pooling for CT image segmentation. The proposed DefED-Net is evaluated on two public benchmark datasets, the LiTS, and the 3DIRCADb. Experiments demonstrate that the DefED-Net has better capability of feature representation as well as provides higher accuracy on liver and liver tumor segmentation than state-of-the-art networks. The available code of DefED-Net we propose can be found from https://github.com/SUST-reynole/DefED-Net .

Abstract:
Multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Corresponding multimodal image analysis and ensemble learning schemes have seen rapid growth and bring unique value to medical applications. Motivated by the recent success of applying deep learning methods to medical image processing, we first propose an algorithmic architecture for supervised multimodal image analysis with cross-modality fusion at the feature learning level, classifier level, and decision-making level. We then design and implement an image segmentation system based on deep convolutional neural networks to contour the lesions of soft tissue sarcomas using multimodal images, including those from magnetic resonance imaging, computed tomography, and positron emission tomography. The network trained with multimodal images shows superior performance compared to networks trained with single-modal images. For the task of tumor segmentation, performing image fusion within the network (i.e., fusing at convolutional or fully connected layers) is generally better than fusing images at the network output (i.e., voting). This paper provides empirical guidance for the design and application of multimodal image analysis.

Abstract:
Deep learning as performed by artificial deep neural networks (DNNs) has achieved great successes recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, the interpretability of DNNs has recently attracted much research attention. In this article, we propose a simple but comprehensive taxonomy for interpretability, systematically review recent studies on interpretability of neural networks, describe applications of interpretability in medicine, and discuss future research directions, such as in relation to fuzzy logic and brain science.

Abstract:
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventional reconstruction, in order to reconstruct images from noisy PET data. The review starts with an overview of conventional PET image reconstruction and then covers the principles of general linear and convolution-based mappings from data to images, and proceeds to consider nonlinearities, as used in convolutional neural networks (CNNs). The direct deep-learning methodology is then reviewed in the context of PET reconstruction. Direct methods learn the imaging physics and statistics from scratch, not relying on a priori knowledge of these models of the data. In contrast, model-based or physics-informed deep-learning uses existing advances in PET image reconstruction, replacing conventional components with deep-learning data-driven alternatives, such as for the regularization. These methods use trusted models of the imaging physics and noise distribution, while relying on training data examples to learn deep mappings for regularization and resolution recovery. After reviewing the main examples of these approaches in the literature, the review finishes with a brief look ahead to future directions.

Abstract:
Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in CT community. Since analytic image reconstruction algorithms would lead to severe image artifacts, various iterative algorithms have been developed for reconstructing images from sparsely view-sampled projection data. However, iterative algorithms take much longer computation time than the analytic algorithms, and images are usually prone to different types of image artifacts that heavily depend on the reconstruction parameters. Interpolation methods have also been utilized to fill the missing data in the sinogram of sparse-view CT thus providing synthetically full data for analytic image reconstruction. In this paper, we introduce a deep-neural-network-enabled sinogram synthesis method for sparse-view CT, and show its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach.