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Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method

Chinese Physics B 2018年第8期27卷 645-654页

作者： Lin Chen Zhi-Wei Zheng Li-Jun Bao Jin-Sheng Fang Tian-He Yang Shu-Hui Cai Cong-Bo Cai Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic ResonanceXiamen UniversityXiamen 361005China Magnetic Resonance Center Zhongshan HospitalMedical College of Xiamen UniversityXiamen 361004China Department of Communication Engineering Xiamen UniversityXiamen 361005China

An ill-posed inverse problem in quantitative susceptibility mapping （QSM） is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. However, in clinical diagnosis, it is necessary to reconstruct a susceptibility map efficiently with an appropriate method. Here, a modified QSM reconstruction method called weighted total variation using split Bregman （WTVSB） is proposed. It reconstructs the susceptibility map with fast computational speed and effective artifact suppression by incorporating noise-suppressed data weighting with split Bregman iteration. The noise-suppressed data weighting is determined using the Laplacian of the calculated local field, which can prevent the noise and errors in field maps from spreading into the susceptibility inversion. The split Bregman iteration accelerates the solution of the Ll-regularized reconstruction model by utilizing a preconditioned conjugate gradient solver. In an experiment, the proposed reconstruction method is compared with truncated k-space division （TKD）, morphology enabled dipole inversion （MEDI）, total variation using the split Bregman （TVSB） method for numerical simulation, phantom and in vivo human brain data evaluated by root mean square error and mean structure similarity. Experimental results demonstrate that our proposed method can achieve better balance between accuracy and efficiency of QSM reconstruction than conventional methods, and thus facilitating clinical applications of QSM.

关键词： quantitative susceptibility mapping ill-posed inverse problem noise-suppressed data weighting split Bregman iteration

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MRI Evaluation of Lateral Geniculate Body in Normal Aging Brain Using quantitative susceptibility mapping

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Chinese Medical Sciences Journal 2015年第1期30卷 34-36页

作者： Meng-qi Liu Zhi-ye Chen Xiang-bing Bian Meng-yu Liu Shen-yuan Yu Lin Ma Department of Radiology Chinese People's Liberation Army General Hospital

Objective To investigate the changes of lateral geniculate body （LGB） in the normal aging brain using quantitative susceptibility mapping （QSM） technique. Methods Magnetic resonance （MR） phase and magnitude images were acquired from enhanced gradient echo T2 star weighted angiography sequence with 16 echoes on 3.0T MR system using the head coil with 32 channels. Morphology Enabled Dipole Inversion （MEDI） method was applied for QSM, and the susceptibility value of LGB was measured by region of interest （ROI） drawn manually on three orthogonal planes. Results LGB of the middle-aged group had a higher susceptibility value （0.16±0.05 ppm） than that of the youth group （0.12±0.05 pprn） and elderly group （0.13±0.03 ppm）（all P〈0.05）. Partial correlation analysis demonstrated that there was significantly positive correlation between susceptibility value and age in the youth group （r=0.71, P〈0.05）. Conclusion LGB could clearly be identified on QSM in the brain in vivo.

关键词： lateral geniculate body quantitative susceptibility mapping magnetic resonance imaging aging

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Deep Learning‑Assisted quantitative susceptibility mapping as a Tool for Grading and Molecular Subtyping of Gliomas

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Phenomics 2023年第3期3卷 243-254页

作者： Wenting Rui Shengjie Zhang Huidong Shi Yaru Sheng Fengping Zhu YiDi Yao Xiang Chen Haixia Cheng Yong Zhang Ababikere Aili Zhenwei Yao Xiao‑Yong Zhang Yan Ren Department of Radiology Huashan HospitalFudan UniversityMid 12 Wulumuqi RoadShanghai 200040China Institute of Science and Technology for Brain‑Inspired Intelligence Fudan UniversityShanghai 200433China MOE Key Laboratory of Computational Neuroscience and Brain‑Inspired Intelligence MOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Department of Neurosurgery Huashan HospitalFudan UniversityShanghai 200040China Department of Neuropathology Huashan HospitalFudan UniversityShanghai 200040China GE Healthcare MR ResearchHuatuo RoadShanghai 201203China Department of Radiology Kuqa County People’s HospitalXinjiang 842000China

This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular ***-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this *** and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)*** segmentation was performed manually using Insight Toolkit-SNAP program(***).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI *** cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:*** performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss.

