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He C, Guan X, Zhang W, Li J, Liu C, Wei H, Xu X, Zhang Y. Correction to: Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological‑consistent iron‑rich deep brain nucleus subregion identification. Brain Struct Funct 2023; 228:697. [PMID: 36692696 DOI: 10.1007/s00429-023-02609-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Chenyu He
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Xiaojun Guan
- Department of Radiology of The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Weimin Zhang
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Chunlei Liu
- Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, 94720, United States
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200030, China
| | - Xiaojun Xu
- Department of Radiology of The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China. .,Shanghai Engineering Research Center of Intelligent Vision and Imaging, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China.
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He C, Guan X, Zhang W, Li J, Liu C, Wei H, Xu X, Zhang Y. Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification. Brain Struct Funct 2022:10.1007/s00429-022-02547-1. [PMID: 36038737 DOI: 10.1007/s00429-022-02547-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 07/27/2022] [Indexed: 01/25/2023]
Abstract
Iron-rich deep brain nuclei (DBN) of the human brain are involved in various motoric, emotional and cognitive brain functions. The abnormal iron alterations in the DBN are closely associated with multiple neurological and psychiatric diseases. Quantitative susceptibility mapping (QSM) provides the spatial distribution of the magnetic susceptibility of human brain tissues. Compared to traditional structural imaging, QSM provides superiority for imaging the iron-rich DBN owing to the susceptibility difference existing between brain tissues. In this study, we constructed a Montreal Neurological Institute (MNI) space unbiased QSM human brain atlas via group-wise registration from 100 healthy subjects aged 19-29 years. The atlas construction process was guided by hybrid images that were fused from multi-modal magnetic resonance images (MRI). We named it as Multi-modal-fused magnetic Susceptibility (MuSus-100) atlas. The high-quality susceptibility atlas provides extraordinary image contrast between iron-rich DBN with their surroundings. Parcellation maps of DBN and their subregions that are highly related to neurological and psychiatric pathology were then manually labeled based on the atlas set with the assistance of an image border-enhancement process. Especially, the bilateral thalamus was delineated into 64 detailed subregions referring to the Schaltenbrand-Wahren stereotactic atlas. To our best knowledge, the histological-consistent thalamic nucleus parcellation map is well defined for the first time in the MNI space. Compared with existing atlases that emphasizing DBN parcellation, the newly proposed atlas outperforms on the task of atlas-guided individual brain image DBN segmentation both in accuracy and robustness. Moreover, we applied the proposed DBN parcellation map to conduct detailed identification of the pathology-related iron content alterations in subcortical nuclei for Parkinson's Disease (PD) patients. We envision that the MuSus-100 atlas can play a crucial role in improving the accuracy of DBN segmentation for the research of neurological and psychiatric disease progress and also be helpful for target planning in deep brain stimulation surgery.
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Affiliation(s)
- Chenyu He
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Xiaojun Guan
- Department of Radiology of The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Weimin Zhang
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Jun Li
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China
| | - Chunlei Liu
- Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, 94720, United States
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200030, China
| | - Xiaojun Xu
- Department of Radiology of The Second Affiliated Hospital, Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Yuyao Zhang
- School of Information Science and Technology, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China. .,Shanghai Engineering Research Center of Intelligent Vision and Imaging, ShanghaiTech University, 393 Huaxia Road, Shanghai, 201210, China.
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Xiao Y, Lau JC, Hemachandra D, Gilmore G, Khan AR, Peters TM. Image Guidance in Deep Brain Stimulation Surgery to Treat Parkinson's Disease: A Comprehensive Review. IEEE Trans Biomed Eng 2020; 68:1024-1033. [PMID: 32746050 DOI: 10.1109/tbme.2020.3006765] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Deep brain stimulation (DBS) is an effective therapy as an alternative to pharmaceutical treatments for Parkinson's disease (PD). Aside from factors such as instrumentation, treatment plans, and surgical protocols, the success of the procedure depends heavily on the accurate placement of the electrode within the optimal therapeutic targets while avoiding vital structures that can cause surgical complications and adverse neurologic effects. Although specific surgical techniques for DBS can vary, interventional guidance with medical imaging has greatly contributed to the development, outcomes, and safety of the procedure. With rapid development in novel imaging techniques, computational methods, and surgical navigation software, as well as growing insights into the disease and mechanism of action of DBS, modern image guidance is expected to further enhance the capacity and efficacy of the procedure in treating PD. This article surveys the state-of-the-art techniques in image-guided DBS surgery to treat PD, and discusses their benefits and drawbacks, as well as future directions on the topic.
