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Shao Y, Li L, Peng W, Lu W, Wang Y. Age-related changes in the healthy adult visual pathway: evidence from diffusion tensor imaging with fixel-based analysis. RADIOLOGIE (HEIDELBERG, GERMANY) 2023; 63:73-81. [PMID: 37603069 DOI: 10.1007/s00117-023-01192-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/06/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND AND PURPOSE Fixel-based analysis (FBA) is a new method that overcomes the technical limitations of diffusion tensor imaging (DTI) by enabling the characterization of multiple fiber populations within a voxel, and provides biologically meaningful indicators. This study aimed to explore age-related changes in the visual pathway in healthy adults and to observe differences in imaging quality between data collected using different b‑values. METHODS In this prospective cross-sectional study, brain DTI scans which were collected with more than six uniformly distributed gradient directions and higher b‑values (up to 2000 s/mm2) than traditional DTI were performed in 72 healthy adults across the adult lifespan (20-79 years). After image preprocessing, FBA was used to process the dataset. At the same time, conventional DTI metrics were also calculated. RESULTS Pearson's correlation analysis showed that DTI parameters of white matter (optic nerve, optic chiasma, optic tract, and optic radiation) in the optic pathway were correlated with age. FA values were negatively correlated with age, while MD/AD/RD showed a positive correlation (P < 0.05). FBA showed that the index including FD/FC/FDC tended to decline with age (P < 0.05). Linear regression analysis showed a linear relationship between DTI metrics of the dataset collected by b‑values of 1000 and 2000 s/mm2 (P < 0.05). CONCLUSION FBA provides a useful method to assess age-related changes in the visual pathway, which is sensitive to diffusion. In addition, the b‑value influences DTI parameters and signal-to-noise ratio of the image.
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Affiliation(s)
- Yan Shao
- The Second Affiliated Hospital of Shandong First Medical University, 271000, Taian, Shandong, China
| | - Li Li
- The Second Affiliated Hospital of Shandong First Medical University, 271000, Taian, Shandong, China
| | - Wei Peng
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430000, Wuhan, Hubei, China
| | - Weizhao Lu
- The Second Affiliated Hospital of Shandong First Medical University, 271000, Taian, Shandong, China
| | - Yi Wang
- Department of Ophthalmology, The Second Affiliated Hospital of Shandong First Medical University & Shandong Academy of Medical Sciences, 271000, Taian, Shandong, China.
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Carrozzi A, Gramegna LL, Sighinolfi G, Zoli M, Mazzatenta D, Testa C, Lodi R, Tonon C, Manners DN. Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods. Neuroimage Clin 2023; 39:103494. [PMID: 37651845 PMCID: PMC10477810 DOI: 10.1016/j.nicl.2023.103494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/21/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
The anterior optic pathway (AOP) is a system of three structures (optic nerves, optic chiasma, and optic tracts) that convey visual stimuli from the retina to the lateral geniculate nuclei. A successful reconstruction of the AOP using tractography could be helpful in several clinical scenarios, from presurgical planning and neuronavigation of sellar and parasellar surgery to monitoring the stage of fiber degeneration both in acute (e.g., traumatic optic neuropathy) or chronic conditions that affect AOP structures (e.g., amblyopia, glaucoma, demyelinating disorders or genetic optic nerve atrophies). However, its peculiar anatomy and course, as well as its surroundings, pose a serious challenge to obtaining successful tractographic reconstructions. Several AOP tractography strategies have been adopted but no standard procedure has been agreed upon. We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines in order to find the combinations of acquisition and reconstruction parameters that have been performed previously and have provided the highest rate of successful reconstruction of the AOP, in order to promote their routine implementation in clinical practice. For this purpose, we reviewed data regarding how the process of anatomical validation of the tractographies was performed. The Cochrane Handbook for Systematic Reviews of Interventions was used to assess the risk of bias and thus the study quality We identified thirty-nine studies that met our inclusion criteria, and only five were considered at low risk of bias and achieved over 80% of successful reconstructions. We found a high degree of heterogeneity in the acquisition and analysis parameters used to perform AOP tractography and different combinations of them can achieve satisfactory levels of anterior optic tractographic reconstruction both in real-life research and clinical scenarios. One thousand s/mm2 was the most frequently used b value, while both deterministic and probabilistic tractography algorithms performed morphological reconstruction of the tract satisfactorily, although probabilistic algorithms estimated a more realistic percentage of crossing fibers (45.6%) in healthy subjects. A wide heterogeneity was also found regarding the method used to assess the anatomical fidelity of the AOP reconstructions. Three main strategies can be found: direct visual direct visual assessment of the tractography superimposed to a conventional MR image, surgical evaluation, and computational methods. Because the latter is less dependent on a priori knowledge of the anatomy by the operator, computational methods of validation of the anatomy should be considered whenever possible.
