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Akdogan G, Istanbullu OB. Analysing the effects of metallic biomaterial design and imaging sequences on MRI interpretation challenges due to image artefacts. Phys Eng Sci Med 2022; 45:1163-1174. [PMID: 36306073 DOI: 10.1007/s13246-022-01183-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 10/01/2022] [Indexed: 12/15/2022]
Abstract
Biometals cause signal loss and susceptibility artefacts in the surrounding tissue, resulting in deterioration in magnetic resonance (MR) images. This metal-artefact effect may lead to interpretation challenges for MR images. Therefore, artefact reduction is required to obtain higher-quality images. This paper aims to analyse the impact of imaging sequence and metallic biomaterial design on MR image artefacts. In this respect, implant specimens were designed in thin, thick, and pointed forms and manufactured using 316LVM, 316L, CoCr-alloy, and Ti-alloy, which are commonly utilized materials in the biomaterials field. Specimens were placed in a phantom that simulates average human anatomy separately and scanned in a 1.5 T MRI under four imaging conditions: "Axial-T1-Gradient-Echo (GRE)", "Sagittal-T1-GRE", "Axial-T2-Spin-Echo (SE)" and "Sagittal-T2-SE". Images were analysed regarding image artefact amount. The lower magnetic susceptibility of Ti-alloy specimens caused 84.76% less deterioration than 316LVM specimens in the MR images with the mean image artefact-to-specimen size ratio. Thinner implant designs provided better performance regarding the metal artefact by reducing the artefact-to-specimen size ratio. T2SE decreased the image artefact by 44.7% for 316LVM and 54.6% for Ti-Alloy specimens and provided better image quality than T1GRE for clinical interpretation. This study reveals that image artefacts directly depend on material content, implant volume, geometry, and imaging sequence selection. The minor artefact effect of T2SE provides more accurate MR images than T1GRE regarding the interpretation of the images of the patients with biometals. The higher magnetic susceptibility of biometals causes more deterioration of the images.
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Affiliation(s)
- Gulsen Akdogan
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey.
| | - Omer Burak Istanbullu
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey
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Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means. Neuroimage 2021; 245:118749. [PMID: 34852276 PMCID: PMC8752961 DOI: 10.1016/j.neuroimage.2021.118749] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/15/2021] [Accepted: 11/20/2021] [Indexed: 11/22/2022] Open
Abstract
Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.
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Veale T, Malone IB, Poole T, Parker TD, Slattery CF, Paterson RW, Foulkes AJM, Thomas DL, Schott JM, Zhang H, Fox NC, Cash DM. Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study. Brain Commun 2021; 3:fcab272. [PMID: 34859218 PMCID: PMC8633427 DOI: 10.1093/braincomms/fcab272] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/24/2021] [Accepted: 10/18/2021] [Indexed: 11/22/2022] Open
Abstract
Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.
