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Zhang H, Li P, Liu T, Wang X, Feng W, Chen R, Wei H, Li G, Ning L, Zhang M, Zhang Y. Focal white matter microstructural alteration after anthracycline-based systemic treatment in long-term breast cancer survivors: a structural magnetic resonance imaging study. Brain Imaging Behav 2021; 16:843-854. [PMID: 34608574 DOI: 10.1007/s11682-021-00551-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/27/2021] [Indexed: 11/29/2022]
Abstract
Understanding the neural correlates of cognitive problems in patients with breast cancer (BC) after systemic treatment have been a topic of increasing investigation. The heterogeneity of the systemic treatment regimens may undermine our ability to identify brain microstructural alterations resulting from any given regimen. We investigated the detrimental effects of the anthracycline-based systemic treatment (AST) regimen (epirubicin and cyclophosphamide + docetaxel + tamoxifen) on brain gray matter (GM) and white matter (WM) microstructural alteration in long-term BC survivors. We performed a battery of neuropsychological tests and structural magnetic resonance imaging (MRI) to 31 long-term BC survivors who had received the AST regimen (AST group) and 43 healthy controls (HC group). Voxel-based morphometry evaluated the whole-brain voxel-wise GM volume, while diffusion tensor imaging technique with tract-based spatial statistics analysis evaluated whole-brain WM microstructural alteration. Partial least squares regression (PLSR) was used to evaluate the relationship between cognitive impairment and brain microstructural alteration in BC survivors. Compared with the HC group, the AST group exhibited a significantly poorer performance in attention, as well as a marginal significantly poorer performance in verbal working memory and executive function. Significantly lower fractional anisotropy (FA), higher radial diffusivity (RD), and lower axial diffusivity (AD) in multiple brain WM regions were showed in AST group compared with the HC group. Overlap of lower FA and higher RD was found in the body of corpus callosum (CC) and bilateral superior corona radiata (SCR), whereas overlap of lower FA and AD was found in the body of CC and right SCR. The PLSR results showed that the WM regions with overlap of lower FA and AD were significantly associated with executive and verbal working memory decline. No significant difference was observed in the GM volume between groups. Our results suggest that microstructural abnormalities of certain vulnerable WM regions in the AST regimen-exposed brain may provide neuroimaging evidence for the identification of brain injury and cognitive impairment induced by specific chemotherapy regimens.
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Affiliation(s)
- Huawen Zhang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Republic of China.,Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Peng Li
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Republic of China.,Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Tonghui Liu
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Xueyuan Wang
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Wei Feng
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Rui Chen
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Hengyang Wei
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Guoqiang Li
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Long Ning
- Department of Medical Imaging, No.215 Hospital of Shaanxi Nuclear Geology, Xianyang, China
| | - Ming Zhang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Republic of China.
| | - Yuchen Zhang
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, No. 277, West Yanta Road, Xi'an, Shaanxi, 710061, Republic of China.
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2
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Martí-Juan G, Sanroma-Guell G, Cacciaglia R, Falcon C, Operto G, Molinuevo JL, González Ballester MÁ, Gispert JD, Piella G. Nonlinear interaction between APOE ε4 allele load and age in the hippocampal surface of cognitively intact individuals. Hum Brain Mapp 2020; 42:47-64. [PMID: 33017488 PMCID: PMC7721244 DOI: 10.1002/hbm.25202] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 07/16/2020] [Accepted: 08/11/2020] [Indexed: 01/27/2023] Open
Abstract
The ε4 allele of the gene Apolipoprotein E is the major genetic risk factor for Alzheimer's Disease. APOE ε4 has been associated with changes in brain structure in cognitively impaired and unimpaired subjects, including atrophy of the hippocampus, which is one of the brain structures that is early affected by AD. In this work we analyzed the impact of APOE ε4 gene dose and its association with age, on hippocampal shape assessed with multivariate surface analysis, in a ε4‐enriched cohort of n = 479 cognitively healthy individuals. Furthermore, we sought to replicate our findings on an independent dataset of n = 969 individuals covering the entire AD spectrum. We segmented the hippocampus of the subjects with a multi‐atlas‐based approach, obtaining high‐dimensional meshes that can be analyzed in a multivariate way. We analyzed the effects of different factors including APOE, sex, and age (in both cohorts) as well as clinical diagnosis on the local 3D hippocampal surface changes. We found specific regions on the hippocampal surface where the effect is modulated by significant APOE ε4 linear and quadratic interactions with age. We compared between APOE and diagnosis effects from both cohorts, finding similarities between APOE ε4 and AD effects on specific regions, and suggesting that age may modulate the effect of APOE ε4 and AD in a similar way.