关键词： quantitative susceptibility mapping Glioma classification Isocitrate dehydrogenase Alpha thalassemia/mental retardation syndrome X-linked gene Deep learning

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The neuroimaging of neurodegenerative and vascular disease in the secondary prevention of cognitive decline

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Neural Regeneration Research 2021年第8期16卷 1490-1499页

作者： Morgan J.Schaeffer Leona Chan Philip A.Barber Department of Clinical Neurosciences University of CalgaryCalgaryABCanada

Structural brain changes indicative of dementia occur up to 20 years before the onset of clinical symptoms. Efforts to modify the disease process after the onset of cognitive symptoms have been unsuccessful in recent years. Thus, future trials must begin during the preclinical phases of the disease before symptom onset. Age related cognitive decline is often the result of two coexisting brain pathologies: Alzheimer's disease(amyloid, tau, and neurodegeneration) and vascular disease. This review article highlights some of the common neuroimaging techniques used to visualize the accumulation of neurodegenerative and vascular pathologies during the preclinical stages of dementia such as structural magnetic resonance imaging, positron emission tomography, and white matter hyperintensities. We also describe some emerging neuroimaging techniques such as arterial spin labeling, diffusion tensor imaging, and quantitative susceptibility mapping. Recent literature suggests that structural imaging may be the most sensitive and cost-effective marker to detect cognitive decline, while molecular positron emission tomography is primarily useful for detecting disease specific pathology later in the disease process. Currently, the presence of vascular disease on magnetic resonance imaging provides a potential target for optimizing vascular risk reduction strategies, and the presence of vascular disease may be useful when combined with molecular and metabolic markers of neurodegeneration for identifying the risk of cognitive impairment.

关键词： amyloid arterial spin labeling cognitive decline dementia imaging magnetic resonance imaging positron emission tomography quantitative susceptibility mapping tau vascular disease white matter hyperintensities

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A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson’s disease: a brain radiomics study

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Neural Regeneration Research 2022年第12期17卷 2743-2749页

作者： Xiao-Jun Guan Tao Guo Cheng Zhou Ting Gao Jing-Jing Wu Victor Han Steven Cao Hong-Jiang Wei Yu-Yao Zhang Min Xuan Quan-Quan Gu Pei-Yu Huang Chun-Lei Liu Jia-Li Pu Bao-Rong Zhang Feng Cui Xiao-Jun Xu Min-Ming Zhang Department of Radiology the Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang ProvinceChina Department of Neurology the Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang ProvinceChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCAUSA Institute for Medical Imaging Technology School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Helen Wills Neuroscience Institute University of CaliforniaBerkeleyCAUSA Department of Radiology Hangzhou Hospital of Traditional Chinese MedicineHangzhouZhejiang ProvinceChina

Brain radiomics can reflect the characteristics of brain ***,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous *** this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August *** to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation *** extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training *** informative features so identified were then used to construct a diagnostic model for *** constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training *** the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,*** accuracies for predicting tremor-dominant and non-tremor-dominant PD w

关键词： diagnosis imaging biomarker iron magnetic resonance imaging neuroimaging Parkinson’s disease quantitative susceptibility mapping R2*mapping radiomics T1-weighted imaging

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IMPROVED HARMONIC INCOMPATIBILITY REMOVAL FOR susceptibility mapping VIA REDUCTION OF BASIS MISMATCH

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Journal of Computational Mathematics 2022年第6期40卷 913-935页

作者： Chenglong Bao Jianfeng Cai Jae Kyu Choi Bin Dong Ke Wei Yau Mathematical Sciences Center Tsinghua UniversityBeijing 100084China Department of Mathematics The Hong Kong University of Science and TechnologyHongkongChina School of Mathematical Sciences Tongji UniversityShanghai 200092China Beijing International Center for Mathematical Research and Laboratory for Biomedical Image Analysis Beijing Institute of Big Data Research Peking UniversityBeijing 100871China School of Data Science Fudan UniversityShanghai 200433China

In quantitative susceptibility mapping(QSM),the background field removal is an essential data acquisition step because it has a significant effect on the restoration quality by generating a harmonic incompatibility in the measured local field *** though the sparsity based first generation harmonic incompatibility removal(1GHIRE)model has achieved the performance gain over the traditional approaches,the 1GHIRE model has to be further improved as there is a basis mismatch underlying in numerically solving Poisson’s equation for the background *** this paper,we propose the second generation harmonic incompatibility removal(2GHIRE)model to reduce a basis mismatch,inspired by the balanced approach in the tight frame based image *** results shows the superiority of the proposed 2GHIRE model both in the restoration qualities and the computational efficiency.

关键词： quantitative susceptibility mapping Magnetic resonance imaging Deconvolution Partial differential equation Harmonic incompatibility removal (Tight)wavelet frames Sparse approximation

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