20 results on '"Cao, Chentao"'
Search Results
2. Synthesizing PET images from high‐field and ultra‐high‐field MR images using joint diffusion attention model.
- Author
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Xie, Taofeng, Cao, Chentao, Cui, Zhuo‐xu, Guo, Yu, Wu, Caiying, Wang, Xuemei, Li, Qingneng, Hu, Zhanli, Sun, Tao, Sang, Ziru, Zhou, Yihang, Zhu, Yanjie, Liang, Dong, Jin, Qiyu, Zeng, Hongwu, Chen, Guoqing, and Wang, Haifeng
- Subjects
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POSITRON emission tomography , *MAGNETIC resonance imaging , *DISTRIBUTION (Probability theory) , *ALZHEIMER'S disease , *GAUSSIAN distribution - Abstract
Background: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) stand as pivotal diagnostic tools for brain disorders, offering the potential for mutually enriching disease diagnostic perspectives. However, the costs associated with PET scans and the inherent radioactivity have limited the widespread application of PET. Furthermore, it is noteworthy to highlight the promising potential of high‐field and ultra‐high‐field neuroimaging in cognitive neuroscience research and clinical practice. With the enhancement of MRI resolution, a related question arises: can high‐resolution MRI improve the quality of PET images? Purpose: This study aims to enhance the quality of synthesized PET images by leveraging the superior resolution capabilities provided by high‐field and ultra‐high‐field MRI. Methods: From a statistical perspective, the joint probability distribution is considered the most direct and fundamental approach for representing the correlation between PET and MRI. In this study, we proposed a novel model, the joint diffusion attention model, namely, the joint diffusion attention model (JDAM), which primarily focuses on learning information about the joint probability distribution. JDAM consists of two primary processes: the diffusion process and the sampling process. During the diffusion process, PET gradually transforms into a Gaussian noise distribution by adding Gaussian noise, while MRI remains fixed. The central objective of the diffusion process is to learn the gradient of the logarithm of the joint probability distribution between MRI and noise PET. The sampling process operates as a predictor‐corrector. The predictor initiates a reverse diffusion process, and the corrector applies Langevin dynamics. Results: Experimental results from the publicly available Alzheimer's Disease Neuroimaging Initiative dataset highlight the effectiveness of the proposed model compared to state‐of‐the‐art (SOTA) models such as Pix2pix and CycleGAN. Significantly, synthetic PET images guided by ultra‐high‐field MRI exhibit marked improvements in signal‐to‐noise characteristics when contrasted with those generated from high‐field MRI data. These results have been endorsed by medical experts, who consider the PET images synthesized through JDAM to possess scientific merit. This endorsement is based on their symmetrical features and precise representation of regions displaying hypometabolism, a hallmark of Alzheimer's disease. Conclusions: This study establishes the feasibility of generating PET images from MRI. Synthesis of PET by JDAM significantly enhances image quality compared to SOTA models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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3. Annihilation‐Net: Learned annihilation relation for dynamic MR imaging.
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Cao, Chentao, Cui, Zhuo‐Xu, Zhu, Qingyong, Liu, Congcong, Liang, Dong, and Zhu, Yanjie
- Subjects
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MAGNETIC resonance imaging , *CONTRAST-enhanced magnetic resonance imaging , *COMPRESSED sensing , *DEEP learning , *MATRIX multiplications , *REGULARIZATION parameter , *MAGNETIC resonance - Abstract
Background: Deep learning methods driven by the low‐rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. The effectiveness of existing methods lies mainly in their ability to capture interframe relationships using network modules, which are lack interpretability. Purpose: This study aims to design an interpretable methodology for modeling interframe relationships using convolutiona networks, namely Annihilation‐Net and use it for accelerating dynamic MRI. Methods: Based on the equivalence between Hankel matrix product and convolution, we utilize convolutional networks to learn the null space transform for characterizing low‐rankness. We employ low‐rankness to represent interframe correlations in dynamic MR imaging, while combining with sparse constraints in the compressed sensing framework. The corresponding optimization problem is solved in an iterative form with the semi‐quadratic splitting method (HQS). The iterative steps are unrolled into a network, dubbed Annihilation‐Net. All the regularization parameters and null space transforms are set as learnable in the Annihilation‐Net. Results: Experiments on the cardiac cine dataset show that the proposed model outperforms other competing methods both quantitatively and qualitatively. The training set and test set have 800 and 118 images, respectively. Conclusions: The proposed Annihilation‐Net improves the reconstruction quality of accelerated dynamic MRI with better interpretability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. Accelerated submillimeter wave‐encoded magnetic resonance imaging via deep untrained neural network.