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Affiliation(s)
- Gerard Martí-Juan
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Raffaele Cacciaglia
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - Carles Falcon
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Madrid, Spain
| | - Grégory Operto
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
| | - José Luis Molinuevo
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Miguel Ángel González Ballester
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain.,ICREA, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.,Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBERBBN), Madrid, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
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3
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Bernardino G, Benkarim O, Sanz-de la Garza M, Prat-Gonzàlez S, Sepulveda-Martinez A, Crispi F, Sitges M, Butakoff C, De Craene M, Bijnens B, González Ballester MA. Handling confounding variables in statistical shape analysis - application to cardiac remodelling. Med Image Anal 2020; 65:101792. [PMID: 32712526 DOI: 10.1016/j.media.2020.101792] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 04/01/2020] [Accepted: 07/17/2020] [Indexed: 11/15/2022]
Abstract
Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.
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Affiliation(s)
- Gabriel Bernardino
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | | | - Susanna Prat-Gonzàlez
- Cardiovascular Institute, Hospital Clínic, Barcelona, Spain; IDIBAPS, Barcelona, Spain
| | - Alvaro Sepulveda-Martinez
- BCNatal, Hospital Clínic and Sant Joan de Déu, Universitat de Barcelona, Barcelona, Spain; Fetal Medicine Unit, Department of Obstetrics and Gynecology, Hospital Clínico de la Universidad de Chile, Santiago de Chile, Chile
| | - Fátima Crispi
- IDIBAPS, Barcelona, Spain; BCNatal, Hospital Clínic and Sant Joan de Déu, Universitat de Barcelona, Barcelona, Spain; CIBER-ER, Barcelona, Spain
| | - Marta Sitges
- Cardiovascular Institute, Hospital Clínic, Barcelona, Spain; IDIBAPS, Barcelona, Spain; CIBER-CV, Barcelona, Spain
| | | | | | - Bart Bijnens
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; IDIBAPS, Barcelona, Spain; ICREA, Barcelona, Spain
| | - Miguel A González Ballester
- BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain
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4
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Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT. Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING, COMMUNICATIONS, AND APPLICATIONS 2020; 16:1-35. [DOI: 10.1145/3344998] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 07/01/2019] [Indexed: 08/30/2023]
Abstract
Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
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Affiliation(s)
- M. Tanveer
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - B. Richhariya
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - R. U. Khan
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, India
| | - A. H. Rashid
- Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Indore 8 School of Computer Science and Engineering, National Institute of Science and Technology, Berhampur, Odisha, India
| | - P. Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India
| | - M. Prasad
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
| | - C. T. Lin
- Centre for Artificial Intelligence, School of Computer Science, FEIT, University of Technology Sydney, Sydney, Australia
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5
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Mirzaei G, Adeli H. Segmentation and clustering in brain MRI imaging. Rev Neurosci 2019; 30:31-44. [PMID: 30265656 DOI: 10.1515/revneuro-2018-0050] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 07/19/2018] [Indexed: 12/17/2022]
Abstract
Clustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.
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Affiliation(s)
- Golrokh Mirzaei
- Department of Computer Science and Engineering, The Ohio State University, Marion, OH 43302, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics, Neurology, Neuroscience, The Ohio State University, Columbus, OH 43210, USA
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6
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Zhang F, Tian S, Chen S, Ma Y, Li X, Guo X. Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer’s Disease Based on an Extreme Learning Machine Method from the ADNI cohort. Neuroscience 2019; 414:273-279. [DOI: 10.1016/j.neuroscience.2019.05.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 05/06/2019] [Accepted: 05/07/2019] [Indexed: 01/17/2023]
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7
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Huizinga W, Poot D, Vernooij M, Roshchupkin G, Bron E, Ikram M, Rueckert D, Niessen W, Klein S. A spatio-temporal reference model of the aging brain. Neuroimage 2018; 169:11-22. [DOI: 10.1016/j.neuroimage.2017.10.040] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/12/2017] [Accepted: 10/19/2017] [Indexed: 01/27/2023] Open
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8
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Wong E, Palande S, Wang B, Zielinski B, Anderson J, Fletcher PT. KERNEL PARTIAL LEAST SQUARES REGRESSION FOR RELATING FUNCTIONAL BRAIN NETWORK TOPOLOGY TO CLINICAL MEASURES OF BEHAVIOR. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2016; 2016:1303-1306. [PMID: 32742554 DOI: 10.1109/isbi.2016.7493506] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper we present a novel method for analyzing the relationship between functional brain networks and behavioral phenotypes. Drawing from topological data analysis, we first extract topological features using persistent homology from functional brain networks that are derived from correlations in resting-state fMRI. Rather than fixing a discrete network topology by thresholding the connectivity matrix, these topological features capture the network organization across all continuous threshold values. We then propose to use a kernel partial least squares (kPLS) regression to statistically quantify the relationship between these topological features and behavior measures. The kPLS also provides an elegant way to combine multiple image features by using linear combinations of multiple kernels. In our experiments we test the ability of our proposed brain network analysis to predict autism severity from rs-fMRI. We show that combining correlations with topological features gives better prediction of autism severity than using correlations alone.
