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  1. Article ; Online: Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

    Yun, Sungho / Jeong, Uijin / Lee, Donghyeon / Kim, Hyeongseok / Cho, Seungryong

    Medical physics

    2023  Volume 50, Issue 12, Page(s) 7498–7512

    Abstract: Background: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-dependent ... ...

    Abstract Background: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-dependent as well as system-specific.
    Purpose: In this study, we propose a dual-domain network for reducing the bowtie-filter-induced artifacts in CBCT images.
    Methods: In the projection domain, the network compensates for the filter-induced beam-hardening that are highly related to the eclipse artifacts. The output of the projection-domain network was used for image reconstruction and the reconstructed images were fed into the image-domain network. In the image domain, the network further reduces the remaining cupping artifacts that are associated with the scatter. A single image-domain-only network was also implemented for comparison.
    Results: The proposed approach successfully enhanced soft-tissue contrast with much-reduced image artifacts. In the numerical study, the proposed method decreased perceptual loss and root-mean-square-error (RMSE) of the images by 84.5% and 84.9%, respectively, and increased the structure similarity index measure (SSIM) by 0.26 compared to the original input images on average. In the experimental study, the proposed method decreased perceptual loss and RMSE of the images by 87.2% and 92.1%, respectively, and increased SSIM by 0.58 compared to the original input images on average.
    Conclusions: We have proposed a deep-learning-based dual-domain framework to reduce the bowtie-filter artifacts and to increase the soft-tissue contrast in CBCT images. The performance of the proposed method has been successfully demonstrated in both numerical and experimental studies.
    MeSH term(s) Humans ; Quality Improvement ; Neural Networks, Computer ; Cone-Beam Computed Tomography/methods ; Image Processing, Computer-Assisted/methods ; X-Rays ; Algorithms ; Phantoms, Imaging ; Artifacts
    Language English
    Publishing date 2023-09-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 188780-4
    ISSN 2473-4209 ; 0094-2405
    ISSN (online) 2473-4209
    ISSN 0094-2405
    DOI 10.1002/mp.16693
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Deep-learning-based projection-domain breast thickness estimation for shape-prior iterative image reconstruction in digital breast tomosynthesis.

    Lee, Seoyoung / Kim, Hyeongseok / Lee, Hoyeon / Cho, Seungryong

    Medical physics

    2022  Volume 49, Issue 6, Page(s) 3670–3682

    Abstract: Background: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT ... ...

    Abstract Background: Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and in-plane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation.
    Purpose: In this study, we propose a deep-learning-based methodology for projection-domain breast thickness estimation and demonstrate a shape-prior iterative DBT image reconstruction.
    Methods: We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin-line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the X-ray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel-wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique.
    Results: The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root-mean-squared error (1.976%) and high structural similarity (99.997%) indicated a good agreement between the network-generated thickness corrected image and the ground truth image. Compared to the existing methods and simple single-input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape prior for the iterative DBT reconstruction.
    Conclusions: We presented a novel deep-learning-based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation.
    MeSH term(s) Algorithms ; Breast/diagnostic imaging ; Breast Neoplasms/diagnostic imaging ; Data Compression ; Deep Learning ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Mammography/methods ; Phantoms, Imaging
    Language English
    Publishing date 2022-03-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 188780-4
    ISSN 2473-4209 ; 0094-2405
    ISSN (online) 2473-4209
    ISSN 0094-2405
    DOI 10.1002/mp.15612
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  3. Article ; Online: Panoramic dental tomosynthesis imaging by use of CBCT projection data.

    Kwon, Taejin / Choi, Da-In / Hwang, Jaehong / Lee, Taewon / Lee, Inje / Cho, Seungryong

    Scientific reports

    2023  Volume 13, Issue 1, Page(s) 8817

    Abstract: Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a ... ...