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Liu, Congcong, Cui, Zhuo‐Xu, Jia, Sen, Cheng, Jing, Cao, Chentao, Guo, Yifan, Zhu, Yanjie, Liang, Dong, and Wang, Haifeng
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MAGNETIC resonance imaging ,MISSING data (Statistics) ,DEEP learning - Abstract
Background: Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto‐calibration signals (ACS) signal acquisition and is time‐consuming, while the latter requires a large amount of training data. Purpose: To tackle the above issues, an untrained neural network (UNN) model incorporating wave‐encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS‐ and training data‐free. Methods: Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k‐space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k‐space linear predictability. And then, the iterative paradigm to recover the full k‐space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six‐fold acceleration, respectively. Results: In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/per slice. Conclusions: The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time‐consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computational efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
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Cui, Zhuo-Xu, Cao, Chentao, Cheng, Jing, Jia, Sen, Zheng, Hairong, Liang, Dong, and Zhu, Yanjie
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Diffusion models are a leading method for image generation and have been successfully applied in magnetic resonance imaging (MRI) reconstruction. Current diffusion-based reconstruction methods rely on coil sensitivity maps (CSM) to reconstruct multi-coil data. However, it is difficult to accurately estimate CSMs in practice use, resulting in degradation of the reconstruction quality. To address this issue, we propose a self-consistency-driven diffusion model inspired by the iterative self-consistent parallel imaging (SPIRiT), namely SPIRiT-Diffusion. Specifically, the iterative solver of the self-consistent term in SPIRiT is utilized to design a novel stochastic differential equation (SDE) for diffusion process. Then $\textit{k}$-space data can be interpolated directly during the reverse diffusion process, instead of using CSM to separate and combine individual coil images. This method indicates that the optimization model can be used to design SDE in diffusion models, driving the diffusion process strongly conforming with the physics involved in the optimization model, dubbed model-driven diffusion. The proposed SPIRiT-Diffusion method was evaluated on a 3D joint Intracranial and Carotid Vessel Wall imaging dataset. The results demonstrate that it outperforms the CSM-based reconstruction methods, and achieves high reconstruction quality at a high acceleration rate of 10.
- Published
- 2023
6. SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
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Cao, Chentao, Cui, Zhuo-Xu, Cheng, Jing, Jia, Sen, Zheng, Hairong, Liang, Dong, and Zhu, Yanjie
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FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve superior reconstruction results on the joint Intracranial and Carotid Vessel Wall imaging dataset., submitted to ISMRM
- Published
- 2022
7. PS-Net: Learned Partially Separable Model for Dynamic MR Imaging
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Cao, Chentao, Cui, Zhuo-Xu, Zhu, Qingyong, Liu, Congcong, Liang, Dong, and Zhu, Yanjie
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Vision and Pattern Recognition (cs.CV) ,Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Machine Learning (cs.LG) - Abstract
Deep learning methods driven by the low-rank regularization have achieved attractive performance in dynamic magnetic resonance (MR) imaging. However, most of these methods represent low-rank prior by hand-crafted nuclear norm, which cannot accurately approximate the low-rank prior over the entire dataset through a fixed regularization parameter. In this paper, we propose a learned low-rank method for dynamic MR imaging. In particular, we unrolled the semi-quadratic splitting method (HQS) algorithm for the partially separable (PS) model to a network, in which the low-rank is adaptively characterized by a learnable null-space transform. Experiments on the cardiac cine dataset show that the proposed model outperforms the state-of-the-art compressed sensing (CS) methods and existing deep learning methods both quantitatively and qualitatively., journal
- Published
- 2022
8. Enhanced microwave absorption of BaTiO3-based ferroelectric/ferromagnetic nanocomposite
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Ma, Zhi, Cao, Chentao, Yuan, Jing, Liu, Qingfang, and Wang, Jianbo
- Published
- 2012
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9. Investigation of InGaAs/InAlAs superlattices for quantum cascade laser structures grown by MOCVD.
