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Pan J, Chang D, Wu W, Chen Y, Wang S. Self-supervised tomographic image noise suppression via residual image prior network. Comput Biol Med 2024; 179:108837. [PMID: 38991317 DOI: 10.1016/j.compbiomed.2024.108837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/29/2024] [Accepted: 06/29/2024] [Indexed: 07/13/2024]
Abstract
Computed tomography (CT) denoising is a challenging task in medical imaging that has garnered considerable attention. Supervised networks require a lot of noisy-clean image pairs, which are always unavailable in clinical settings. Existing self-supervised algorithms for suppressing noise with paired noisy images have limitations, such as ignoring the residual between similar image pairs during training and insufficiently learning the spectrum information of images. In this study, we propose a Residual Image Prior Network (RIP-Net) to sufficiently model the residual between the paired similar noisy images. Our approach offers new insights into the field by addressing the limitations of existing methods. We first establish a mathematical theorem clarifying the non-equivalence between similar-image-based self-supervised learning and supervised learning. It helps us better understand the strengths and limitations of self-supervised learning. Secondly, we introduce a novel regularization term to model a low-frequency residual image prior. This can improve the accuracy and robustness of our model. Finally, we design a well-structured denoising network capable of exploring spectrum information while simultaneously sensing context messages. The network has dual paths for modeling high and low-frequency compositions in the raw noisy image. Additionally, context perception modules capture local and global interactions to produce high-quality images. The comprehensive experiments on preclinical photon-counting CT, clinical brain CT, and low-dose CT datasets, demonstrate that our RIP-Net is superior to other unsupervised denoising methods.
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Affiliation(s)
- Jiayi Pan
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong, China.
| | - Dingyue Chang
- Institute of Materials, China Academy of Engineering Physics, Mianyang, Sichuan, China
| | - Weiwen Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong, China.
| | - Yang Chen
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
| | - Shaoyu Wang
- School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China.
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Ren J, Zheng Z, Wang Y, Liang N, Wang S, Cai A, Li L, Yan B. Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography. Comput Biol Med 2024; 180:108854. [PMID: 39068902 DOI: 10.1016/j.compbiomed.2024.108854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 07/04/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. METHODS To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. RESULTS To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. CONCLUSION Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.
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Affiliation(s)
- Junru Ren
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Zhizhong Zheng
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Yizhong Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Ningning Liang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Shaoyu Wang
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
| | - Ailong Cai
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.
| | - Lei Li
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China.
| | - Bin Yan
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, China
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Chen B, Zhang Z, Xia D, Sidky EY, Pan X. Accurate Reconstruction of Multiple Basis Images Directly From Dual Energy CT Data. IEEE Trans Biomed Eng 2024; 71:2058-2069. [PMID: 38300771 PMCID: PMC11264342 DOI: 10.1109/tbme.2024.3361382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
OBJECTIVE We develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT. METHODS In medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array. RESULTS We conduct experimental studies using simulated and real DE data in CT, and evaluate basis images and VMIs obtained in terms of visual inspection and quantitative metrics. The study results reveal that the algorithm developed can accurately and robustly reconstruct multiple ( 2) basis images directly from DE data. CONCLUSIONS The developed algorithm can yield accurate multiple ( 2) basis images, VMIs, and physical quantities of interest from DE data in CT. SIGNIFICANCE The work may provide insights into the development of practical procedures for reconstructing multiple basis images, VMIs, and physical quantities from DE data in applications. The work can be extended to reconstruct multiple basis images in multi-spectral or photon-counting CT.
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Kronfeld A, Rose P, Baumgart J, Brockmann C, Othman AE, Schweizer B, Brockmann MA. Quantitative multi-energy micro-CT: A simulation and phantom study for simultaneous imaging of four different contrast materials using an energy integrating detector. Heliyon 2024; 10:e23013. [PMID: 38148814 PMCID: PMC10750148 DOI: 10.1016/j.heliyon.2023.e23013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 11/23/2023] [Accepted: 11/23/2023] [Indexed: 12/28/2023] Open
Abstract
Emerging from the development of single-energy Computed Tomography (CT) and Dual-Energy Computed Tomography, Multi-Energy Computed Tomography (MECT) is a promising tool allowing advanced material and tissue decomposition and thereby enabling the use of multiple contrast materials in preclinical research. The scope of this work was to evaluate whether a usual preclinical micro-CT system is applicable for the decomposition of different materials using MECT together with a matrix-inversion method and how different changes of the measurement-environment affect the results. A matrix-inversion based algorithm to differentiate up to five materials (iodine, iron, barium, gadolinium, residual material) by applying four different acceleration voltages/energy levels was established. We carried out simulations using different ratios and concentrations (given in fractions of volume units, VU) of the four different materials (plus residual material) at different noise-levels for 30 keV, 40 keV, 50 keV, 60 keV, 80 keV and 100 keV (monochromatic). Our simulation results were then confirmed by using region of interest-based measurements in a phantom-study at corresponding acceleration voltages. Therefore, different mixtures of contrast materials were scanned using a micro-CT. Voxel wise evaluation of the phantom imaging data was conducted to confirm its usability for future imaging applications and to estimate the influence of varying noise-levels, scattering, artifacts and concentrations. The analysis of our simulations showed the smallest deviation of 0.01 (0.003-0.15) VU between given and calculated concentrations of the different contrast materials when using an energy-combination of 30 keV, 40 keV, 50 keV and 100 keV for MECT. Subsequent MECT phantom measurements, however, revealed a combination of acceleration voltages of 30 kV, 40 kV, 60 kV and 100 kV as most effective for performing material decomposition with a deviation of 0.28 (0-1.07) mg/ml. The feasibility of our voxelwise analyses using the proposed algorithm was then confirmed by the generation of phantom parameter-maps that matched the known contrast material concentrations. The results were mostly influenced by the noise-level and the concentrations used in the phantoms. MECT using a standard micro-CT combined with a matrix inversion method is feasible at four different imaging energies and allows the differentiation of mixtures of up to four contrast materials plus an additional residual material.