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Affiliation(s)
- Thomas Veale
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Teresa Poole
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas D Parker
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Catherine F Slattery
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ross W Paterson
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Alexander J M Foulkes
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - David L Thomas
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Jonathan M Schott
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - David M Cash
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
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Weston PSJ, Poole T, Nicholas JM, Toussaint N, Simpson IJA, Modat M, Ryan NS, Liang Y, Rossor MN, Schott JM, Ourselin S, Zhang H, Fox NC. Measuring cortical mean diffusivity to assess early microstructural cortical change in presymptomatic familial Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2020; 12:112. [PMID: 32943095 PMCID: PMC7499910 DOI: 10.1186/s13195-020-00679-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/04/2020] [Indexed: 12/21/2022]
Abstract
Background There is increasing interest in improving understanding of the timing and nature of early neurodegeneration in Alzheimer’s disease (AD) and developing methods to measure this in vivo. Autosomal dominant familial Alzheimer’s disease (FAD) provides the opportunity for investigation of presymptomatic change. We assessed early microstructural breakdown of cortical grey matter in FAD with diffusion-weighted MRI. Methods Diffusion-weighted and T1-weighed MRI were acquired in 38 FAD mutation carriers (17 symptomatic, 21 presymptomatic) and 39 controls. Mean diffusivity (MD) was calculated for six cortical regions previously identified as being particularly vulnerable to FAD-related neurodegeneration. Linear regression compared MD between symptomatic and presymptomatic carriers and controls, adjusting for age and sex. Spearman coefficients assessed associations between cortical MD and cortical thickness. Spearman coefficients also assessed associations between cortical MD and estimated years to/from onset (EYO). Across mutation carriers, linear regression assessed associations between MD and EYO, adjusting for cortical thickness. Results Compared with controls, cortical MD was higher in symptomatic mutation carriers (mean ± SD CDR = 0.88 ± 0.39) for all six regions (p < 0.001). In late presymptomatic carriers (within 8.1 years of predicted symptom onset), MD was higher in the precuneus (p = 0.04) and inferior parietal cortex (p = 0.003) compared with controls. Across all presymptomatic carriers, MD in the precuneus correlated with EYO (p = 0.04). Across all mutation carriers, there was strong evidence (p < 0.001) of association between MD and cortical thickness in all regions except entorhinal cortex. After adjusting for cortical thickness, there remained an association (p < 0.05) in mutation carriers between MD and EYO in all regions except entorhinal cortex. Conclusions Cortical MD measurement detects microstructural breakdown in presymptomatic FAD and correlates with proximity to symptom onset independently of cortical thickness. Cortical MD may thus be a feasible biomarker of early AD-related neurodegeneration, offering additional/complementary information to conventional MRI measures.
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Affiliation(s)
- Philip S J Weston
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.
| | - Teresa Poole
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Nicolas Toussaint
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Transitional Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ivor J A Simpson
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Transitional Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Natalie S Ryan
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Yuying Liang
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Martin N Rossor
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
| | - Sebastien Ourselin
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK.,Department of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Hui Zhang
- Microstructure Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, Queen Square, Box 16, London, WC1N 3BG, UK
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Xia H, Montresor S, Guo R, Li J, Picart P. Optimal processing scheme for restoration of phase data corrupted by strong decorrelation noise and dislocations. APPLIED OPTICS 2019; 58:G187-G196. [PMID: 31873502 DOI: 10.1364/ao.58.00g187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/25/2019] [Indexed: 06/10/2023]
Abstract
The presence of speckle noise and dislocations makes phase restoration potentially difficult in quantitative phase imaging and metrology. Unfortunately, there is no appropriate approach to deal with phase data corrupted by high speckle noise and phase dislocations. Usually, processing schemes may deal with low-pass phase filtering, phase unwrapping, or phase inpainting. This paper discusses the efficient processing to deal with noisy phase maps corrupted with phase dislocations. Six processing schemes, combining four operations, are evaluated. The investigation is carried out by realistic numerical simulations in which strong decorrelation phase noise and phase dislocations are generated. As a result, most robust and faster processing is established. The applicability of the optimal scheme is demonstrated through deformation measurement in dental materials.