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Dong P, Guo Y, Gao Y, Liang P, Shi Y, Wu G. Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:3061-3072. [PMID: 31502994 DOI: 10.1109/tnnls.2019.2935184] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the study of imaging biomarkers of neurodegenerative diseases. Although multi-atlas segmentation (MAS) has achieved many successes in the medical imaging area, this approach encounters limitations in segmenting anatomical structures associated with poor image contrast. To address this issue, we propose a new MAS method that uses a hypergraph learning framework to model the complex subject-within and subject-to-atlas image voxel relationships and propagate the label on the atlas image to the target subject image. To alleviate the low-image contrast issue, we propose two strategies equipped with our hypergraph learning framework. First, we use a hierarchical strategy that exploits high-level context features for hypergraph construction. Because the context features are computed on the tentatively estimated probability maps, we can ultimately turn the hypergraph learning into a hierarchical model. Second, instead of only propagating the labels from the atlas images to the target subject image, we use a dynamic label propagation strategy that can gradually use increasing reliably identified labels from the subject image to aid in predicting the labels on the difficult-to-label subject image voxels. Compared with the state-of-the-art label fusion methods, our results show that the hierarchical hypergraph learning framework can substantially improve the robustness and accuracy in the segmentation of anatomical brain structures with low image contrast from magnetic resonance (MR) images.
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Xiao Y, Lau JC, Anderson T, DeKraker J, Collins DL, Peters T, Khan AR. An accurate registration of the BigBrain dataset with the MNI PD25 and ICBM152 atlases. Sci Data 2019; 6:210. [PMID: 31624250 PMCID: PMC6797784 DOI: 10.1038/s41597-019-0217-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 09/04/2019] [Indexed: 01/05/2023] Open
Abstract
Brain atlases that encompass detailed anatomical or physiological features are instrumental in the research and surgical planning of various neurological conditions. Magnetic resonance imaging (MRI) has played important roles in neuro-image analysis while histological data remain crucial as a gold standard to guide and validate such analyses. With cellular-scale resolution, the BigBrain atlas offers 3D histology of a complete human brain, and is highly valuable to the research and clinical community. To bridge the insights at macro- and micro-levels, accurate mapping of BigBrain and established MRI brain atlases is necessary, but the existing registration is unsatisfactory. The described dataset includes co-registration of the BigBrain atlas to the MNI PD25 atlas and the ICBM152 2009b atlases (symmetric and asymmetric versions) in addition to manual segmentation of the basal ganglia, red nucleus, amygdala, and hippocampus for all mentioned atlases. The dataset intends to provide a bridge between insights from histological data and MRI studies in research and neurosurgical planning. The registered atlases, anatomical segmentations, and deformation matrices are available at: https://osf.io/xkqb3/ .
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Affiliation(s)
- Yiming Xiao
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada.
| | - Jonathan C Lau
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
- Department of Clinical Neurological Sciences, Division of Neurosurgery, Western University, London, Canada
| | - Taylor Anderson
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
| | - Jordan DeKraker
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
| | - D Louis Collins
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Terry Peters
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
| | - Ali R Khan
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
- School of Biomedical Engineering, Western University, London, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada
- The Brain and Mind Institute, Western University, London, Canada
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Xiao Y, Fortin M, Battié MC, Rivaz H. Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2018; 27:2442-2448. [PMID: 30051147 DOI: 10.1007/s00586-018-5704-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/16/2018] [Indexed: 12/19/2022]
Abstract
PURPOSE Growing evidence suggests an association between lumbar paraspinal muscle degeneration and low back pain (LBP). Currently, time-consuming and laborious manual segmentations of paraspinal muscles are commonly performed on magnetic resonance imaging (MRI) axial scans. Automated image analysis algorithms can mitigate these drawbacks, but they often require individual MRIs to be aligned to a standard "reference" atlas. Such atlases are well established in automated neuroimaging analysis. Our aim was to create atlases of similar nature for automated paraspinal muscle measurements. METHODS Lumbosacral T2-weighted MRIs were acquired from 117 patients who experienced LBP, stratified by gender and age group (30-39, 40-49, and 50-59 years old). Axial MRI slices of the L4-L5 and L5-S1 levels at mid-disc were obtained and aligned using group-wise linear and nonlinear image registration to produce a set of unbiased population-averaged atlases for lumbar paraspinal muscles. RESULTS The resulting atlases represent the averaged morphology and MRI intensity features of the corresponding cohorts. Differences in paraspinal muscle shapes and fat infiltration levels with respect to gender and age can be visually identified from the population-averaged data from both linear and nonlinear registrations. CONCLUSION We constructed a set of population-averaged atlases for developing automated algorithms to help analyze paraspinal muscle morphometry from axial MRI scans. Such an advancement could greatly benefit the fields of paraspinal muscle and LBP research. These slides can be retrieved under Electronic Supplementary Material.