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Affiliation(s)
- Andrea Kronfeld
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
| | - Patrick Rose
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
- RheinMain University of Applied Sciences, Faculty of Engineering, Am Brückweg 26, 65428, Rüsselsheim am Main, Germany
| | - Jan Baumgart
- University Medical Center of the Johannes Gutenberg University Mainz, Translational Animal Research Center, Hanns-Dieter-Hüsch-Weg 19, 55128, Mainz, Germany
| | - Carolin Brockmann
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
| | - Ahmed E. Othman
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
| | - Bernd Schweizer
- RheinMain University of Applied Sciences, Faculty of Engineering, Am Brückweg 26, 65428, Rüsselsheim am Main, Germany
| | - Marc Alexander Brockmann
- University Medical Center of the Johannes Gutenberg University Mainz, Department of Neuroradiology, Langenbeck 1, 55131, Mainz, Germany
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Vecsey-Nagy M, Varga-Szemes A, Emrich T, Zsarnoczay E, Nagy N, Fink N, Schmidt B, Nowak T, Kiss M, Vattay B, Boussoussou M, Kolossváry M, Kubovje A, Merkely B, Maurovich-Horvat P, Szilveszter B. Calcium scoring on coronary computed angiography tomography with photon-counting detector technology: Predictors of performance. J Cardiovasc Comput Tomogr 2023; 17:328-335. [PMID: 37635032 DOI: 10.1016/j.jcct.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/10/2023] [Accepted: 08/05/2023] [Indexed: 08/29/2023]
Abstract
INTRODUCTION Obtaining accurate coronary artery calcium (CAC) score measurements from CCTA datasets with virtual non-iodine (VNI) algorithms would reduce acquisition time and radiation dose. We aimed to assess the agreement of VNI-derived and conventional true non-contrast (TNC)-based CAC scores and to identify the predictors of accuracy. METHODS CCTA datasets were acquired with either 120 or 140 kVp. CAC scores and volumes were calculated from TNC and VNI images in 197 consecutive patients undergoing CCTA. CAC density score, mean volume/lesion, aortic Hounsfield units and standard deviations were then measured. Finally, percentage deviation (VNI - TNC/TNC∗100) of CTA-derived CAC scores from non-enhanced scans was calculated for each patient. Predictors (including anthropometric and acquisition parameters, as well as CAC characteristics) of the degree of discrepancy were evaluated using linear regression analysis. RESULTS While the agreement between TNC and VNI was substantial (mean bias, 6.6; limits of agreement, 178.5/145.3), a non-negligible proportion of patients (36/197, 18.3%) were falsely reclassified as CAC score = 0 on VNI. The use of higher tube voltage significantly decreased the percentage deviation relative to TNC-based values (β = -0.21 [95%CI: 0.38 to -0.03], p = 0.020) and a higher CAC density score also proved to be an independent predictor of a smaller difference (β = -0.22 [95%CI: 0.37 to -0.07], p = 0.006). CONCLUSION The performance of VNI-based calcium scoring may be improved by increased tube voltage protocols, while the accuracy may be compromised for calcified lesions of lower density. The implementation of VNI in clinical routine, however, needs to be preceded by a solution for detecting smaller lesions as well.
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Affiliation(s)
- M Vecsey-Nagy
- Heart and Vascular Center of Semmelweis University, Budapest, Hungary; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - A Varga-Szemes
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
| | - T Emrich
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany
| | - E Zsarnoczay
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Medical Imaging Center of Semmelweis University, Budapest, Hungary
| | - N Nagy
- Medical Imaging Center of Semmelweis University, Budapest, Hungary
| | - N Fink
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - B Schmidt
- Siemens Healthcare GmbH, Forchheim, Germany
| | - T Nowak
- Siemens Healthcare GmbH, Forchheim, Germany
| | - M Kiss
- Siemens Healthcare GmbH, Forchheim, Germany
| | - B Vattay
- Heart and Vascular Center of Semmelweis University, Budapest, Hungary
| | - M Boussoussou
- Heart and Vascular Center of Semmelweis University, Budapest, Hungary
| | - M Kolossváry
- Gottsegen National Cardiovascular Center, Budapest, Hungary; Physiological Controls Research Center, Budapest, Hungary
| | - A Kubovje
- Medical Imaging Center of Semmelweis University, Budapest, Hungary
| | - B Merkely
- Heart and Vascular Center of Semmelweis University, Budapest, Hungary
| | | | - B Szilveszter
- Heart and Vascular Center of Semmelweis University, Budapest, Hungary.
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Wei J, Chen P, Liu B, Han Y. A Multienergy Computed Tomography Method without Image Segmentation or Prior Knowledge of X-ray Spectra or Materials. Heliyon 2022; 8:e11584. [DOI: 10.1016/j.heliyon.2022.e11584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/01/2022] [Accepted: 11/07/2022] [Indexed: 11/17/2022] Open
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Gong H, Baffour F, Glazebrook KN, Rhodes NG, Tiegs-Heiden CA, Thorne JE, Cook JM, Kumar S, Fletcher JG, McCollough CH, Leng S. Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT. Med Phys 2022; 49:6346-6358. [PMID: 35983992 PMCID: PMC9588661 DOI: 10.1002/mp.15934] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/27/2022] [Accepted: 08/04/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Dual-energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise and artifacts due to material decomposition used in synthesizing VNCa images. OBJECTIVES In this work, we aim to improve VNCa image quality for the assessment of focal multiple myeloma, using an Artificial intelligence based Generalizable Algorithm for mulTi-Energy CT (AGATE) method. MATERIALS AND METHODS AGATE method used a custom dual-task convolutional neural network (CNN) that concurrently carries out material classification and quantification. The material classification task provided an auxiliary regularization to the material quantification task. CNN parameters were optimized using custom loss functions that involved cross-entropy, physics-informed constraints, structural redundancy in spectral and material images, and texture information in spectral images. For training data, CT phantoms (diameters 30 to 45 cm) with tissue-mimicking inserts were scanned on a third generation dual-source CT system. Scans were performed at routine dose and half of the routine dose. Small image patches (i.e., 40 × 40 pixels) of tissue-mimicking inserts with known basis material densities were extracted for training samples. Numerically simulated insert materials with various shapes increased diversity of training samples. Generalizability of AGATE was evaluated using CT images from phantoms and patients. In phantoms, material decomposition accuracy was estimated using mean-absolute-percent-error (MAPE), using physical inserts that were not used during the training. Noise power spectrum (NPS) and modulation transfer function (MTF) were compared across phantom sizes and radiation dose levels. Five patients with multiple myeloma underwent dual-energy CT, with VNCa images generated using a commercial method and AGATE. Two fellowship-trained musculoskeletal radiologists reviewed the VNCa images (commercial and AGATE) side-by-side using a dual-monitor display, blinded to VNCa type, rating the image quality for focal multiple myeloma lesion visualization using a 5-level Likert comparison scale (-2 = worse visualization and diagnostic confidence, -1 = worse visualization but equivalent diagnostic confidence, 0 = equivalent visualization and diagnostic confidence, 1 = improved visualization but equivalent diagnostic confidence, 2 = improved visualization and diagnostic confidence). A post hoc assignment of comparison ratings was performed to rank AGATE images in comparison to commercial ones. RESULTS AGATE demonstrated consistent material quantification accuracy across phantom sizes and radiation dose levels, with MAPE ranging from 0.7% to 4.4% across all testing materials. Compared to commercial VNCa images, the AGATE-synthesized VNCa images yielded considerably lower image noise (50-77% noise reduction) without compromising noise texture or spatial resolution across different phantom sizes and two radiation doses. AGATE VNCa images had markedly reduced area under NPS curves and maintained NPS peak frequency (0.7 lp/cm to 1.0 lp/cm), with similar MTF curves (50% MTF at 3.0 lp/cm). In patients, AGATE demonstrated reduced image noise and artifacts with improved delineation of focal multiple myeloma lesions (all readers comparison scores indicating improved overall diagnostic image quality [scores 1 or 2]). CONCLUSIONS AGATE demonstrated reduced noise and artifacts in VNCa images and ability to improve visualization of bone marrow lesions for assessing multiple myeloma.