    Abstract Dental CBCT and panoramic images are important imaging modalities used in dental diagnosis and treatment planning. In order to acquire a panoramic image without an additional panoramic scan, in this study, we proposed a method of reconstructing a panoramic image by extracting panoramic projection data from dental CBCT projection data. After specifying the patient's dental arch from the patient's CBCT image, panoramic projection data are extracted from the CBCT projection data along the appropriate panoramic scan trajectory that fits the dental arch. A total of 40 clinical human datasets and one head phantom dataset were used to test the proposed method. The clinical human dataset used in this study includes cases in which it is difficult to reconstruct panoramic images from CBCT images, such as data with severe metal artifacts or data without teeth. As a result of applying the panoramic image reconstruction method proposed in this study, we were able to successfully acquire panoramic images from the CBCT projection data of various patients. The proposed method acquires a universally applicable panoramic image that is less affected by CBCT image quality and metal artifacts by extracting panoramic projection data from dental CBCT data and reconstructing a panoramic image.
    MeSH term(s) Humans ; Spiral Cone-Beam Computed Tomography ; Image Processing, Computer-Assisted/methods ; Radiography, Panoramic/methods ; Tooth ; Phantoms, Imaging ; Cone-Beam Computed Tomography/methods ; Algorithms ; Artifacts
    Language English
    Publishing date 2023-05-31
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-35805-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: A novel analysis of the formation and resorption changes in dental hard tissue using longitudinal in vivo micro computed tomography.

    Yoo, Yeon-Jee / Hwang, Joonil / Park, So-Hyun / Hwang, Jaehong / Cho, Seungryong / Kim, Sun-Young

    Dental materials journal

    2023  Volume 42, Issue 5, Page(s) 708–716

    Abstract: This study was to investigate the new analysis manner of dental hard tissue change using in vivo micro-computed tomography (CT) in rat. Scanning, registration, analyzing, and presenting method to track longitudinal in vivo micro-CT data on dental hard ... ...

    Abstract This study was to investigate the new analysis manner of dental hard tissue change using in vivo micro-computed tomography (CT) in rat. Scanning, registration, analyzing, and presenting method to track longitudinal in vivo micro-CT data on dental hard tissues were validated in murine models: formative, dentin thickness after direct pulp capping with mineral trioxide aggregate; resorptive, development of apical bone rarefaction in apical periodontitis model. Serial in vivo micro-CT scans were analyzed through rigid-registration, active-contouring, deformable-registration, and motion vector-based quantitative analyses. The rate and direction of hard tissue formation after direct pulp capping was datafied by tracing coordinate shift of fiducial points on pulp chamber outline in formative model. The development of apical periodontitis could be monitored with voxel counts, and quantitatively analyzed in terms of lesion size, bone loss, and mineral density in resorptive model. This study supports the application of longitudinal in vivo micro-CT for resorptive- and formative-phase specific monitoring of dental hard tissues.
    MeSH term(s) Rats ; Mice ; Animals ; X-Ray Microtomography/methods ; Dental Pulp Capping/methods ; Calcium Compounds ; Silicates/pharmacology ; Minerals ; Periapical Periodontitis/pathology ; Drug Combinations ; Oxides ; Dental Pulp
    Chemical Substances Calcium Compounds ; Silicates ; Minerals ; Drug Combinations ; Oxides
    Language English
    Publishing date 2023-08-23
    Publishing country Japan
    Document type Journal Article
    ZDB-ID 605650-7
    ISSN 1881-1361 ; 0287-4547
    ISSN (online) 1881-1361
    ISSN 0287-4547
    DOI 10.4012/dmj.2023-008
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.B 2687: Show issues Location:
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  5. Article: Homogenization of multi-institutional chest x-ray images in various data transformation schemes.

    Kim, Hyeongseok / Lee, Seoyoung / Shim, Woo Jung / Choi, Min-Seong / Cho, Seungryong

    Journal of medical imaging (Bellingham, Wash.)

    2023  Volume 10, Issue 6, Page(s) 61103

    Abstract: Purpose: Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation ... ...