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Affiliation(s)
- Yiming Xiao
- Robarts Research Institute, Western University, 1151 Richmond Street North, London, ON, N6A 5B7, Canada.
| | - Maryse Fortin
- PERFORM Centre, Concordia University, Montreal, Canada
| | - Michele C Battié
- School of Physical Therapy, Western University, London, Canada.,Bone and Joint Institute, Western University, London, Canada
| | - Hassan Rivaz
- PERFORM Centre, Concordia University, Montreal, Canada.,Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
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Li B, Jiang C, Li L, Zhang J, Meng D. Automated Segmentation and Reconstruction of the Subthalamic Nucleus in Parkinson's Disease Patients. Neuromodulation 2015; 19:13-9. [DOI: 10.1111/ner.12350] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/23/2015] [Accepted: 08/17/2015] [Indexed: 12/01/2022]
Affiliation(s)
- Bo Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace, Tsinghua University, Beijing, China
| | - Changqing Jiang
- National Engineering Laboratory for Neuromodulation, School of Aerospace, Tsinghua University, Beijing, China
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace, Tsinghua University, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Dawei Meng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson's disease. Int J Comput Assist Radiol Surg 2014; 10:1029-41. [PMID: 25249471 DOI: 10.1007/s11548-014-1119-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 09/10/2014] [Indexed: 12/11/2022]
Abstract
PURPOSE Parkinson's disease (PD) is a neurodegenerative disorder that impairs the motor functions. Both surgical treatment and study of PD require delineation of basal ganglia nuclei morphology. While many automatic volumetric segmentation methods have been proposed for the lentiform nucleus, few have attempted to identify the key brainstem substructures including the subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN) due to their small size and poor contrast in conventional T1W MRI. METHODS A dual-contrast patch-based label fusion method was developed to segment the SN, STN, and RN using multivariate cross-correlation. Two different MRI contrasts (T2*w and phase) are produced from a multi-contrast multi-echo FLASH MRI sequence, enabling visualization of these nuclei. T1-T2* fusion MRI was used to resolve the issue of poor nuclei (i.e., the STN, SN, and RN) contrast on T1w MRI, and to mitigate susceptibility artifacts that may hinder accurate nonlinear registration on T2*w MRI. Unbiased group-wise registration was used for anatomical normalization between the atlas library and the target subject. The performance of the proposed method was compared with a state-of-the-art single-contrast label fusion technique. RESULTS The proposed method outperformed a state-of-the-art single-contrast patch-based method in segmenting the STN, RN and SN, and the results were better than those reported in previous literature. CONCLUSION Our dual-contrast patch-based label fusion method was superior to a single-contrast method for segmenting brainstem nuclei using a multi-contrast multi-echo FLASH MRI sequence. The method is promising for the treatment and research of Parkinson's disease. This method can be extended for multiple alternative image contrasts and other fields of applications.
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Xiao Y, Fonov V, Bériault S, Subaie FA, Chakravarty MM, Sadikot AF, Pike GB, Collins DL. Multi-contrast unbiased MRI atlas of a Parkinson’s disease population. Int J Comput Assist Radiol Surg 2014; 10:329-41. [DOI: 10.1007/s11548-014-1068-y] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 04/29/2014] [Indexed: 11/24/2022]
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Abstract
We present a novel method for preoperative computer-assisted deep brain stimulation (DBS) electrode targeting that takes into account the multiplicity of available contacts and their polarity. Our framework automatically evaluates the efficacy of many possible electrode orientations to optimize the interplay between the extracellular electric field, created from distinct arrangements of active contacts, and anatomical structures responsible for therapeutic and potential side effects. Experimental results on subthalamic DBS cases suggest bipolar configurations provide more flexibility and control on the spread of electric field and, consequently, are most robust to targeting imprecision. Visualization of predicted efficacy maps provides surgeons with complementary feedback that can bridge the gap between insertion safety and optimal therapeutic efficacy. Overall, this work adds a new dimension to preoperative DBS planning and suggests new insights regarding multi-target stimulation.
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Multimodal imaging and image analysis techniques for neuromodulation. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2012. [PMID: 23206685 DOI: 10.1016/b978-0-12-404706-8.00012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Functional neurosurgical procedures used to treat the debilitating motor symptoms of Parkinson's disease and that target small subcortical structures have typically relied on semi-qualitative manual approaches that rely upon the establishing qualitative between volumetric imaging data and print atlases. This chapter reviews many new high -precision and -accuracy techniques that can be used for the full automated localization of these targets. These techniques rely on the a priori development of neuroanatomical atlases derived from magnetic resonance imaging data, high-resolution identification of subcortical structures from histology, and spatially localized data bases of intra-operative recordings and successful surgical outcomes. Other novel structural and functional MRI techniques that allow for the direct visualization of thalamic sub nuclei are also reviewed.
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