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Affiliation(s)
- Alessandro Carrozzi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Laura Ludovica Gramegna
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy.
| | - Giovanni Sighinolfi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Matteo Zoli
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Pituitary Unit, Bologna, Italy
| | - Diego Mazzatenta
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Pituitary Unit, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
| | - David Neil Manners
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy; Department for Life Quality Studies (QUVI), University of Bologna, Bologna, Italy
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Jin R, Cai Y, Zhang S, Yang T, Feng H, Jiang H, Zhang X, Hu Y, Liu J. Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review. Front Neurosci 2023; 17:1191999. [PMID: 37304011 PMCID: PMC10250625 DOI: 10.3389/fnins.2023.1191999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.
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Affiliation(s)
- Richu Jin
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yongning Cai
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
| | - Shiyang Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Ting Yang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Haibo Feng
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Hongyang Jiang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoqing Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yan Hu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
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Moon Y, Yang JJ, Lee WJ, Lee JY, Kim YJ, Lim HW. In vivo Analysis of Normal Optic Nerve in an Elderly Population Using Diffusion Magnetic Resonance Imaging Tractography. Front Neurol 2021; 12:680488. [PMID: 34630272 PMCID: PMC8498569 DOI: 10.3389/fneur.2021.680488] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 07/22/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: To quantitatively investigate the microstructural properties of the optic nerve (ON) in vivo using diffusion magnetic resonance imaging (dMRI) tractography in an elderly population and to determine the differences between the ON diffusion properties stratified by basic demographics. Methods: We measured fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the intraorbital ON in cognitively normal controls selected from the Alzheimer's Disease Neuroimaging Initiative 3 database (n =104; mean age = 73. 8 ± 8.1 years) using dMRI probabilistic tractography and evaluated the correlation between diffusion parameters and demographic factors. Diffusion parameters were measured in 20 equidistant nodes along the tract, and the data from proximal 70% (14 nodes) of the intraorbital ON were averaged. Results: The mean FA of the intraorbital ON was 0.392 ± 0.063, and the mean MD was 1.163 ± 0.165 μm2/s. The mean RD was 0.882 ± 0.152 μm2/s, and the mean AD was 1.693 ± 0.183 μm2/s. The multiple linear regression model showed a negative correlation between FA and age. FA in females was significantly higher than males, whereas RD in female was significantly lower. Conclusions: We measured the diffusion properties of the intraorbital ON using dMRI tractography in an elderly cognitively normal population. The diffusion properties detected by dMRI tractography may substantially reflect the microstructure of the ON.
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Affiliation(s)
- Yeji Moon
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea.,Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, South Korea
| | - Jin-Ju Yang
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, South Korea.,Hanyang Vision Research Center, Hanyang University, Seoul, South Korea
| | - Won June Lee
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, South Korea.,Hanyang Vision Research Center, Hanyang University, Seoul, South Korea
| | - Ji Young Lee
- Hanyang Vision Research Center, Hanyang University, Seoul, South Korea.,Department of Radiology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, South Korea
| | - Yu Jeong Kim
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, South Korea.,Hanyang Vision Research Center, Hanyang University, Seoul, South Korea
| | - Han Woong Lim
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, South Korea.,Hanyang Vision Research Center, Hanyang University, Seoul, South Korea
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He J, Zhang F, Xie G, Yao S, Feng Y, Bastos DCA, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI. Hum Brain Mapp 2021; 42:3887-3904. [PMID: 33978265 PMCID: PMC8288095 DOI: 10.1002/hbm.25472] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/24/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022] Open
Abstract
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
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Affiliation(s)
- Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Guoqiang Xie
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryNuclear Industry 215 Hospital of Shaanxi ProvinceXianyangChina
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Pituitary Tumor Surgery, Department of NeurosurgeryThe First Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouChina
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
| | - Dhiego C. A. Bastos
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry, Neurology and Radiology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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