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Liu C, Cui ZX, Jia S, Cheng J, Liu Y, Lin L, Hu Z, Xie T, Zhou Y, Zhu Y, Liang D, Zeng H, Wang H. DPP: deep phase prior for parallel imaging with wave encoding. Phys Med Biol 2024; 69:105013. [PMID: 38608645 DOI: 10.1088/1361-6560/ad3e5d] [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: 10/20/2023] [Accepted: 04/12/2024] [Indexed: 04/14/2024]
Abstract
Objective.In Magnetic Resonance (MR) parallel imaging with virtual channel-expanded Wave encoding, limitations are imposed on the ability to comprehensively and accurately characterize the background phase. These limitations are primarily attributed to the calibration process relying solely on center low-frequency Auto-Calibration Signals (ACS) data for calibration.Approach.To tackle the challenge of accurately estimating the background phase in wave encoding, a novel deep neural network model guided by deep phase priors is proposed with integrated virtual conjugate coil (VCC) extension. Concretely, within the proposed framework, the background phase is implicitly characterized by employing a carefully designed decoder convolutional neural network, leveraging the inherent characteristics of phase smoothness and compact support in the transformed domain. Furthermore, the proposed model with wave encoding benefits from additional priors, which incorporate transmission sparsity of the latent image and coil sensitivity smoothness.Main results.Ablation experiments were conducted to ascertain the proposed method's capability to implicitly represent CSM and the background phase. Subsequently, the superiority of the proposed method is demonstrated through confidence comparisons with competing methods, employing 4-fold and 5-fold acceleration experiments. In achieving 4-fold and 5-fold acceleration, the optimal quantitative metrics (PSNR/SSIM/NMSE) are 44.1359 dB/0.9863/0.0008 (4-fold) and 41.2074/0.9846/0.0017 (5-fold), respectively. Furthermore, the generalizability of the proposed method is further validated by conducting acceleration experiments with T1, T2, T2*, and various undersampling patterns. In addition, the DPP delivered much better performance than the conventional methods by exploring accelerated phase-sensitive SWI imaging. In SWI accelerated imaging, it also surpasses the optimal competing method in terms of (PSNR/SSIM/NMSE) with 0.096%/0.009%/0.0017%.Significance.The proposed method enables precise characterization of the background phase in the integrated VCC and wave encoding framework, supported via theoretical analysis and empirical findings. Our code is available at:https://github.com/sober235/DPP.
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Affiliation(s)
- Congcong Liu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Zhuo-Xu Cui
- Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Sen Jia
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Jing Cheng
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Yuanyuan Liu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Ling Lin
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Zhanqi Hu
- Department of Neurology, Shenzhen Children's Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Taofeng Xie
- Inner Mongolia University, Hohhot, Inner Mongolia, People's Republic of China
- Inner Mongolia Medical University, Hohhot, Inner Mongolia, People's Republic of China
| | - Yihang Zhou
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
| | - Yanjie Zhu
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
| | - Dong Liang
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Haifeng Wang
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, People's Republic of China
- Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen, People's Republic of China
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Wang H, Hu Z, Jiang D, Lin R, Zhao C, Zhao X, Zhou Y, Zhu Y, Zeng H, Liang D, Liao J, Li Z. Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning. AJNR Am J Neuroradiol 2023; 44:1373-1383. [PMID: 38081677 PMCID: PMC10714846 DOI: 10.3174/ajnr.a8053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 10/03/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND AND PURPOSE Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatric patients to have positive outcomes. The purpose of this study was to predict the effectiveness of antiseizure medication treatment in children with tuberous sclerosis complex-related epilepsy. MATERIALS AND METHODS We conducted a retrospective study involving 300 children with tuberous sclerosis complex-related epilepsy. The study included the analysis of clinical data and T2WI and FLAIR images. The clinical data consisted of sex, age of onset, age at imaging, infantile spasms, and antiseizure medication numbers. To forecast antiseizure medication treatment, we developed a multitechnique deep learning method called WAE-Net. This method used multicontrast MR imaging and clinical data. The T2WI and FLAIR images were combined as FLAIR3 to enhance the contrast between tuberous sclerosis complex lesions and normal brain tissues. We trained a clinical data-based model using a fully connected network with the above-mentioned variables. After that, a weighted-average ensemble network built from the ResNet3D architecture was created as the final model. RESULTS The experiments had shown that age of onset, age at imaging, infantile spasms, and antiseizure medication numbers were significantly different between the 2 drug-treatment outcomes (P < .05). The hybrid technique of FLAIR3 could accurately localize tuberous sclerosis complex lesions, and the proposed method achieved the best performance (area under the curve = 0.908 and accuracy of 0.847) in the testing cohort among the compared methods. CONCLUSIONS The proposed method could predict antiseizure medication treatment of children with rare tuberous sclerosis complex-related epilepsy and could be a strong baseline for future studies.
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Affiliation(s)
- Haifeng Wang
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Zhanqi Hu
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
- Department of Pediatric Neurology (Z.H.), Boston Children's Hospital, Boston, Massachusetts
| | - Dian Jiang
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Rongbo Lin
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Cailei Zhao
- Department of Radiology (C.Z., H.Z.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Xia Zhao
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Yihang Zhou
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Research Department (Y. Zhou), Hong Kong Sanatorium and Hospital, Hong Kong, China
| | - Yanjie Zhu
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Paul C. Lauterbur Research Center for Biomedical Imaging (Y.Zhu, D.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Hongwu Zeng
- Department of Radiology (C.Z., H.Z.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Dong Liang
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Paul C. Lauterbur Research Center for Biomedical Imaging (Y.Zhu, D.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Jianxiang Liao
- Department of Neurology (Z.H., R.L., X.Z., J.L.), Shenzhen Children's Hospital, Shenzhen, Guangdong, China
| | - Zhicheng Li
- From the Research Center for Medical Artificial Intelligence (H.W., D.J., Y. Zhou, D.L., Z.L.), Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen College of Advanced Technology (H.W., D.J., Y.Zhu, D.L., Z.L.), University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
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