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Affiliation(s)
- Eleanor Wong
- Scientific Computing and Imaging Institute, University of Utah.,School of Computing, University of Utah
| | | | - Bei Wang
- Scientific Computing and Imaging Institute, University of Utah
| | | | | | - P Thomas Fletcher
- Scientific Computing and Imaging Institute, University of Utah.,School of Computing, University of Utah
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10
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Bruse JL, McLeod K, Biglino G, Ntsinjana HN, Capelli C, Hsia TY, Sermesant M, Pennec X, Taylor AM, Schievano S. A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta. BMC Med Imaging 2016; 16:40. [PMID: 27245048 PMCID: PMC4894556 DOI: 10.1186/s12880-016-0142-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 05/19/2016] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. METHODS Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. RESULTS The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. CONCLUSIONS The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.
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Affiliation(s)
- Jan L Bruse
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK.
| | - Kristin McLeod
- Cardiac Modelling Department, Simula Research Laboratory, Oslo, Norway
- Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France
| | - Giovanni Biglino
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
- Bristol Heart Institute, School of Clinical Sciences, University of Bristol, Bristol, UK
| | - Hopewell N Ntsinjana
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Claudio Capelli
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Tain-Yen Hsia
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Maxime Sermesant
- Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France
| | - Xavier Pennec
- Inria Sophia Antipolis-Méditeranée, ASCLEPIOS Project, Sophia Antipolis, France
| | - Andrew M Taylor
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
| | - Silvia Schievano
- Centre for Cardiovascular Imaging, University College London, Institute of Cardiovascular Science & Cardiorespiratory Unit, Great Ormond Street Hospital for Children, London, UK
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11
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Schmidt-Richberg A, Ledig C, Guerrero R, Molina-Abril H, Frangi A, Rueckert D. Learning Biomarker Models for Progression Estimation of Alzheimer's Disease. PLoS One 2016; 11:e0153040. [PMID: 27096739 PMCID: PMC4838309 DOI: 10.1371/journal.pone.0153040] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 03/22/2016] [Indexed: 11/26/2022] Open
Abstract
Being able to estimate a patient's progress in the course of Alzheimer's disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and--employing cognitive scores and image-based biomarkers--real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.
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Affiliation(s)
| | - Christian Ledig
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Ricardo Guerrero
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - Helena Molina-Abril
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom
| | - Alejandro Frangi
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Sheffield, Sheffield, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
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12
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Bruse JL, McLeod K, Biglino G, Ntsinjana HN, Capelli C, Hsia TY, Sermesant M, Pennec X, Taylor AM, Schievano S. A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal? STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES 2016. [DOI: 10.1007/978-3-319-28712-6_3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Inhibitory effect of sesquiterpene lactones and the sesquiterpene alcohol aromadendrane-4β,10α-diol on memory impairment in a mouse model of Alzheimer. Eur J Pharmacol 2015; 769:195-202. [DOI: 10.1016/j.ejphar.2015.11.018] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 11/11/2015] [Accepted: 11/13/2015] [Indexed: 12/23/2022]
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14
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Miller MI, Trouvé A, Younes L. Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson. Annu Rev Biomed Eng 2015; 17:447-509. [PMID: 26643025 DOI: 10.1146/annurev-bioeng-071114-040601] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Computational Anatomy project is the morphome-scale study of shape and form, which we model as an orbit under diffeomorphic group action. Metric comparison calculates the geodesic length of the diffeomorphic flow connecting one form to another. Geodesic connection provides a positioning system for coordinatizing the forms and positioning their associated functional information. This article reviews progress since the Euler-Lagrange characterization of the geodesics a decade ago. Geodesic positioning is posed as a series of problems in Hamiltonian control, which emphasize the key reduction from the Eulerian momentum with dimension of the flow of the group, to the parametric coordinates appropriate to the dimension of the submanifolds being positioned. The Hamiltonian viewpoint provides important extensions of the core setting to new, object-informed positioning systems. Several submanifold mapping problems are discussed as they apply to metamorphosis, multiple shape spaces, and longitudinal time series studies of growth and atrophy via shape splines.