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Affiliation(s)
- Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | - Joselle M. Cook
- Department of Medicine, Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | - Shaji Kumar
- Department of Medicine, Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Shuai Leng
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Li Z, Long Y, Chun IY. An improved iterative neural network for high-quality image-domain material decomposition in dual-energy CT. Med Phys 2022; 50:2195-2211. [PMID: 35735056 DOI: 10.1002/mp.15817] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/08/2021] [Accepted: 01/11/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE Dual-energy computed tomography (DECT) has been widely used in many applications that need material decomposition. Image-domain methods directly decompose material images from high- and low-energy attenuation images, and thus, are susceptible to noise and artifacts on attenuation images. The purpose of this study is to develop an improved iterative neural network (INN) for high-quality image-domain material decomposition in DECT, and to study its properties. METHODS We propose a new INN architecture for DECT material decomposition. The proposed INN architecture uses distinct cross-material convolutional neural network (CNN) in image refining modules, and uses image decomposition physics in image reconstruction modules. The distinct cross-material CNN refiners incorporate distinct encoding-decoding filters and cross-material model that captures correlations between different materials. We study the distinct cross-material CNN refiner with patch-based reformulation and tight-frame condition. RESULTS Numerical experiments with extended cardiac-torso phantom and clinical data show that the proposed INN significantly improves the image quality over several image-domain material decomposition methods, including a conventional model-based image decomposition (MBID) method using an edge-preserving regularizer, a recent MBID method using pre-learned material-wise sparsifying transforms, and a noniterative deep CNN method. Our study with patch-based reformulations reveals that learned filters of distinct cross-material CNN refiners can approximately satisfy the tight-frame condition. CONCLUSIONS The proposed INN architecture achieves high-quality material decompositions using iteration-wise refiners that exploit cross-material properties between different material images with distinct encoding-decoding filters. Our tight-frame study implies that cross-material CNN refiners in the proposed INN architecture are useful for noise suppression and signal restoration. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zhipeng Li
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yong Long
- University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Il Yong Chun
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Gyeonggi, 16419, Republic of Korea
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Lin L, Han L, Jia S, Zhang T, Liu Z, Fan J. Evaluating image quality and optimal parameters for non-linear blending dual-energy computed tomography images of hepatic portal veins by blending-property-map. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:835-846. [PMID: 35599529 DOI: 10.3233/xst-221182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Blending technology is usually used to improve quality of dual-energy computed (DECT) images. OBJECTIVES To evaluate the blended DECT image qualities by employing the Blending-Property-Map (BP-Map) and elucidating the optimal parameters with the highest signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). METHODS Sixty pairs of 80 kV and 140 kV CT images are blended non-linearly by four methods. Protocol A uses the fixed values of blending width (BW) and blending center (BC); Protocol B uses the values of BW = (CThepatic portal vein - CThepatic parenchymal) / 2 and BC = (CThepatic portal vein + CThepatic parenchymal) / 2; Protocol C uses a BW ranging from 10 to 100 HU at an interval of 10 HU and BC = (CThepatic portal vein + CThepatic parenchymal) / 2; Protocol D uses the BP-Map that covers all possible values of BW and BC. RESULTS When using CT value of adipose tissue as noise, the calculated SNR and CNR of optimal blending width and blending center were 123.22±41.73 and 9.00±3.52, respectively, by the BP-Map in the protocol D. By employing the CT value of back muscle as noise, the SNR and CNR of the best-blended images were 75.90±14.52 and 6.39±2.37, respectively. The subjective score of protocol D was 4.88±0.12. CONCLUSIONS Compared to traditional blending methods, the BP-Map technique can determine the optimal blending parameter and provide the best-blended images with the highest SNR and CNR.
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Affiliation(s)
- Liying Lin
- First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Li Han
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Shaowei Jia
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Tianyou Zhang
- Department of Radiology, Chinese Academy of Medical Sciences Institute of Hematology and Blood Diseases Hospital, Tianjin, China
| | - Zefeng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Fan
- School of Linguistics, Hebei University of Technology, Tianjin, China
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Lee D, Yun S, Soh J, Lim S, Kim H, Cho S. A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:549-566. [PMID: 35253722 DOI: 10.3233/xst-211054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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.
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Affiliation(s)
- Donghyeon Lee
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Sungho Yun
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Jeongtae Soh
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Sunho Lim
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Hyoyi Kim
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Seungryong Cho
- Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- KAIST Institutes for ITC, AI, and HST, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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Fang W, Wu D, Kim K, Kalra MK, Singh R, Li L, Li Q. Iterative material decomposition for spectral CT using self-supervised Noise2Noise prior. Phys Med Biol 2021; 66. [PMID: 34126602 DOI: 10.1088/1361-6560/ac0afd] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/14/2021] [Indexed: 11/11/2022]
Abstract
Compared to conventional computed tomography (CT), spectral CT can provide the capability of material decomposition, which can be used in many clinical diagnosis applications. However, the decomposed images can be very noisy due to the dose limit in CT scanning and the noise magnification of the material decomposition process. To alleviate this situation, we proposed an iterative one-step inversion material decomposition algorithm with a Noise2Noise prior. The algorithm estimated material images directly from projection data and used a Noise2Noise prior for denoising. In contrast to supervised deep learning methods, the designed Noise2Noise prior was built based on self-supervised learning and did not need external data for training. In our method, the data consistency term and the Noise2Noise network were alternatively optimized in the iterative framework, respectively, using a separable quadratic surrogate (SQS) and the Adam algorithm. The proposed iterative algorithm was validated and compared to other methods on simulated spectral CT data, preclinical photon-counting CT data and clinical dual-energy CT data. Quantitative analysis showed that our proposed method performs promisingly on noise suppression and structure detail recovery.