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Jiskoot LC, Bocchetta M, Nicholas JM, Cash DM, Thomas D, Modat M, Ourselin S, Rombouts SA, Dopper EG, Meeter LH, Panman JL, van Minkelen R, van der Ende EL, Donker Kaat L, Pijnenburg YA, Borroni B, Galimberti D, Masellis M, Tartaglia MC, Rowe J, Graff C, Tagliavini F, Frisoni GB, Laforce R, Finger E, de Mendonça A, Sorbi S, Papma JM, van Swieten JC, Rohrer JD. Presymptomatic white matter integrity loss in familial frontotemporal dementia in the GENFI cohort: A cross-sectional diffusion tensor imaging study. Ann Clin Transl Neurol 2018; 5:1025-1036. [PMID: 30250860 PMCID: PMC6144447 DOI: 10.1002/acn3.601] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 06/08/2018] [Indexed: 12/12/2022] Open
Abstract
Objective We aimed to investigate mutation-specific white matter (WM) integrity changes in presymptomatic and symptomatic mutation carriers of the C9orf72,MAPT, and GRN mutations by use of diffusion-weighted imaging within the Genetic Frontotemporal dementia Initiative (GENFI) study. Methods One hundred and forty mutation carriers (54 C9orf72, 30 MAPT, 56 GRN), 104 presymptomatic and 36 symptomatic, and 115 noncarriers underwent 3T diffusion tensor imaging. Linear mixed effects models were used to examine the association between diffusion parameters and years from estimated symptom onset in C9orf72,MAPT, and GRN mutation carriers versus noncarriers. Post hoc analyses were performed on presymptomatic mutation carriers only, as well as left-right asymmetry analyses on GRN mutation carriers versus noncarriers. Results Diffusion changes in C9orf72 mutation carriers are present significantly earlier than both MAPT and GRN mutation carriers - characteristically in the posterior thalamic radiation and more posteriorly located tracts (e.g., splenium of the corpus callosum, posterior corona radiata), as early as 30 years before estimated symptom onset. MAPT mutation carriers showed early involvement of the uncinate fasciculus and cingulum, sparing the internal capsule, whereas involvement of the anterior and posterior internal capsule was found in GRN. Restricting analyses to presymptomatic mutation carriers only, similar - albeit less extensive - patterns were found: posteriorly located WM tracts (e.g., posterior thalamic radiation, splenium of the corpus callosum, posterior corona radiata) in presymptomatic C9orf72, the uncinate fasciculus in presymptomatic MAPT, and the internal capsule (anterior and posterior limbs) in presymptomatic GRN mutation carriers. In GRN, most tracts showed significant left-right differences in one or more diffusion parameter, with the most consistent results being found in the UF, EC, RPIC, and ALIC. Interpretation This study demonstrates the presence of early and widespread WM integrity loss in presymptomatic FTD, and suggests a clear genotypic "fingerprint." Our findings corroborate the notion of FTD as a network-based disease, where changes in connectivity are some of the earliest detectable features, and identify diffusion tensor imaging as a potential neuroimaging biomarker for disease-tracking and -staging in presymptomatic to early-stage familial FTD.
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Parker TD, Slattery CF, Zhang J, Nicholas JM, Paterson RW, Foulkes AJM, Malone IB, Thomas DL, Modat M, Cash DM, Crutch SJ, Alexander DC, Ourselin S, Fox NC, Zhang H, Schott JM. Cortical microstructure in young onset Alzheimer's disease using neurite orientation dispersion and density imaging. Hum Brain Mapp 2018; 39:3005-3017. [PMID: 29575324 PMCID: PMC6055830 DOI: 10.1002/hbm.24056] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 02/20/2018] [Accepted: 03/13/2018] [Indexed: 11/06/2022] Open
Abstract
Alzheimer's disease (AD) is associated with extensive alterations in grey matter microstructure, but our ability to quantify this in vivo is limited. Neurite orientation dispersion and density imaging (NODDI) is a multi-shell diffusion MRI technique that estimates neuritic microstructure in the form of orientation dispersion and neurite density indices (ODI/NDI). Mean values for cortical thickness, ODI, and NDI were extracted from predefined regions of interest in the cortical grey matter of 38 patients with young onset AD and 22 healthy controls. Five cortical regions associated with early atrophy in AD (entorhinal cortex, inferior temporal gyrus, middle temporal gyrus, fusiform gyrus, and precuneus) and one region relatively spared from atrophy in AD (precentral gyrus) were investigated. ODI, NDI, and cortical thickness values were compared between controls and patients for each region, and their associations with MMSE score were assessed. NDI values of all regions were significantly lower in patients. Cortical thickness measurements were significantly lower in patients in regions associated with early atrophy in AD, but not in the precentral gyrus. Decreased ODI was evident in patients in the inferior and middle temporal gyri, fusiform gyrus, and precuneus. The majority of AD-related decreases in cortical ODI and NDI persisted following adjustment for cortical thickness, as well as each other. There was evidence in the patient group that cortical NDI was associated with MMSE performance. These data suggest distinct differences in cortical NDI and ODI occur in AD and these metrics provide pathologically relevant information beyond that of cortical thinning.
