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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] [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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Ji X, Zhuo X, Lu Y, Mao W, Zhu S, Quan G, Xi Y, Lyu T, Chen Y. Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering. IEEE J Biomed Health Inform 2024; 28:2891-2903. [PMID: 38363665 DOI: 10.1109/jbhi.2023.3348135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.
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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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Duan J, Zhao Y, Sun Q, Liang D, Liu Z, Chen X, Li Z. Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer. Cancer Med 2023; 12:21256-21269. [PMID: 37962087 PMCID: PMC10726892 DOI: 10.1002/cam4.6704] [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] [Received: 07/05/2023] [Revised: 10/08/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.
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Affiliation(s)
- Jingxian Duan
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Yuanshen Zhao
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Qiuchang Sun
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Dong Liang
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of SciencesShenzhenChina
- National Innovation Center for Advanced Medical DevicesShenzhenChina
- Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
| | - Zaiyi Liu
- Department of RadiologyGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of MedicineSouth China University of TechnologyGuangzhouChina
| | - Zhi‐Cheng Li
- Institute of Biomedical and Health EngineeringShenzhen Institute of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of SciencesShenzhenChina
- National Innovation Center for Advanced Medical DevicesShenzhenChina
- Shenzhen United Imaging Research Institute of Innovative Medical EquipmentShenzhenChina
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Li C, Zhou L, Deng J, Wu H, Wang R, Wang F, Yao K, Chen C, Niu T, Zhang Y. A generalizable new figure of merit for dose optimization in dual energy cone beam CT scanning protocols. Phys Med Biol 2023; 68:185021. [PMID: 37619587 DOI: 10.1088/1361-6560/acf3cd] [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: 05/01/2023] [Accepted: 08/24/2023] [Indexed: 08/26/2023]
Abstract
Objective. This study proposes and evaluates a new figure of merit (FOMn) for dose optimization of Dual-energy cone-beam CT (DE-CBCT) scanning protocols based on size-dependent modeling of radiation dose and multi-scale image quality.Approach. FOMn was defined using Z-score normalization and was proportional to the dose efficiency providing better multi-scale image quality, including comprehensive contrast-to-noise ratio (CCNR) and electron density (CED) for CatPhan604 inserts of various materials. Acrylic annuluses were combined with CatPhan604 to create four phantom sizes (diameters of the long axis are 200 mm, 270 mm, 350 mm, and 380 mm, respectively). DE-CBCT was decomposed using image-domain iterative methods based on Varian kV-CBCT images acquired using 25 protocols (100 kVp and 140 kVp combined with 5 tube currents).Main results. The accuracy of CED was approximately 1% for all protocols, but degraded monotonically with the increased phantom sizes. Combinations of lower voltage + higher current and higher voltage + lower current were optimal protocols balancing CCNR and dose. The most dose-efficient protocols for CED and CCNR were inconsistent, underlining the necessity of including multi-scale image quality in the evaluation and optimization of DE-CBCT. Pediatric and adult anthropomorphic phantom tests confirmed dose-efficiency of FOMn-recommended protocols.Significance. FOMn is a comprehensive metric that collectively evaluates radiation dose and multi-scale image quality for DE-CBCT. The models and data can also serve as lookup tables, suggesting personalized dose-efficient protocols for specific clinical imaging purposes.
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Affiliation(s)
- Chenguang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China
- Department of Physics and Astronomy, University of British Columbia, 325-6224 Agricultural Road, Vancouver, BC V6T1Z1, Canada
| | - Li Zhou
- Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, People's Republic of China
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06510, United States of America
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China
| | - Ruoxi Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China
| | - Fei Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China
| | - Kaining Yao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China
| | - Chen Chen
- School of Electronics, Peking University, Beijing, 100871, People's Republic of China
| | - Tianye Niu
- Shenzhen Bay Laboratory, Shenzhen, 518118, People's Republic of China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, People's Republic of China
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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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Zhao X, Li Y, Han Y, Chen P, Wei J. Statistical iterative spectral CT imaging method based on blind separation of polychromatic projections. OPTICS EXPRESS 2022; 30:18219-18237. [PMID: 36221628 DOI: 10.1364/oe.456184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/02/2022] [Indexed: 06/16/2023]
Abstract
Spectral computed tomography (CT) can provide narrow-energy-width reconstructed images, thereby suppressing beam hardening artifacts and providing rich attenuation information for component characterization. We propose a statistical iterative spectral CT imaging method based on blind separation of polychromatic projections to improve the accuracy of narrow-energy-width image decomposition. For direct inversion in blind scenarios, we introduce the system matrix into the X-ray multispectral forward model to reduce indirect errors. A constrained optimization problem with edge-preserving regularization is established and decomposed into two sub-problems to be alternately solved. Experiments indicate that the novel algorithm obtains more accurate narrow-energy-width images than the state-of-the-art method.
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