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Affiliation(s)
- Wei Fang
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, People's Republic of China.,Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Dufan Wu
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America.,Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Kyungsang Kim
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America.,Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Ramandeep Singh
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Liang Li
- Department of Engineering Physics, Tsinghua University, Beijing, 100084, People's Republic of China
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America.,Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
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12
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Jiang X, Fang C, Hu P, Cui H, Zhu L, Yang Y. Fast and effective single-scan dual-energy cone-beam CT reconstruction and decomposition denoising based on dual-energy vectorization. Med Phys 2021; 48:4843-4856. [PMID: 34289129 DOI: 10.1002/mp.15117] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/11/2021] [Accepted: 07/02/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Flat-panel detector (FPD) based dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single-scan DE-CBCT image reconstruction and decomposition. METHODS A rotatable Mo filter was inserted between an x-ray source and imaged object to alternately produce low and high-energy x-ray spectra. First, filtered-backprojection (FBP) method was applied on down-sampled projections to reconstruct low and high-energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high-quality dual-energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual-energy images were further used for low-noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan® 600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two-scan method and a commonly used filtering method (HYPR-LR). RESULTS On the Catphan® 600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise-power-spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast-to-noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft-tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two-scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two-scan results, and by over 40.4% compared with HYPR-LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. CONCLUSIONS Using down-sampled projections in single-scan DE-CBCT, the proposed method could effectively and efficiently produce high-quality DE-CBCT images and low-noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.
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Affiliation(s)
- Xiao Jiang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Chengyijue Fang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Panpan Hu
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.,Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Hehe Cui
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Lei Zhu
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Yidong Yang
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.,School of Physical Sciences & Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, China
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13
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Wu W, Chen P, Wang S, Vardhanabhuti V, Liu F, Yu H. Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:537-547. [PMID: 34222737 PMCID: PMC8248524 DOI: 10.1109/trpms.2020.2997880] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.
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Affiliation(s)
- Weiwen Wu
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, China
| | - Peijun Chen
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Shaoyu Wang
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, 999077, China
| | - Fenglin Liu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
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14
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Xue Y, Qin W, Luo C, Yang P, Jiang Y, Tsui T, He H, Wang L, Qin J, Xie Y, Niu T. Multi-Material Decomposition for Single Energy CT Using Material Sparsity Constraint. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1303-1318. [PMID: 33460369 DOI: 10.1109/tmi.2021.3051416] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Multi-material decomposition (MMD) decomposes CT images into basis material images, and is a promising technique in clinical diagnostic CT to identify material compositions within the human body. MMD could be implemented on measurements obtained from spectral CT protocol, although spectral CT data acquisition is not readily available in most clinical environments. MMD methods using single energy CT (SECT), broadly applied in radiological departments of most hospitals, have been proposed in the literature while challenged by the inferior decomposition accuracy and the limited number of material bases due to the constrained material information in the SECT measurement. In this paper, we propose an image-domain SECT MMD method using material sparsity as an assistance under the condition that each voxel of the CT image contains at most two different elemental materials. L0 norm represents the material sparsity constraint (MSC) and is integrated into the decomposition objective function with a least-square data fidelity term, total variation term, and a sum-to-one constraint of material volume fractions. An accelerated primal-dual (APD) algorithm with line-search scheme is applied to solve the problem. The pixelwise direct inversion method with the two-material assumption (TMA) is applied to estimate the initials. We validate the proposed method on phantom and patient data. Compared with the TMA method, the proposed MSC method increases the volume fraction accuracy (VFA) from 92.0% to 98.5% in the phantom study. In the patient study, the calcification area can be clearly visualized in the virtual non-contrast image generated by the proposed method, and has a similar shape to that in the ground-truth contrast-free CT image. The high decomposition image quality from the proposed method substantially facilitates the SECT-based MMD clinical applications.
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15
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Wu W, Yu H, Chen P, Luo F, Liu F, Wang Q, Zhu Y, Zhang Y, Feng J, Yu H. Dictionary learning based image-domain material decomposition for spectral CT. Phys Med Biol 2020; 65:245006. [PMID: 32693395 DOI: 10.1088/1361-6560/aba7ce] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The potential huge advantage of spectral computed tomography (CT) is that it can provide accurate material identification and quantitative tissue information by material decomposition. However, material decomposition is a typical inverse problem, where the noise can be magnified. To address this issue, we develop a dictionary learning based image-domain material decomposition (DLIMD) method for spectral CT to achieve accurate material components with better image quality. Specifically, a set of image patches are extracted from the mode-1 unfolding of normalized material images decomposed by direct inversion to train a unified dictionary using the K-SVD technique. Then, the DLIMD model is established to explore the redundant similarities of the material images, where the split-Bregman is employed to optimize the model. Finally, more constraints (i.e. volume conservation and the bounds of each pixel within material maps) are integrated into the DLIMD model. Numerical phantom, physical phantom and preclinical experiments are performed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.
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Affiliation(s)
- Weiwen Wu
- Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China
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16
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Gong H, Tao S, Rajendran K, Zhou W, McCollough CH, Leng S. Deep-learning-based direct inversion for material decomposition. Med Phys 2020; 47:6294-6309. [PMID: 33020942 DOI: 10.1002/mp.14523] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 09/16/2020] [Accepted: 10/24/2020] [Indexed: 01/25/2023] Open
Abstract
PURPOSE To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi-energy computed tomography (CT) images without performing conventional material decomposition. METHODS The proposed CNN (denoted as Incept-net) followed the general framework of encoder-decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi-energy CT. Incept-net was implemented with a customized loss function, including an in-house-designed image-gradient-correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood-iodine mixture, and fat] were scanned using a research photon-counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different-configurations of full field-of-view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept-net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least-square-based material decomposition (LS-MD), total-variation regularized material decomposition (TV-MD), and U-net-based method. RESULTS Incept-net improved accuracy of the predicted mass density of basis materials compared with the U-net, TV-MD, and LS-MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept-net, U-net, TV-MD, and LS-MD, respectively, across all iodine-present inserts (2.0-24.0 mgI/cc). With the LS-MD as the baseline, Incept-net and U-net achieved comparable noise reduction (both around 95%), both higher than TV-MD (85%). The proposed IGC regularizer effectively helped both Incept-net and U-net to reduce image artifact. Incept-net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept-net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept-net achieved relatively improved performance than the comparator U-net, indicating that performance gain by Incept-net was not achieved by simply increasing network learning capacity. CONCLUSION Incept-net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept-net generalized and performed well with unseen image structures and different material mass densities. This study provided preliminary evidence that the proposed CNN may be used to improve the material decomposition quality in multi-energy CT.