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Affiliation(s)
- Thomas D Parker
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - Catherine F Slattery
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - Jiaying Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Jennifer M Nicholas
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom.,Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Ross W Paterson
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - Alexander J M Foulkes
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - Ian B Malone
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - David L Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom.,Leonard Wolfson Experimental Neurology Centre, UCL Institute of Neurology, London, United Kingdom
| | - Marc Modat
- Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - David M Cash
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom.,Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Sebastian J Crutch
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - Daniel C Alexander
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Nick C Fox
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Jonathan M Schott
- Department of Neurodegenerative Disease, Institute of Neurology, UCL, London, United Kingdom
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Lane CA, Parker TD, Cash DM, Macpherson K, Donnachie E, Murray-Smith H, Barnes A, Barker S, Beasley DG, Bras J, Brown D, Burgos N, Byford M, Jorge Cardoso M, Carvalho A, Collins J, De Vita E, Dickson JC, Epie N, Espak M, Henley SMD, Hoskote C, Hutel M, Klimova J, Malone IB, Markiewicz P, Melbourne A, Modat M, Schrag A, Shah S, Sharma N, Sudre CH, Thomas DL, Wong A, Zhang H, Hardy J, Zetterberg H, Ourselin S, Crutch SJ, Kuh D, Richards M, Fox NC, Schott JM. Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurol 2017; 17:75. [PMID: 28420323 PMCID: PMC5395844 DOI: 10.1186/s12883-017-0846-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Accepted: 03/21/2017] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. METHODS/DESIGN This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). DISCUSSION Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
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Affiliation(s)
- Christopher A. Lane
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Thomas D. Parker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Dave M. Cash
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Kirsty Macpherson
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Elizabeth Donnachie
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
| | - Heidi Murray-Smith
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Anna Barnes
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Suzie Barker
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Daniel G. Beasley
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jose Bras
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Medical Sciences and Institute of Biomedicine - iBiMED, University of Aveiro, Aveiro, Portugal
| | - David Brown
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Ninon Burgos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | | | - M. Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Ana Carvalho
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Jessica Collins
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Enrico De Vita
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - John C. Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Norah Epie
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Miklos Espak
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Susie M. D. Henley
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Chandrashekar Hoskote
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Michael Hutel
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Jana Klimova
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ian B. Malone
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Pawel Markiewicz
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Andrew Melbourne
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Anette Schrag
- Department of Clinical Neuroscience, Institute of Neurology, University College London, London, UK
| | - Sachit Shah
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Nikhil Sharma
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
- National Hospital for Neurology and Neurosurgery, London, UK
| | - Carole H. Sudre
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - David L. Thomas
- Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, University College London, London, UK
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, UK
| | - John Hardy
- Reta Lila Weston Research Laboratories, Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
| | - Henrik Zetterberg
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
| | - Sebastian J. Crutch
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing at UCL, London, UK
| | | | - Nick C. Fox
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Jonathan M. Schott
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
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9
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Xia H, Montresor S, Guo R, Li J, Olchewsky F, Desse JM, Picart P. Robust processing of phase dislocations based on combined unwrapping and inpainting approaches. OPTICS LETTERS 2017; 42:322-325. [PMID: 28081103 DOI: 10.1364/ol.42.000322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This Letter proposes a robust processing of phase dislocations to recover continuous phase maps. The approach is based on combined unwrapping and inpainting methods. Phase dislocations are determined using an estimator based on the second order phase gradient. The algorithm is validated using a realistic simulation of phase dislocations, and the phase restoration exhibits only weak errors. A comparison with other inpainting algorithms is also provided, demonstrating the suitability of the approach. The approach is applied to experimental data from off-axis digital holographic interferometry. The phase dislocation from phase data from a wake flow at Mach 0.73 are identified and processed. Excellent phase restoration can be appreciated.