    Abstract Purpose: Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation methods to reduce data discrepancies between different data domains, detailed analysis for explaining the performance of data transformation methods is lacking.
    Approach: This study compares several data transformation methods in the tuberculosis detection task with multi-institutional chest x-ray (CXR) data. Five different data transformations, including normalization, standardization with and without lung masking, and multi-frequency-based (MFB) standardization with and without lung masking were implemented. A tuberculosis detection network was trained using a reference dataset, and the data from six other sites were used for the network performance comparison. To analyze data harmonization performance, we extracted radiomic features and calculated the Mahalanobis distance. We visualized the features with a dimensionality reduction technique. Through similar methods, deep features of the trained networks were also analyzed to examine the models' responses to the data from various sites.
    Results: From various numerical assessments, the MFB standardization with lung masking provided the highest network performance for the non-reference datasets. From the radiomic and deep feature analyses, the features of the multi-site CXRs after MFB with lung masking were found to be well homogenized to the reference data, whereas the others showed limited performance.
    Conclusions: Conventional normalization and standardization showed suboptimal performance in minimizing feature differences among various sites. Our study emphasizes the strengths of MFB standardization with lung masking in terms of network performance and feature homogenization.
    Language English
    Publishing date 2023-04-26
    Publishing country United States
    Document type Journal Article
    ISSN 2329-4302
    ISSN 2329-4302
    DOI 10.1117/1.JMI.10.6.061103
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  6. Article ; Online: A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography.

    Lee, Donghyeon / Yun, Sungho / Soh, Jeongtae / Lim, Sunho / Kim, Hyoyi / Cho, Seungryong

    Journal of X-ray science and technology

    2022  Volume 30, Issue 3, Page(s) 549–566

    Abstract: Background: Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods ... ...

    Abstract Background: Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation.
    Objective: In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients.
    Methods: By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm.
    Results: In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition.
    Conclusions: The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.
    Language English
    Publishing date 2022-03-07
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2012019-9
    ISSN 1095-9114 ; 0895-3996
    ISSN (online) 1095-9114
    ISSN 0895-3996
    DOI 10.3233/XST-211054
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  7. Article ; Online: A feasibility study on deep-neural-network-based dose-neutral dual-energy digital breast tomosynthesis.

    Kim, Hyeongseok / Lee, Hoyeon / Lee, Seoyoung / Choi, Young-Wook / Choi, Young Jin / Kim, Kee Hyun / Seo, Wontaek / Shin, Choul Woo / Cho, Seungryong

    Medical physics

    2022  Volume 50, Issue 2, Page(s) 791–807

    Abstract: Background: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy ... ...

    Abstract Background: Diagnostic performance based on x-ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser breasts, mass detection and malignancy characterization are often considered challenging yet.
    Purpose: As an improved diagnostic solution to the dense breast cases, we propose a dual-energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single-energy DBT.
    Methods: The proposed dual-energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x-ray energy spectrum. For material decomposition, we estimate partial path lengths of an x-ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material-selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual-energy DBT system to demonstrate the feasibility of the proposed imaging method.
    Results: The developed deep neural network successfully synthesized missing projections. Material-selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data.
    Conclusions: The proposed dual-energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts.
    MeSH term(s) Humans ; Female ; Mammography/methods ; Feasibility Studies ; Breast Density ; Breast Neoplasms/diagnostic imaging ; Neural Networks, Computer ; Phantoms, Imaging ; Radiographic Image Enhancement
    Language English
    Publishing date 2022-11-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 188780-4
    ISSN 2473-4209 ; 0094-2405
    ISSN (online) 2473-4209
    ISSN 0094-2405
    DOI 10.1002/mp.16071
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  8. Article ; Online: Selective morphological analysis of cerium metal in electrodeposit recovered from molten LiCl-KCl eutectic by radiography and computed tomography.

    Jee, Young Taek / Park, Miran / Cho, Seungryong / Yun, Jong-Il

    Scientific reports

    2019  Volume 9, Issue 1, Page(s) 1346

    Abstract: This paper presents, for the first time, a study to analyze the surface morphology of metal extracted from a high temperature molten salt medium in the electrodeposit using x-ray radiography and computed tomography. Widely used methods such as scanning ... ...