- Author
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Cao, Chentao, Chen, Bingqi, Chen, Hongtai, and Fang, Yulong
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- 2023
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10. Comparative study of microwave absorption in Ni/SiO2 and SiO2/Ni core-shell structures
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Ma, Zhi, Yuan, Jing, Cao, Chentao, Liu, Qingfang, and Wang, Jianbo
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Condensed Matter - Materials Science ,Materials Science (cond-mat.mtrl-sci) ,FOS: Physical sciences ,Physics::Classical Physics - Abstract
Ni coated SiO2 and SiO2 coated Ni composite particles with core-shell structures were designed, prepared and their microwave absorption properties were characterized. The comparison study of the shell effect on the effective electromagnetic parameters reveals that the effective permittivity/permeability was crucially determined by the percent and the intrinsic electromagnetic parameters of the component materials regardless of various core/shell structures. Both of the composite core-shell structures could have good microwave absorption properties. Investigation of the input impedance indicates that, good microwave absorption performances are a consequence of proper electromagnetic impedance matches when the effective electromagnetic parameters were modulated., 31 pages, 9 figures, submitted to materials chemistry and physics
- Published
- 2011
11. Designed synthesis and magnetic properties of Co hierarchical nanostructures
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Ma, Zhi, Cao, Chentao, Liu, Qingfang, Wang, Zhenkun, and Wang, Jianbo
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- 2011
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12. Synthesis and characterization of Fe/C core–shell nanoparticles
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Cao, Chentao, Ma, Zhi, Ma, Chuansheng, Pan, Weiwei, Liu, Qingfang, and Wang, Jianbo
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NANOPARTICLE synthesis , *MICROENCAPSULATION , *METAL nanoparticles , *CHEMICAL reduction , *ANNEALING of crystals , *TEMPERATURE effect , *X-ray diffraction , *AMORPHOUS substances - Abstract
Abstract: Carbon-encapsulated Fe nanoparticles have been successfully synthesized by a two-step procedure method of hydrothermal and subsequent thermal reduction process. The introduction of hydrogen atmosphere helps to obtain pure α-Fe phase and the lower annealing temperature (at 550°C). The morphological characterization indicates that the nanoparticle presents a quasi-spherical shaped, core–shell structure with diameter ranged from 10 to 20nm and a carbon shell of about 4nm. The results of X-ray diffraction and Raman spectrum demonstrate that carbon in the as-synthesized product is in amorphous phase. Magnetic measurements reveal the ferromagnetic behavior of the product. [Copyright &y& Elsevier]
- Published
- 2012
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13. Enhanced microwave absorption of BaTiO3-based ferroelectric/ferromagnetic nanocomposite
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Ma, Zhi, Cao, Chentao, Yuan, Jing, Liu, Qingfang, and Wang, Jianbo
- Subjects
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BARIUM titanate , *MICROWAVES , *ABSORPTION , *FERROELECTRICITY , *FERROMAGNETISM , *NANOCOMPOSITE materials , *MOLECULAR structure , *MAGNETIC properties of metals , *DIELECTRIC loss - Abstract
Abstract: Morphology, crystal structure, magnetic and microwave absorption properties of BaTiO3-based ferromagnetic/ferroelectric composites were investigated in this research. It was evident that Niph name="sbnd" />P/BaTiO3 composite was a narrowband absorber, whereas carbonyl iron/BaTiO3 composite samples showed broadband absorption characteristics. For the carbonyl iron/BaTiO3 composite, a reflection loss exceeding −20dB was obtained in the frequency range of 3.8–5.8GHz with an absorber thickness of 2.3–3.3mm. An optimal RL of −46dB was found at 4.7GHz for an absorber thickness of 2.8mm. The effective absorption bandwidth with RL<−10dB was extended and reached 16GHz (2–18GHz). Moreover, the intrinsic reasons for microwave absorption of the composites, the dielectric loss and magnetic loss mechanics were also investigated. [Copyright &y& Elsevier]
- Published
- 2012
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14. Analyses on multiple resonance behaviors and microwave reflection loss in magnetic Co microflowers.