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Affiliation(s)
- Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA
| | - Shengzhen Tao
- Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA
| | | | - Wei Zhou
- Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA
| | | | - Shuai Leng
- Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA
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17
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Noid G, Zhu J, Tai A, Mistry N, Schott D, Prah D, Paulson E, Schultz C, Li XA. Improving Structure Delineation for Radiation Therapy Planning Using Dual-Energy CT. Front Oncol 2020; 10:1694. [PMID: 32984048 PMCID: PMC7484725 DOI: 10.3389/fonc.2020.01694] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 07/29/2020] [Indexed: 12/11/2022] Open
Affiliation(s)
- George Noid
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Justin Zhu
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - An Tai
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Nilesh Mistry
- Siemens Medical Solutions USA, Inc., Malvern, PA, United States
| | - Diane Schott
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Douglas Prah
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Christopher Schultz
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - X. Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
- *Correspondence: X. Allen Li,
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18
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Jacobsen MC, Thrower SL. Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement. Med Phys 2020; 47:3752-3771. [PMID: 32453879 PMCID: PMC8495770 DOI: 10.1002/mp.14241] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/21/2022] Open
Abstract
Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual-energy CT (DECT) and photon-counting detector CT (PCD-CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD-CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi-energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image-guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.
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Affiliation(s)
- Megan C. Jacobsen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sara L. Thrower
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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19
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Li Z, Ravishankar S, Long Y, Fessler JA. DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1223-1234. [PMID: 31603815 PMCID: PMC7263375 DOI: 10.1109/tmi.2019.2946177] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.
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20
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Xue Y, Luo C, Jiang Y, Yang P, Hu X, Zhou Q, Wang J, Hu X, Sheng K, Niu T. Image domain multi-material decomposition using single energy CT. Phys Med Biol 2020; 65:065014. [PMID: 32045890 DOI: 10.1088/1361-6560/ab7503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Multi-material decomposition (MMD) technique decomposes the CT images into basis material images and has been promising in clinical practice for material composition quantification within the human body. MMD could be implemented using the image data acquired from spectral CT or its special case, dual-energy CT (DECT) while the spectral CT data acquisition usually requires a hardware modification. In this paper, we propose an image domain MMD method using single energy CT (SECT). The proposed objective function applies a least square data fidelity term to enforce the minimization between the linear combination of decomposed material image and the measured SECT image, and an edge-preserving (EP) regularization term to meet the piecewise constant property of the material image. We apply the optimization transfer principle to form a pixel-wise separable quadratic surrogate (PWSQS) function in each iteration to decrease the objective function. The pixelwise direct inversion method assisted by the two-material assumption (TMA) is applied to obtain a good initial value. The proposed method is evaluated using a digital phantom, a Catphan phantom and the clinical data. A low-pass filtration method is implemented for a comparison purpose. In the phantom study, the proposed TMA method achieves high volume fraction accuracy (VFA) of 79.64% and the proposed EP method further increases the VFA by 15.56% and decreases the decomposition standard deviation (STD) by 81.51% compared with the TMA method. At the comparable noise level, the proposed EP method increases spatial resolution by an overall factor of 1.01 when the modulation transfer function magnitude is decreased to 50% compared with the low-pass filtration method. In clinical data study, the virtual non-contrast image generated by the proposed method achieves the root-mean-squared-relative error of 2.93% compared with the contrast-free ground-truth image.
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Affiliation(s)
- Yi Xue
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, People's Republic of China. Both authors contribute equally
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21
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Jiang Y, Zhang X, Sheng K, Niu T, Xue Y, Lyu Q, Xu L, Luo C, Yang P, Yang C, Wang J, Hu X. Noise Suppression in Image-Domain Multi-Material Decomposition for Dual-Energy CT. IEEE Trans Biomed Eng 2020; 67:523-535. [DOI: 10.1109/tbme.2019.2916907] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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22
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Zhang W, Wang L, Li L, Niu T, Li Z, Liang N, Xue Y, Yan B, Hu G. Reconstruction method for DECT with one half-scan plus a second limited-angle scan using prior knowledge of complementary support set (Pri-CSS). Phys Med Biol 2020; 65:025005. [PMID: 31810075 DOI: 10.1088/1361-6560/ab5faf] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Dual-energy computed tomography (DECT) has capability to improve material differentiation, but most scanning schemes require two sets of full-scan measurements at different x-ray spectra, limiting its application to imaging system with incomplete scan. In this study, using one half-scan and a second limited-angle scan, we propose a DECT reconstruction method by exploiting the consistent information of gradient images at high- and low-energy spectra, which relaxes the requirement of data acquisition of DECT. Based on the theory of sampling condition analysis, the complementary support set of gradient images plays an important role in image reconstruction because it constitutes the sufficient and necessary condition for accurate CT reconstruction. For DECT, the gradient images of high- and low-energy CT images ideally share the same complementary support set for the same object. Inspired by this idea, we extract the prior knowledge of complementary support set (Pri-CSS) from the gradient image of the first half-scan CT image to promote the second limited-angle CT reconstruction. Pri-CSS will be incorporated into total variation regularization model in the form of constrains. Alternative direction method is applied to iteratively solve the modified optimization model, thereby deriving the proposed algorithm to recover low-energy CT image from limited-angle measurements. The qualitative and quantitative experiments on digital and real data are performed to validate the proposed method. The results show that the proposed method outperforms its counterparts and achieve high reconstruction quality for the designed scanning configuration.
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23
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Niu S, Lu S, Zhang Y, Huang X, Zhong Y, Yu G, Wang J. Statistical image-based material decomposition for triple-energy computed tomography using total variation regularization. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:751-771. [PMID: 32597827 DOI: 10.3233/xst-200672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
BACKGROUND Triple-energy computed tomography (TECT) can obtain x-ray attenuation measurements at three energy spectra, thereby allowing identification of different material compositions with same or very similar attenuation coefficients. This ability is known as material decomposition, which can decompose TECT images into different basis material image. However, the basis material image would be severely degraded when material decomposition is directly performed on the noisy TECT measurements using a matrix inversion method. OBJECTIVE To achieve high quality basis material image, we present a statistical image-based material decomposition method for TECT, which uses the penalized weighted least-squares (PWLS) criteria with total variation (TV) regularization (PWLS-TV). METHODS The weighted least-squares term involves the noise statistical properties of the material decomposition process, and the TV regularization penalizes differences between local neighboring pixels in a decomposed image, thereby contributing to improving the quality of the basis material image. Subsequently, an alternating optimization method is used to minimize the objective function. RESULTS The performance of PWLS-TV is quantitatively evaluated using digital and mouse thorax phantoms. The experimental results show that PWLS-TV material decomposition method can greatly improve the quality of decomposed basis material image compared to the quality of images obtained using the competing methods in terms of suppressing noise and preserving edge and fine structure details. CONCLUSIONS The PWLS-TV method can simultaneously perform noise reduction and material decomposition in one iterative step, and it results in a considerable improvement of basis material image quality.