    Abstract This paper presents, for the first time, a study to analyze the surface morphology of metal extracted from a high temperature molten salt medium in the electrodeposit using x-ray radiography and computed tomography. Widely used methods such as scanning electron microscopy and inductively coupled plasma-optical emission spectrometry/mass spectrometry are destructive and the related processes are often subject to the air condition. The x-ray imaging can provide rich information of the target sample in a non-destructive way without invoking hydrolysis or oxidation of a hygroscopic sample. In this study, the x-ray imaging conditions were optimized as following: tube voltage at 100 kVp and the current exposure time product at 8.8 mAs in our in-house x-ray imaging system. LiCl-KCl and cerium metals used in this work produced substantially distinguishable contrasts in the radiography due to their distinctive attenuation characteristics, and this difference was well quantified in the histograms of brightness. Electrodeposits obtained by chronoamperometry and chronopotentiometry demonstrated a completely different behavior of electrodeposition even at the same applied charge. In particular, computed tomography and volumetric analysis clearly showed the structural and morphological dissimilarity. The structure of cerium metal in the electrodeposit was successfully separated from the chloride salt structure in the CT image by an image segmentation process.
    Language English
    Publishing date 2019-02-04
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-018-38022-3
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  9. Article ; Online: Data-specific mask-guided image reconstruction for diffuse optical tomography.

    Sabir, Sohail / Cho, Sanghoon / Heo, Duchang / Hyun Kim, Kee / Cho, Seungryong / Pua, Rizza

    Applied optics

    2020  Volume 59, Issue 30, Page(s) 9328–9339

    Abstract: Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data- ... ...

    Abstract Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the
    Language English
    Publishing date 2020-10-26
    Publishing country United States
    Document type Journal Article
    ISSN 1539-4522
    ISSN (online) 1539-4522
    DOI 10.1364/AO.401132
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  10. Article ; Online: On-beam computed tomography reconstruction for radiotherapy verification from projection image differences caused by motion during treatment.

    Lee, Hoyeon / Cheong, Kwang-Ho / Jung, Jae Won / Cho, Byungchul / Cho, Seungryong / Yeo, Inhwan

    Physics in medicine and biology

    2020  Volume 65, Issue 5, Page(s) 55001

    Abstract: The purpose of this study is to propose a reconstruction method of a target and its neighborhood, representative of the moment of radiotherapy delivery, based on differences in its transit images between the time of planning computed tomography (pCT) and ...

    Abstract The purpose of this study is to propose a reconstruction method of a target and its neighborhood, representative of the moment of radiotherapy delivery, based on differences in its transit images between the time of planning computed tomography (pCT) and the time of treatment beam delivery. To validate the method, a lung phantom with a target object was constructed, and CT-scanned before and after making a shift of the target. The latter scan was intended to simulate a potential organ movement at the time of treatment, and to serve as ground-truth images. Treatment planning using arc-beam delivery was done on the first pCT images. The planned beams were irradiated to the phantom after the shift, while cine transit images were acquired. Cine transit images were also calculated through the pCT images before the shift. From the ratio of the measured and calculated transit images, the amount of image changes due to the organ movement between the time of pCT and that of treatment was three-dimensionally reconstructed. By adding the reconstructed images to the pCT images before the shift, the CT images of the phantom at the time of the beam delivery were generated and compared with the ground truth images. The phantom after the shift was also scanned by on-board cone-beam computer tomography (CBCT) and reconstructed from the measured transit images (MVCT) for comparison. The proposed method reconstructed images that are very close to the ground-truth images in the volume and HU values of the target and the dose-volume coverage of the target and lung. Similar agreement was not found in the CBCT and MVCT images. The method may be used for 4D target image reconstruction, and, combined with the reconstructed image of un-irradiated areas, may offer clinically useful images of the entire region of interest.
    MeSH term(s) Algorithms ; Cone-Beam Computed Tomography/methods ; Humans ; Lung Neoplasms/radiotherapy ; Motion ; Movement ; Phantoms, Imaging ; Radiotherapy Planning, Computer-Assisted/methods
    Language English
    Publishing date 2020-02-28
    Publishing country England
    Document type Journal Article
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ab6eb9
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