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Ma, Zhi, Liu, Qingfang, Yuan, Jing, Wang, Zhenkun, Cao, Chentao, and Wang, Jianbo
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- 2012
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15. Attractive microwave absorption and the impedance match effect in zinc oxide and carbonyl iron composite
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Ma, Zhi, Zhang, Yi, Cao, ChenTao, Yuan, Jing, Liu, QingFang, and Wang, JianBo
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ZINC oxide , *MICROWAVES , *ABSORPTION , *IMPEDANCE matching , *CARBONYL compounds , *IRON compounds , *METALLIC composites - Abstract
Abstract: The flower-like ZnO and ZnO/carbonyl-iron composite have been prepared by a sonochemical route and ball-milling process, respectively. For ZnO/carbonyl-iron composite, a reflection loss (RL) exceeding −20dB was obtained in a broad frequency range of 8.4–17.9GHz with a thin thickness of 1.2–2.3mm. An optimal RL of −61dB was found at 11.7GHz for an absorber thickness of 1.91mm. It is demonstrated that the attractive microwave-absorption properties are a consequence of a proper electro-magnetic impedance match and geometrical cancellation at the air–material interface. In addition, an impedance mismatch function was proposed, which provides an effective method to determine the microwave absorbing properties from the intrinsic materials constants. The calculated value of matching frequency and thickness is well consistent with the experimental data. The method also provides a simple theoretical graphic aid for determining the absorption characteristics and the location of the matching conditions in the frequency domain. [Copyright &y& Elsevier]
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- 2011
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16. A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection.
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Wang W, Cui ZX, Cheng G, Cao C, Xu X, Liu Z, Wang H, Qi Y, Liang D, and Zhu Y
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- Humans, Brain diagnostic imaging, Neural Networks, Computer, Magnetic Resonance Imaging methods, Brain Neoplasms diagnostic imaging, Image Interpretation, Computer-Assisted methods, Algorithms
- Abstract
Accuratedetection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.
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- 2024
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17. High-Frequency Space Diffusion Model for Accelerated MRI.
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Cao C, Cui ZX, Wang Y, Liu S, Chen T, Zheng H, Liang D, and Zhu Y
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- Humans, Diffusion Magnetic Resonance Imaging methods, Image Processing, Computer-Assisted methods, Algorithms, Magnetic Resonance Imaging methods, Brain diagnostic imaging
- Abstract
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation. It can serve as a deep generative prior to solving the inverse problem in magnetic resonance (MR) reconstruction. However, low-frequency regions of k -space data are typically fully sampled in fast MR imaging, while existing diffusion models are performed throughout the entire image or k -space, inevitably introducing uncertainty in the reconstruction of low-frequency regions. Additionally, existing diffusion models often demand substantial iterations to converge, resulting in time-consuming reconstructions. To address these challenges, we propose a novel SDE tailored specifically for MR reconstruction with the diffusion process in high-frequency space (referred to as HFS-SDE). This approach ensures determinism in the fully sampled low-frequency regions and accelerates the sampling procedure of reverse diffusion. Experiments conducted on the publicly available fastMRI dataset demonstrate that the proposed HFS-SDE method outperforms traditional parallel imaging methods, supervised deep learning, and existing diffusion models in terms of reconstruction accuracy and stability. The fast convergence properties are also confirmed through theoretical and experimental validation. Our code and weights are available at https://github.com/Aboriginer/HFS-SDE.
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- 2024
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18. PEARL: Cascaded Self-supervised Cross-fusion Learning For Parallel MRI Acceleration.