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Affiliation(s)
- Shanzhou Niu
- Jiangxi Key Laboratory of Numerical Simulation Technology, School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shaohui Lu
- Jiangxi Key Laboratory of Numerical Simulation Technology, School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China
| | - You Zhang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiaokun Huang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yuncheng Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gaohang Yu
- School of Science, Hangzhou Dianzi University, Hangzhou, China
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Ren L, Rajendran K, McCollough CH, Yu L. Quantitative accuracy and dose efficiency of dual-contrast imaging using dual-energy CT: a phantom study. Med Phys 2019; 47:441-456. [PMID: 31705664 DOI: 10.1002/mp.13912] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 10/28/2019] [Accepted: 10/31/2019] [Indexed: 12/17/2022] Open
Abstract
PURPOSE To evaluate the quantitative accuracy and dose efficiency of simultaneous imaging of two contrast agents using dual-energy computed tomography (DECT), two imaging tasks each representing one potential clinical application were investigated in a phantom study: biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth. METHODS To separate and quantify mixtures of two contrast agents using a single DECT scan, mixed iodine and gadolinium samples were prepared with the contrast enhancement values corresponding to the late arterial (iodine) and the portal-venous (gadolinium) phase for biphasic liver imaging. Mixed iodine and bismuth samples were prepared mimicking the arterial (iodine) and the enteric (bismuth) enhancement for small bowel imaging. For comparison to the reference condition of performing two single-energy CT (SECT) scans, contrast samples were prepared separately to mimic separate scans in the arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water tank and scanned using a third-generation dual-source DECT scanner with three tube potential pairs: 80/Sn150, 90/Sn150, and 100/Sn150 kV, all with default dose partitioning between two x-ray beams to acquire DECT data. The same scanner operated in a single-energy mode acquired SECT data (120 kV). Total radiation dose (CTDIvol) was matched for the single-scan DECT and the two-scan SECT protocols. The DECT protocol was followed by a generic image-based three-material decomposition method to determine the material-specific images, based on which concentrations of each basis material were quantified and noise levels were measured. To compare with the SECT images directly acquired with the SECT protocol, the concentration values in each contrast-specific image were converted to CT numbers at 120 kV (i.e., virtual SECT (vSECT) images). The noise level and noise power spectra differences between the SECT and vSECT images were compared to evaluate the dose efficiency of the single-scan DECT protocol. The impact of dose partitioning in the DECT protocol on quantitative dual-contrast imaging performance was also studied. RESULTS For each imaging task, contrast materials were accurately quantified against the nominal concentrations using the DECT data with strong correlation (R2 ≥ 0.98 for both imaging tasks). Compared to the SECT protocol, the DECT protocol was not dose efficient. With the optimal x-ray tube potential pair 80/Sn150 kV, the noise level in vSECT images increased by 401%/488% (arterial/portal-venous) for the biphasic liver imaging task and by 10%/41% (arterial/enteric) for the small bowel imaging task compared to that in SECT images. The corresponding radiation dose increase is 2410%/3357% for the biphasic liver imaging task and 21%/99% for the small bowel imaging task, respectively, to achieve the same noise as that in SECT images. This could be improved by adjusting the dose partitioning in DECT. CONCLUSIONS DECT can be used to simultaneously separate and quantify two contrast materials. However, compared to a two-scan SECT protocol, much higher radiation dose is needed in a single-scan DECT protocol to achieve the same image noise, especially for tasks involving the dual contrast of iodine and gadolinium.
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Affiliation(s)
- Liqiang Ren
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
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Liu C, Huang H. Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging. J Appl Clin Med Phys 2019; 20:104-113. [PMID: 31390137 PMCID: PMC6753738 DOI: 10.1002/acm2.12694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 06/05/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Purpose Virtual monoenergetic images (VMIs) derived from dual‐energy computed tomography (DECT) have been explored for several clinical applications in recent years. However, VMIs at low and high keVs have high levels of noise. The aim of this study was to reduce image noise in VMIs by using a two‐step noise reduction technique. Methods VMI was first denoised using a modified highly constrained backprojection (HYPR) method. After the first‐step denoising, a general‐threshold filtering method was performed. Two sets of anthropomorphic phantoms were scanned with a clinical dual‐source DECT system. DECT data (80/140Sn kV) were reconstructed as VMI series at 12 different energy levels (range, 40‐150 keV, interval, 10 keV). For comparison, the averaged VMIs obtained from 10 repeated DECT scans were used as the reference standard. The signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR) and root‐mean‐square error (RMSE) were used to evaluate the quality of VMIs. Results Compared to the original HYPR method, the proposed two‐step image denoising method could provide better performance in terms of SNR, CNR, and RMSE. In addition, the proposed method could achieve effective noise reduction while preserving edges and small structures, especially for low‐keV VMIs. Conclusion The proposed two‐step image denoising method is a feasible method for reducing noise in VMIs obtained from a clinical DECT scanner. The proposed method can also reduce edge blurring and the loss of intensity in small lesions.