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Affiliation(s)
- Michael I Miller
- Center of Imaging Science.,Department of Biomedical Engineering.,Kavli Neuroscience Discovery Institute, and
| | - Alain Trouvé
- CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 94235 Cachan, France;
| | - Laurent Younes
- Center of Imaging Science.,Department of Applied Mathematics, The John Hopkins University, Baltimore, Maryland 21218; ,
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15
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Singh N, Vialard FX, Niethammer M. Splines for diffeomorphisms. Med Image Anal 2015; 25:56-71. [PMID: 25980676 DOI: 10.1016/j.media.2015.04.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2014] [Revised: 04/06/2015] [Accepted: 04/09/2015] [Indexed: 11/16/2022]
Abstract
This paper develops a method for higher order parametric regression on diffeomorphisms for image regression. We present a principled way to define curves with nonzero acceleration and nonzero jerk. This work extends methods based on geodesics which have been developed during the last decade for computational anatomy in the large deformation diffeomorphic image analysis framework. In contrast to previously proposed methods to capture image changes over time, such as geodesic regression, the proposed method can capture more complex spatio-temporal deformations. We take a variational approach that is governed by an underlying energy formulation, which respects the nonflat geometry of diffeomorphisms. Such an approach of minimal energy curve estimation also provides a physical analogy to particle motion under a varying force field. This gives rise to the notion of the quadratic, the cubic and the piecewise cubic splines on the manifold of diffeomorphisms. The variational formulation of splines also allows for the use of temporal control points to control spline behavior. This necessitates the development of a shooting formulation for splines. The initial conditions of our proposed shooting polynomial paths in diffeomorphisms are analogous to the Euclidean polynomial coefficients. We experimentally demonstrate the effectiveness of using the parametric curves both for synthesizing polynomial paths and for regression of imaging data. The performance of the method is compared to geodesic regression.
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Affiliation(s)
- Nikhil Singh
- The University of North Carolina, Chapel Hill, NC 27514, USA.
| | | | - Marc Niethammer
- The University of North Carolina, Chapel Hill, NC 27514, USA
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16
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Risser L, Dolius L, Fonta C, Mescam M. Diffeomorphic registration with self-adaptive spatial regularization for the segmentation of non-human primate brains. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:6695-8. [PMID: 25571532 DOI: 10.1109/embc.2014.6945164] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Cerebral aging has been linked to structural and functional changes in the brain throughout life. Here, we study the marmoset, a small non-human primate, in order to get insights into the mechanisms of brain aging in normal and pathological conditions. Imaging the brain of small animals with techniques such as MRI, quickly becomes a challenging task when compared with human brain imaging. Very often, a simple pre-processing step such as brain extraction cannot be achieved with classical tools. In this paper, we propose a diffeomorphic registration algorithm, which makes use of learned constraints to propagate the manual segmentation of a marmoset brain template to other MR images of marmoset brains. The main methological contribution of our paper is to explore a new strategy to automatically tune the spatial regularization of the deformations. Results show that we obtain a robust segmentation of the brain, even for images with a low contrast.
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17
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Toews M, Wachinger C, Estepar RSJ, Wells WM. A Feature-Based Approach to Big Data Analysis of Medical Images. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2015. [PMID: 26221685 DOI: 10.1007/978-3-319-19992-4_26] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.
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18
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Vialard FX, Risser L. Spatially-varying metric learning for diffeomorphic image registration: a variational framework. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2014; 17:227-34. [PMID: 25333122 DOI: 10.1007/978-3-319-10404-1_29] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
This paper introduces a variational strategy to learn spatially-varying metrics on large groups of images, in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. Spatially-varying metrics we learn not only favor local deformations but also correlated deformations in different image regions and in different directions. In addition, metric parameters can be efficiently estimated using a gradient descent method. We first describe the general strategy and then show how to use it on 3D medical images with reasonable computational ressources. Our method is assessed on the 3D brain images of the LPBA40 dataset. Results are compared with ANTS-SyN and LDDMM with spatially-homogeneous metrics.
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