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Zhu Q, Liu B, Cui ZX, Cao C, Yan X, Liu Y, Cheng J, Zhou Y, Zhu Y, Wang H, Zeng H, and Liang D
- Abstract
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, eliminating the explicit training procedure but often struggling to remove noise and artifacts under significant degradation. This work introduces a novel self-supervised accelerated parallel MRI approach called PEARL, leveraging a multiple-stream joint deep decoder with two cross-fusion schemes to accurately reconstruct one or more target images from compressively sampled k-space. Each stream comprises cascaded cross-fusion sub-block networks (SBNs) that sequentially perform combined upsampling, 2D convolution, joint attention, ReLU activation and batch normalization (BN). Among them, combined upsampling and joint attention facilitate mutual learning between multiple-stream networks by integrating multi-parameter priors in both additive and multiplicative manners. Long-range unified skip connections within SBNs ensure effective information propagation between distant cross-fusion layers. Additionally, incorporating dual-normalized edge-orientation similarity regularization into the training loss enhances detail reconstruction and prevents overfitting. Experimental results consistently demonstrate that PEARL outperforms the existing state-of-the-art (SOTA) self-supervised AMRI technologies in various MRI cases. Notably, 5-fold ∼ 6-fold accelerated acquisition yields a 1 % ∼ 2 % improvement in SSIM
ROI and a 3 % ∼ 6 % improvement in PSNRROI , along with a significant 15 % ∼ 20 % reduction in RLNEROI .- Published
- 2023
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19. Accelerating Magnetic Resonance T 1ρ Mapping Using Simultaneously Spatial Patch-Based and Parametric Group-Based Low-Rank Tensors (SMART).
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Liu Y, Liang D, Cui ZX, Yang Y, Cao C, Zhu Q, Cheng J, Shi C, Wang H, and Zhu Y
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- Prospective Studies, Brain diagnostic imaging, Magnetic Resonance Spectroscopy, Image Processing, Computer-Assisted methods, Algorithms, Magnetic Resonance Imaging methods
- Abstract
Quantitative magnetic resonance (MR) [Formula: see text] mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor models have been employed and demonstrated exemplary performance in accelerating MR [Formula: see text] mapping. This study proposes a novel method that uses spatial patch-based and parametric group-based low-rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in [Formula: see text] mapping. The parametric group-based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results demonstrated that the proposed method achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and three-dimensional acquisitions, respectively, with more accurate reconstructed images and maps than several state-of-the-art methods. Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR [Formula: see text] imaging.
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- 2023
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20. Accelerated cardiac cine MRI using spatiotemporal correlation-based hybrid plug-and-play priors (SEABUS).
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Zhu Q, Liu B, Cui ZX, Cheng J, Cao C, Liu Y, Liang D, and Zhu Y
- Subjects
- Artifacts, Heart diagnostic imaging, Algorithms, Image Processing, Computer-Assisted methods, Magnetic Resonance Imaging, Cine methods, Magnetic Resonance Imaging methods
- Abstract
Objective . The plug-and-play prior (P
3 ) can be flexibly coupled with multiple iterative optimizations, which has been successfully applied to the inverse problems of medical imaging. In this work, for accelerated cardiac cine magnetic resonance imaging (CC-MRI), the Spatiotemporal corrElAtion-based hyBrid plUg-and-play priorS (SEABUS) integrating a local P3 and a nonlocal P3 are introduced. Approach . Specifically, the local P3 enforces pixelwise edge-orientation consistency by conducting reference frame guided multiscale orientation projection on a subset containing a few adjacent frames; the nonlocal P3 constrains the cubewise anatomic-structure similarity by performing cube matching and 4D filtering (CM4D) on all frames. By using effectively a composite splitting algorithm (CSA), SEABUS is incorporated into a fast iterative shrinkage-thresholding algorithm and a new accelerated CC-MRI approach named SEABUS-FCSA is proposed. Main results . The experiment and algorithm analysis demonstrate the efficiency and potential of the proposed SEABUS-FCSA approach, which has the best performance in terms of reducing aliasing artifacts and capturing dynamic features in comparison with several state-of-the-art accelerated CC-MRI technologies. Significance . Our approach aims to propose a new hybrid P3 based iterative algorithm, which is not only used to improve the quality of accelerated cardiac cine imaging but also extend the FCSA methodology., (© 2022 Institute of Physics and Engineering in Medicine.)- Published
- 2022
- Full Text
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