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Affiliation(s)
- Chi‐Kuang Liu
- Department of Medical Imaging Changhua Christian Hospital Changhua City Taiwan
| | - Hsuan‐Ming Huang
- Institute of Medical Device and Imaging, College of Medicine National Taiwan University Taipei Taiwan
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Fredette NR, Kavuri A, Das M. Multi-step material decomposition for spectral computed tomography. ACTA ACUST UNITED AC 2019; 64:145001. [DOI: 10.1088/1361-6560/ab2b0e] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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27
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Yao Y, Li L, Chen Z. Dynamic-dual-energy spectral CT for improving multi-material decomposition in image-domain. ACTA ACUST UNITED AC 2019; 64:135006. [DOI: 10.1088/1361-6560/ab196d] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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28
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Li M, Wang Z, Xu Q, Zhang Z, Cheng Z, Liu S, Liu B, Wei C, Wei L. A study on noise reduction for dual-energy CT material decomposition with autoencoder. RADIATION DETECTION TECHNOLOGY AND METHODS 2019. [DOI: 10.1007/s41605-019-0122-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Zhang W, Zhang H, Wang L, Wang X, Hu X, Cai A, Li L, Niu T, Yan B. Image domain dual material decomposition for dual‐energyCTusing butterfly network. Med Phys 2019; 46:2037-2051. [DOI: 10.1002/mp.13489] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 01/13/2019] [Accepted: 02/28/2019] [Indexed: 01/29/2023] Open
Affiliation(s)
- Wenkun Zhang
- National Digital Switching System Engineering and Technological Research Center Zhengzhou Henan 450002China
| | - Hanming Zhang
- National Digital Switching System Engineering and Technological Research Center Zhengzhou Henan 450002China
| | - Linyuan Wang
- National Digital Switching System Engineering and Technological Research Center Zhengzhou Henan 450002China
| | - Xiaohui Wang
- 153 Central Hospital of Henan Province Zhengzhou Henan 4500002China
| | - Xiuhua Hu
- Sir Run Run Shaw Hospital Zhejiang University School of Medicine Institute of Translational Medicine Zhejiang University Hangzhou Zhejiang 310009 China
| | - Ailong Cai
- National Digital Switching System Engineering and Technological Research Center Zhengzhou Henan 450002China
| | - Lei Li
- National Digital Switching System Engineering and Technological Research Center Zhengzhou Henan 450002China
| | - Tianye Niu
- Sir Run Run Shaw Hospital Zhejiang University School of Medicine Institute of Translational Medicine Zhejiang University Hangzhou Zhejiang 310009 China
| | - Bin Yan
- National Digital Switching System Engineering and Technological Research Center Zhengzhou Henan 450002China
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Ren L, Tao S, Rajendran K, McCollough CH, Yu L. Impact of prior information on material decomposition in dual- and multienergy computed tomography. J Med Imaging (Bellingham) 2019; 6:013503. [PMID: 30891466 DOI: 10.1117/1.jmi.6.1.013503] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 02/12/2019] [Indexed: 01/13/2023] Open
Abstract
Prior information is often included in the basis material decomposition to solve the quantification problem of three-material mixtures in dual-energy computed tomography (DECT). Multienergy computed tomography (MECT) with more than two energy bins can provide a sufficient solution to this problem without invoking additional prior information. However, a question remains as to whether the prior information should still be included in the material decomposition process using MECT to improve the quantification accuracy and control noise amplification. This study aims to evaluate the impact of the prior information on noise and quantification bias in both DECT and MECT. The material decomposition tasks we used in this study are to quantify water/iodine, water/iodine/gadolinium, and water/ iodine/calcium in two- and three-material decompositions, under the assumption that the object to be decomposed consists of the basis materials and their mixtures. We performed phantom simulation and experimental studies using a clinical DECT system and a research photon-counting-detector-based MECT system. Results in the current phantom studies show that the prior information can still improve the noise performance without substantially affecting the basis material quantitative accuracy during the material decomposition process, even when the number of x-ray energy beams/bins is equal or greater than the number of basis materials.
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Affiliation(s)
- Liqiang Ren
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Shengzhen Tao
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | - Kishore Rajendran
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
| | | | - Lifeng Yu
- Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States
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Levine ZH, Peskin AP, Garboczi EJ, Holmgren AD. Multi-Energy X-Ray Tomography of an Optical Fiber: The Role of Spatial Averaging. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2019; 25:70-76. [PMID: 30869576 PMCID: PMC6572719 DOI: 10.1017/s1431927618016136] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Using a commercial X-ray tomography instrument, we have obtained reconstructions of a graded-index optical fiber with voxels of edge length 1.05 µm at 12 tube voltages. The fiber manufacturer created a graded index in the central region by varying the germanium concentration from a peak value in the center of the core to a very small value at the core-cladding boundary. Operating on 12 tube voltages, we show by a singular value decomposition that there are only two singular vectors with significant weight. Physically, this means scans beyond two tube voltages contain largely redundant information. We concentrate on an analysis of the images associated with these two singular vectors. The first singular vector is dominant and images of the coefficients of the first singular vector at each voxel look are similar to any of the single-energy reconstructions. Images of the coefficients of the second singular vector by itself appear to be noise. However, by averaging the reconstructed voxels in each of several narrow bands of radii, we can obtain values of the second singular vector at each radius. In the core region, where we expect the germanium doping to go from a peak value at the fiber center to zero at the core-cladding boundary, we find that a plot of the two coefficients of the singular vectors forms a line in the two-dimensional space consistent with the dopant decreasing linearly with radial distance from the core center. The coating, made of a polymer rather than silica, is not on this line indicating that the two-dimensional results are sensitive not only to the density but also to the elemental composition.
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Affiliation(s)
- Zachary H Levine
- Sensor Science Division,National Institute of Standards and Technology,Gaithersburg, Maryland 20899-8441,USA
| | - Adele P Peskin
- Software and Systems Division,National Institute of Standards and Technology,Boulder, Colorado 80305-3337,USA
| | - Edward J Garboczi
- Applied Chemicals and Materials Division,National Institute of Standards and Technology,Boulder, Colorado 80305,USA
| | - Andrew D Holmgren
- Holmgren Professional Research Experience Program,University of Colorado,Boulder, Colorado 80309,USA
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Braig E, Böhm J, Dierolf M, Jud C, Günther B, Mechlem K, Allner S, Sellerer T, Achterhold K, Gleich B, Noël P, Pfeiffer D, Rummeny E, Herzen J, Pfeiffer F. Direct quantitative material decomposition employing grating-based X-ray phase-contrast CT. Sci Rep 2018; 8:16394. [PMID: 30401876 PMCID: PMC6219573 DOI: 10.1038/s41598-018-34809-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/24/2018] [Indexed: 11/09/2022] Open
Abstract
Dual-energy CT has opened up a new level of quantitative X-ray imaging for many diagnostic applications. The energy dependence of the X-ray attenuation is the key to quantitative material decomposition of the volume under investigation. This material decomposition allows the calculation of virtual native images in contrast enhanced angiography, virtual monoenergetic images for beam-hardening artifact reduction and quantitative material maps, among others. These visualizations have been proven beneficial for various diagnostic questions. Here, we demonstrate a new method of 'virtual dual-energy CT' employing grating-based phase-contrast for quantitative material decomposition. Analogue to the measurement at two different energies, the applied phase-contrast measurement approach yields dual information in form of a phase-shift and an attenuation image. Based on these two image channels, all known dual-energy applications can be demonstrated with our technique. While still in a preclinical state, the method features the important advantages of direct access to the electron density via the phase image, simultaneous availability of the conventional attenuation image at the full energy spectrum and therefore inherently registered image channels. The transfer of this signal extraction approach to phase-contrast data multiplies the diagnostic information gained within a single CT acquisition. The method is demonstrated with a phantom consisting of exemplary solid and fluid materials as well as a chicken heart with an iodine filled tube simulating a vessel. For this first demonstration all measurements have been conducted at a compact laser-undulator synchrotron X-ray source with a tunable X-ray energy and a narrow spectral bandwidth, to validate the quantitativeness of the processing approach.
