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Raji CA, Meysami S, Porter VR, Merrill DA, Mendez MF. Diagnostic utility of brain MRI volumetry in comparing traumatic brain injury, Alzheimer disease and behavioral variant frontotemporal dementia. BMC Neurol 2024; 24:337. [PMID: 39261753 PMCID: PMC11389120 DOI: 10.1186/s12883-024-03844-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 09/03/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Brain MRI with volumetric quantification, MRI volumetry, can improve diagnostic delineation of patients with neurocognitive disorders by identifying brain atrophy that may not be evident on visual assessments. OBJECTIVE To investigate diagnostic utility of MRI volumetry in traumatic brain injury (TBI), early-onset Alzheimer disease (EOAD), late-onset Alzheimer disease, and behavioral variant frontotemporal dementia (bvFTD). METHOD We utilized 137 participants of TBI (n = 40), EOAD (n = 45), LOAD (n = 32), and bvFTD (n = 20). Participants had 3D T1 brain MRI imaging amendable to MRI volumetry. Scan volumes were analyzed with Neuroreader. One-way ANOVA compared brain volumes across diagnostic groups. Discriminant analysis was done with leave-one-out cross validation on Neuroreader metrics to determine diagnostic delineation across groups. RESULT LOAD was the oldest compared to other groups (F = 27.5, p < .001). There were no statistically significant differences in sex (p = .58) with women comprising 54.7% of the entire cohort. EOAD and LOAD had the lowest Mini-Mental State Exam (MMSE) scores compared to TBI (p = .04 for EOAD and p = .01 for LOAD). LOAD had lowest hippocampal volumes (Left Hippocampus F = 13.1, Right Hippocampus F = 7.3, p < .001), low white matter volume in TBI (F = 5.9, p < .001), lower left parietal lobe volume in EOAD (F = 9.4, p < .001), and lower total gray matter volume in bvFTD (F = 32.8, p < .001) and caudate atrophy (F = 1737.5, p < .001). Areas under the curve ranged from 92.3 to 100%, sensitivity between 82.2 and 100%, specificity of 78.1-100%. TBI was the most accurately delineated diagnosis. Predictive features included caudate, frontal, parietal, temporal lobar and total white matter volumes. CONCLUSION We identified the diagnostic utility of regional volumetric differences across multiple neurocognitive disorders. Brain MRI volumetry is widely available and can be applied in distinguishing these disorders.
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Affiliation(s)
- Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA.
- Department of Neurology, Washington University, St. Louis, MO, USA.
| | - Somayeh Meysami
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Verna R Porter
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
- Providence Saint John's Health Center, Santa Monica, CA, USA
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
- Providence Saint John's Health Center, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Greater Los Angeles VA Healthcare System, Los Angeles, CA, USA
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Wittens MMJ, Denissen S, Sima DM, Fransen E, Niemantsverdriet E, Bastin C, Benoit F, Bergmans B, Bier JC, de Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Picard G, Ribbens A, Salmon E, Segers K, Sieben A, Struyfs H, Thiery E, Tournoy J, van Binst AM, Versijpt J, Smeets D, Bjerke M, Nagels G, Engelborghs S. Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study. Alzheimers Res Ther 2024; 16:128. [PMID: 38877568 PMCID: PMC11179390 DOI: 10.1186/s13195-024-01491-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVES This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. METHODS The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject. RESULTS MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. CONCLUSIONS Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.
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Affiliation(s)
- Mandy M J Wittens
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Stijn Denissen
- icometrix, Leuven, Belgium
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Diana M Sima
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Erik Fransen
- Centre of Medical Genetics, University of Antwerp, and Antwerp University Hospital - UZA, Edegem, Belgium
| | | | - Christine Bastin
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
| | - Florence Benoit
- Geriatrics Department, Brugmann University Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Bruno Bergmans
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Jean-Christophe Bier
- Neurological department H. U. B. - Erasme Hospital - Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Peter Paul de Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Antwerp, 2610, Belgium
- Memory Clinic, Ziekenhuisnetwerk, Antwerp, Belgium
| | - Olivier Deryck
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, 1200, Belgium
- Department of Neurology, Clinique Universitaires Saint-Luc, Brussels, 1200, Belgium
- WELBIO Department, WEL Research Institute, Wavre, 1300, Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc, and Institute of Neuroscience, Université Catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
- Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Memory Clinic - Neurology and Geriatrics Department, CHU Brugmann, Van Gehuchtenplein 4, Brussels, 1020, Belgium
| | - Anne Sieben
- Neuropathology Lab, IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Pathology, Antwerp University Hospital - UZA, Antwerp, Belgium
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Department of Chronic Diseases, Metabolism and Ageing, Geriatric Medicine and Memory Clinic, University Hospitals Leuven and KU Leuven, Louvain, Belgium
| | - Anne-Marie van Binst
- Radiology Department, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Dirk Smeets
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Maria Bjerke
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- Department of Clinical Chemistry, Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- St. Edmund Hall, University of Oxford, Oxford, UK
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium.
