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Li Q, Yuan Y, Song G, Liu Y. Nursing Analysis Based on Medical Imaging Technology before and after Coronary Angiography in Cardiovascular Medicine. Appl Bionics Biomech 2022; 2022:3279068. [PMID: 35465185 PMCID: PMC9033406 DOI: 10.1155/2022/3279068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/19/2022] [Accepted: 03/29/2022] [Indexed: 11/17/2022] Open
Abstract
With the advancement of technology, medical imaging technology has been greatly improved. This article mainly studies the nursing before and after coronary angiography in cardiovascular medicine based on medical imaging technology. This paper proposes a multimodal medical image fusion algorithm based on multiscale decomposition and convolution sparse representation. The algorithm first decomposes the preregistered source medical image by NSST, takes the subimages of different scales as training images, and optimizes the subdictionaries of different scales; then convolution and sparse the subimages on each scale encoding to obtain the sparse coefficients of different subimages; secondly, the combination of improved L1 norm and improved spatial frequency (novel sum-modified SF (NMSF)) is used for high-frequency subimage coefficients, and the fusion of low-frequency subimages improved the rule of combining the L1 norm and the regional energy; finally, the final fused image is obtained by inverse NSST of the fused low-frequency subband and high-frequency subband. Experimental analysis found that the bifurcation angle has nothing to do with the damage of the branch vessels after the main branch stent is placed. The bifurcation angle greater than 50° is an independent predictor of MACE after stent extrusion for bifurcation lesions. Experimental results show that the proposed method has good performance in contrast enhancement, detail extraction, and information retention, and it improves the quality of the fusion image.
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Affiliation(s)
- Qin Li
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
| | - Yangyang Yuan
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
| | - Guangyu Song
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
| | - Yonghua Liu
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
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Liu Z, Bagnaninchi P, Yang Y. Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:983-996. [PMID: 34797763 DOI: 10.1109/tmi.2021.3129739] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
While Electrical Impedance Tomography (EIT) has found many biomedicine applications, better image quality is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper reports an impedance-optical dual-modal imaging framework that primarily targets at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could improve the image quality notably, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.
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Gómez-Cortés JC, Díaz-Carmona JJ, Padilla-Medina JA, Calderon AE, Gutiérrez AIB, Gutiérrez-López M, Prado-Olivarez J. Electrical Impedance Tomography Technical Contributions for Detection and 3D Geometric Localization of Breast Tumors: A Systematic Review. MICROMACHINES 2022; 13:mi13040496. [PMID: 35457801 PMCID: PMC9025021 DOI: 10.3390/mi13040496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 12/30/2022]
Abstract
Impedance measuring acquisition systems focused on breast tumor detection, as well as image processing techniques for 3D imaging, are reviewed in this paper in order to define potential opportunity areas for future research. The description of reported works using electrical impedance tomography (EIT)-based techniques and methodologies for 3D bioimpedance imaging of breast tissues with tumors is presented. The review is based on searching and analyzing related works reported in the most important research databases and is structured according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) parameters and statements. Nineteen papers reporting breast tumor detection and location using EIT were systematically selected and analyzed in this review. Clinical trials in the experimental stage did not produce results in most of analyzed proposals (about 80%), wherein statistical criteria comparison was not possible, such as specificity, sensitivity and predictive values. A 3D representation of bioimpedance is a potential tool for medical applications in malignant breast tumors detection being capable to estimate an ap-proximate the tumor volume and geometric location, in contrast with a tumor area computing capacity, but not the tumor extension depth, in a 2D representation.
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Li H, Chen R, Xu C, Liu B, Dong X, Fu F. Combing signal processing methods with algorithm priori information to produce synergetic improvements on continuous imaging of brain electrical impedance tomography. Sci Rep 2018; 8:10086. [PMID: 29973602 PMCID: PMC6031681 DOI: 10.1038/s41598-018-28284-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 06/18/2018] [Indexed: 11/10/2022] Open
Abstract
Dynamic electrical impedance tomography (EIT) promises to be a valuable technique for monitoring the development of brain injury. But in practical long-term monitoring, noise and interferences may cause insufficient image quality. To help unveil intracranial conductivity changes, signal processing methods were introduced to improve EIT data quality and algorithms were optimized to be more robust. However, gains for EIT image reconstruction can be significantly increased if we combine the two techniques properly. The basic idea is to apply the priori information in algorithm to help de-noise EIT data and use signal processing to optimize algorithm. First, we process EIT data with principal component analysis (PCA) and reconstruct an initial CT-EIT image. Then, as the priori that changes in scalp and skull domains are unwanted, we eliminate their corresponding boundary voltages from data sets. After the two-step denoising process, we finally re-select a local optimal regularization parameter and accomplish the reconstruction. To evaluate performances of the signal processing-priori information based reconstruction (SPR) method, we conducted simulation and in-vivo experiments. The results showed SPR could improve brain EIT image quality and recover the intracranial perturbations from certain bad measurements, while for some measurement data the generic reconstruction method failed.
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Affiliation(s)
- Haoting Li
- Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China
| | - Rongqing Chen
- Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China
| | - Canhua Xu
- Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China
| | - Benyuan Liu
- Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China
| | - Xiuzhen Dong
- Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China
| | - Feng Fu
- Faculty of Biomedical Engineering, Fourth Military Medical University, 169 West Changle Road, Xi'an, 710032, China.
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Gong B, Schullcke B, Krueger-Ziolek S, Zhang F, Mueller-Lisse U, Moeller K. Higher order total variation regularization for EIT reconstruction. Med Biol Eng Comput 2018; 56:1367-1378. [PMID: 29308547 DOI: 10.1007/s11517-017-1782-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 12/26/2017] [Indexed: 10/18/2022]
Abstract
Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.
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Affiliation(s)
- Bo Gong
- Institute of Technical Medicine, Furtwangen University, VS-Schwenningen, Germany. .,Department of Radiology, University of Munich, Munich, Germany.
| | - Benjamin Schullcke
- Institute of Technical Medicine, Furtwangen University, VS-Schwenningen, Germany.,Department of Radiology, University of Munich, Munich, Germany
| | - Sabine Krueger-Ziolek
- Institute of Technical Medicine, Furtwangen University, VS-Schwenningen, Germany.,Department of Radiology, University of Munich, Munich, Germany
| | - Fan Zhang
- Department of Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, People's Republic of China
| | | | - Knut Moeller
- Institute of Technical Medicine, Furtwangen University, VS-Schwenningen, Germany
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Murphy EK, Mahara A, Wu X, Halter RJ. Phantom experiments using soft-prior regularization EIT for breast cancer imaging. Physiol Meas 2017; 38:1262-1277. [DOI: 10.1088/1361-6579/aa691b] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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