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10
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Xia H, Montresor S, Guo R, Li J, Yan F, Cheng H, Picart P. Phase calibration unwrapping algorithm for phase data corrupted by strong decorrelation speckle noise. OPTICS EXPRESS 2016; 24:28713-28730. [PMID: 27958515 DOI: 10.1364/oe.24.028713] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Robust phase unwrapping in the presence of high noise remains an open issue. Especially, when both noise and fringe densities are high, pre-filtering may lead to phase dislocations and smoothing that complicate even more unwrapping. In this paper an approach to deal with high noise and to unwrap successfully phase data is proposed. Taking into account influence of noise in wrapped data, a calibration method of the 1st order spatial phase derivative is proposed and an iterative approach is presented. We demonstrate that the proposed method is able to process holographic phase data corrupted by non-Gaussian speckle decorrelation noise. The algorithm is validated by realistic numerical simulations in which the fringe density and noise standard deviation is progressively increased. Comparison with other established algorithms shows that the proposed algorithm exhibits better accuracy and shorter computation time, whereas others may fail to unwrap. The proposed algorithm is applied to phase data from digital holographic metrology and the unwrapped results demonstrate its practical effectiveness. The realistic simulations and experiments demonstrate that the proposed unwrapping algorithm is robust and fast in the presence of strong speckle decorrelation noise.
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11
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Kochan M, Daga P, Burgos N, White M, Cardoso MJ, Mancini L, Winston GP, McEvoy AW, Thornton J, Yousry T, Duncan JS, Stoyanov D, Ourselin S. Simulated field maps for susceptibility artefact correction in interventional MRI. Int J Comput Assist Radiol Surg 2015; 10:1405-16. [PMID: 26179219 DOI: 10.1007/s11548-015-1253-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 06/30/2015] [Indexed: 10/23/2022]
Abstract
PURPOSE Intraoperative MRI (iMRI) is a powerful modality for acquiring images of the brain to facilitate precise image-guided neurosurgery. Diffusion-weighted MRI (DW-MRI) provides critical information about location, orientation and structure of nerve fibre tracts, but suffers from the "susceptibility artefact" stemming from magnetic field perturbations due to the step change in magnetic susceptibility at air-tissue boundaries in the head. An existing approach to correcting the artefact is to acquire a field map by means of an additional MRI scan. However, to recover true field maps from the acquired field maps near air-tissue boundaries is challenging, and acquired field maps are unavailable in historical MRI data sets. This paper reports a detailed account of our method to simulate field maps from structural MRI scans that was first presented at IPCAI 2014 and provides a thorough experimental and analysis section to quantitatively validate our technique. METHODS We perform automatic air-tissue segmentation of intraoperative MRI scans, feed the segmentation into a field map simulation step and apply the acquired and the simulated field maps to correct DW-MRI data sets. RESULTS We report results for 12 patient data sets acquired during anterior temporal lobe resection surgery for the surgical management of focal epilepsy. We find a close agreement between acquired and simulated field maps and observe a statistically significant reduction in the susceptibility artefact in DW-MRI data sets corrected using simulated field maps in the vicinity of the resection. The artefact reduction obtained using acquired field maps remains better than that using the simulated field maps in all evaluated regions of the brain. CONCLUSIONS The proposed simulated field maps facilitate susceptibility artefact reduction near the resection. Accurate air-tissue segmentation is key to achieving accurate simulation. The proposed simulation approach is adaptable to different iMRI and neurosurgical applications.
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Affiliation(s)
- Martin Kochan
- Centre for Medical Image Computing, University College London, London, UK,
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