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Affiliation(s)
- Eva Braig
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany.
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, 81675, München, Germany.
| | - Jessica Böhm
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Martin Dierolf
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Christoph Jud
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Benedikt Günther
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
- Max-Planck-Institute of Quantum Optics, Hans-Kopfermann-Straße 1, 85748, Garching, Germany
| | - Korbinian Mechlem
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, 81675, München, Germany
| | - Sebastian Allner
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Thorsten Sellerer
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Klaus Achterhold
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Bernhard Gleich
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Peter Noël
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, 81675, München, Germany
| | - Daniela Pfeiffer
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, 81675, München, Germany
| | - Ernst Rummeny
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, 81675, München, Germany
| | - Julia Herzen
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
| | - Franz Pfeiffer
- Chair of Biomedical Physics, Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, 81675, München, Germany
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Quantification of Cisplatin Using a Modified 3-Material Decomposition Algorithm at Third-Generation Dual-Source Dual-Energy Computed Tomography. Invest Radiol 2018; 53:673-680. [DOI: 10.1097/rli.0000000000000491] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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34
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Tao S, Rajendran K, McCollough CH, Leng S. Material decomposition with prior knowledge aware iterative denoising (MD-PKAID). ACTA ACUST UNITED AC 2018; 63:195003. [DOI: 10.1088/1361-6560/aadc90] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Shengzhen Tao
- Department of Radiology, Mayo Clinic, Rochester, MN, United States of America
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35
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Ding Q, Niu T, Zhang X, Long Y. Image-domain multimaterial decomposition for dual-energy CT based on prior information of material images. Med Phys 2018; 45:3614-3626. [PMID: 29807395 DOI: 10.1002/mp.13001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 05/07/2018] [Accepted: 05/07/2018] [Indexed: 01/16/2023] Open
Abstract
PURPOSE Dual-Energy Computed Tomography (DECT) is of great interest in medical imaging, security inspection, and nondestructive testing. Most DECT reconstruction methods focus on producing two material images with different linear attenuation coefficients. However, the ability to reconstruct three or more basis materials is clinically and industrially important. Under the assumption that there are at most three materials in each pixel, there are a few methods that estimate multiple material images from DECT measurements by enforcing sum-to-one and a box constraint ([0 1]) derived from both the volume and mass conservation assumption. The recently proposed image-domain multimaterial decomposition (MMD) method introduces edge-preserving regularization for each material image. It enforces the assumption that there are at most three materials in each pixel using a time-consuming loop over all possible material triplets. However, this method neglects relations among material images. We propose a new image-domain MMD model for DECT that considers the prior information that different material images have common or complementary edges and encourages sparsity of material composition in each pixel using regularization. METHOD The proposed PWLS-TNV-ℓ0 method uses penalized weighted least-square (PWLS) reconstruction with three regularization terms. The first term is total nuclear variation (TNV) that accounts for the image property that basis material images share common or complementary boundaries and each material image is piecewise constant. The second term is an ℓ0 norm that encourages each pixel containing a small subset of material types out of several possible materials. The third term is a characteristic function based on sum-to-one and a box constraint derived from the volume and mass conservation assumption. We apply the Alternating Direction Method of Multipliers (ADMM) to optimize the cost function of the PWLS-TNV-ℓ0 method. RESULT We evaluated the proposed method on a simulated digital phantom, Catphan©600 phantom and patient's pelvis data. We implemented two existing image-domain MMD methods for DECT, the Direct Inversion and the PWLS-EP-LOOP method. We initialized the PWLS-TNV-ℓ0 method and the PWLS-EP-LOOP method with the results of the Direct Inversion method and compared performance of the proposed method with that of the PWLS-EP-LOOP method. The proposed method lowers the bias of decomposed material fractions by 84.47% in the digital phantom study, by 99.50% in the Catphan©600 phantom study, and by 99.64% in the pelvis patient study, respectively, compared to the PWLS-EP-LOOP method. The proposed method reduces noise standard deviation (STD) by 52.21% in the Catphan©600 phantom study, and by 16.74% in the patient's pelvis study, compared to the PWLS-EP-LOOP method. The proposed method increases volume fraction accuracy by 6.04%,20.55%, and 13.46% for the digital phantom, the Catphan©600 phantom, and the patient's pelvis study, respectively, compared to the PWLS-EP-LOOP method. Compared with the PWLS-EP-LOOP method, the root mean square percentage error [RMSE(%)] of electron densities in the Catphan©600 phantom is decreased by about 7.39%. CONCLUSIONS We proposed an image-domain MMD method, PWLS-TNV-ℓ0 , for DECT. The PWLS-TNV-ℓ0 method takes low rank property of material image gradients, sparsity of material composition and mass and volume conservation into consideration. The proposed method suppresses noise, reduces cross contamination, and improves accuracy in the decomposed material images, compared to the PWLS-EP-LOOP method.
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Affiliation(s)
- Qiaoqiao Ding
- School of Mathematical Sciences, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai, 200240, China
| | - Tianye Niu
- Sir run run Shaw hospital, Zhejiang University school of medicine: institute of translational medicine, Zhejiang University, Hangzhou, 310020, China
| | - Xiaoqun Zhang
- School of Mathematical Sciences, MOE-LSC, and Institute of Natural Sciences, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Yong Long
- University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
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Zhao W, Vernekohl D, Han F, Han B, Peng H, Yang Y, Xing L, Min JK. A unified material decomposition framework for quantitative dual‐ and triple‐energy CT imaging. Med Phys 2018; 45:2964-2977. [DOI: 10.1002/mp.12933] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 01/26/2018] [Accepted: 02/25/2018] [Indexed: 12/16/2022] Open
Affiliation(s)
- Wei Zhao
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.,Department of Biomedical Engineering, Huazhong University of Science and Technology, Hubei, China
| | - Don Vernekohl
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Fei Han
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.,Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Bin Han
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Hao Peng
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - James K Min
- Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and Weill Cornell Medical College, New York, NY, 10021, USA
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