Other affiliations: University of Illinois at Chicago, Samsung, Capital Normal University ...read more
Bio: Seungryong Cho is an academic researcher from KAIST. The author has contributed to research in topics: Iterative reconstruction & Image quality. The author has an hindex of 16, co-authored 160 publications receiving 1057 citations. Previous affiliations of Seungryong Cho include University of Illinois at Chicago & Samsung.
Papers published on a yearly basis
••01 Mar 2019
TL;DR: A deep-neural-network-enabled sinogram synthesis method for sparse-view CT is introduced and its outperformance to the existing interpolation methods and also to the iterative image reconstruction approach is shown.
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.
TL;DR: In CS-based image reconstructions both sampling density and data incoherence affect the image quality, and the authors suggest that a sampling scheme should be devised and optimized by use of these indicators.
Abstract: Purpose: Various scanning methods and image reconstruction algorithms are actively investigated for low-dose computed tomography (CT) that can potentially reduce a health-risk related to radiation dose. Particularly, compressive-sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low-dose CT, it has not been studied how sparse sampling schemes affect image quality in CS-based image reconstruction. In this work, the authors present several sparse-sampling schemes for low-dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality.Methods: Data properties of several sampling schemes are analyzed with respect to the CS-based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone-beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data.Results: It is found that both sampling density and data incoherence affect the image quality in the CS-based reconstruction. Among the sampling schemes the authorsmore » investigated, the sparse-view, many-view undersampling (MVUS)-fine, and MVUS-moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction.Conclusions: The authors found that in CS-based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic approach, one can acquire optimally sampled sparse data so that the CS-based algorithms can best perform in terms of image quality.« less
TL;DR: A novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies and can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent is proposed.
Abstract: Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.
TL;DR: A novel method of endodontic treatment of an anomalous maxillary central incisor with the aid of a physical tooth model and a custom-made guide jig via 3D printing technique is described.
Abstract: Endodontic treatment of tooth formation anomalies is a challenge to clinicians and as such requires a complete understanding of the aberrant root canal anatomy followed by careful root canal disinfection and obturation. Here, we report the use of a 3-dimensional (3D) printed physical tooth model including internal root canal structures for the endodontic treatment of a challenging tooth anomaly. A 12-year-old boy was referred for endodontic treatment of tooth #8. The tooth showed class II mobility with swelling and a sinus tract in the buccal mucosa and periapical radiolucency. The tooth presented a very narrow structure between the crown and root by distal concavity and a severely dilacerated root. Moreover, a perforation site with bleeding and another ditching site were identified around the cervical area in the access cavity. A translucent physical tooth model carrying the information on internal root canal structures was built through a 3-step process: data acquisition by cone-beam computed tomographic scanning, virtual modeling by image processing, and manufacturing by 3D printing. A custom-made guide jig was then fabricated to achieve a safe and precise working path to the root canal. Endodontic procedures including access cavity preparation were performed using the physical tooth model and the guide jig. At the 7-month follow-up, the endodontically treated tooth showed complete periapical healing with no clinical signs and symptoms. This case report describes a novel method of endodontic treatment of an anomalous maxillary central incisor with the aid of a physical tooth model and a custom-made guide jig via 3D printing technique.
TL;DR: This paper proposes energy-efficient probabilistic routing (EEPR) algorithm, which controls the transmission of the routing request packets stochastically in order to increase the network lifetime and decrease the packet loss under the flooding algorithm.
Abstract: In the future network with Internet of Things (IoT), each of the things communicates with the others and acquires information by itself. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose energy-efficient probabilistic routing (EEPR) algorithm, which controls the transmission of the routing request packets stochastically in order to increase the network lifetime and decrease the packet loss under the flooding algorithm. The proposed EEPR algorithm adopts energy-efficient probabilistic control by simultaneously using the residual energy of each node and ETX metric in the context of the typical AODV protocol. In the simulations, we verify that the proposed algorithm has longer network lifetime and consumes the residual energy of each node more evenly when compared with the typical AODV protocol.
•26 May 2011
TL;DR: In this paper, a spatial selective excitation pulse is applied to a region including at least a part of an ascending aorta for distinguishably displaying inflowing blood flowing into the imaging region.
Abstract: An MRI apparatus includes an imaging data acquiring unit and a blood flow information generating unit. The imaging data acquiring unit acquires imaging data from an imaging region including myocardium, without using a contrast medium, by applying a spatial selective excitation pulse to a region including at least a part of an ascending aorta for distinguishably displaying inflowing blood flowing into the imaging region. The blood flow information generating unit generates blood flow image data based on the imaging data.
TL;DR: In this article, the authors examine the reasons for the disconnection between theoretical and application-oriented research in computed tomography (CT) and provide recommendations on how it can be resolved.
Abstract: Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years—the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues.
TL;DR: This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
Abstract: Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical–analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.
TL;DR: The extensive research performed during the development of breast tomosynthesis is reviewed, with a focus on the research addressing the medical physics aspects of this imaging modality.
Abstract: Mammography is a very well-established imaging modality for the early detection and diagnosis of breast cancer. However, since the introduction of digital imaging to the realm of radiology, more advanced, and especially tomographic imaging methods have been made possible. One of these methods, breast tomosynthesis, has finally been introduced to the clinic for routine everyday use, with potential to in the future replace mammography for screening for breast cancer. In this two part paper, the extensive research performed during the development of breast tomosynthesis is reviewed, with a focus on the research addressing the medical physics aspects of this imaging modality. This first paper will review the research performed on the issues relevant to the image acquisition process, including system design, optimization of geometry and technique, x-ray scatter, and radiation dose. The companion to this paper will review all other aspects of breast tomosynthesis imaging, including the reconstruction process.
TL;DR: The general technical strategies that are commonly used for radiation dose management in CT are summarized, and dose-management strategies for pediatric CT, cardiac CT, dual-energy CT, CT perfusion and interventional CT are specifically discussed.
Abstract: Despite universal consensus that computed tomography (CT) overwhelmingly benefits patients when used for appropriate indications, concerns have been raised regarding the potential risk of cancer induction from CT due to the exponentially increased use of CT in medicine Keeping radiation dose as low as reasonably achievable, consistent with the diagnostic task, remains the most important strategy for decreasing this potential risk This article summarizes the general technical strategies that are commonly used for radiation dose management in CT Dose-management strategies for pediatric CT, cardiac CT, dual-energy CT, CT perfusion and interventional CT are specifically discussed, and future perspectives on CT dose reduction are presented