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Simarro J, Meyer MI, Van Eyndhoven S, Phan TV, Billiet T, Sima DM, Ortibus E. A deep learning model for brain segmentation across pediatric and adult populations. Sci Rep 2024; 14:11735. [PMID: 38778071 PMCID: PMC11111768 DOI: 10.1038/s41598-024-61798-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range: 2-81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range: 4-90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer's Disease.
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Affiliation(s)
- Jaime Simarro
- icometrix, Leuven, Belgium.
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
| | | | | | | | | | | | - Els Ortibus
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Pediatric Neurology, UZ Leuven, Leuven, Belgium
- Child and Youth Institute, KU Leuven, Leuven, Belgium
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Velasquez-Minoli JP, Cardona-Ramirez N, Garcia-Arias HF, Restrepo-Restrepo F, Porras-Hurtado GL. Clinical-functional correlation with brain volumetry in severe perinatal asphyxia: a case report. Ital J Pediatr 2024; 50:66. [PMID: 38594715 PMCID: PMC11003057 DOI: 10.1186/s13052-024-01633-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND Hypoxic-ischemic encephalopathy (HIE) appears in neurological conditions where some brain areas are likely to be injured, such as deep grey matter, basal ganglia area, and white matter subcortical periventricular áreas. Moreover, modeling these brain areas in a newborn is challenging due to significant variability in the intensities associated with HIE conditions. This paper aims to evaluate functional measurements and 3D machine learning models of a given HIE case by correlating the affected brain areas with the pathophysiology and clinical neurodevelopmental. CASE PRESENTATION A comprehensive analysis of a term infant with perinatal asphyxia using longitudinal 3D brain information from Machine Learning Models is presented. The clinical analysis revealed the perinatal asphyxia diagnosis with APGAR <5 at 5 and 10 minutes, umbilical arterial pH of 7.0 BE of -21.2 mmol / L), neonatal seizures, and invasive ventilation mechanics. Therapeutic interventions: physical, occupational, and language neurodevelopmental therapies. Epilepsy treatment: vagus nerve stimulation, levetiracetam, and phenobarbital. Furthermore, the 3D analysis showed how the volume decreases due to age, exhibiting an increasing asymmetry between hemispheres. The results of the basal ganglia area showed that thalamus asymmetry, caudate, and putamen increase over time while globus pallidus decreases. CLINICAL OUTCOMES spastic cerebral palsy, microcephaly, treatment-refractory epilepsy. CONCLUSIONS Slight changes in the basal ganglia and cerebellum require 3D volumetry for detection, as standard MRI examinations cannot fully reveal their complex shape variations. Quantifying these subtle neurodevelopmental changes helps in understanding their clinical implications. Besides, neurophysiological evaluations can boost neuroplasticity in children with neurological sequelae by stimulating new neuronal connections.
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Affiliation(s)
| | | | - Hernan Felipe Garcia-Arias
- Salud Comfamiliar, Caja de Compensación Familiar de Risaralda, Pereira, Colombia
- SISTEMIC Research Group, Universidad de Antioquia, Medellín, Colombia
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Wittens MMJ, Allemeersch GJ, Sima DM, Vanderhasselt T, Raeymaeckers S, Fransen E, Smeets D, de Mey J, Bjerke M, Engelborghs S. Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer's disease diagnosis. Neuroradiology 2024; 66:487-506. [PMID: 38240767 PMCID: PMC10937807 DOI: 10.1007/s00234-024-03280-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 12/28/2023] [Indexed: 03/14/2024]
Abstract
PURPOSE To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer's disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings. METHODS One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported. RESULTS Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA "severity" between visual MTA and ILV/Hip ratio measurements. CONCLUSION The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.
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Affiliation(s)
- Mandy M J Wittens
- Dept. of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- Dept. of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium.
| | - Diana M Sima
- Icometrix, Kolonel Begaultlaan 1b, 3012, Leuven, Belgium
- AI Supported Modelling in Clinical Sciences (AIMS), Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
| | - Tim Vanderhasselt
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Steven Raeymaeckers
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Dirk Smeets
- Icometrix, Kolonel Begaultlaan 1b, 3012, Leuven, Belgium
| | - Johan de Mey
- Dept. of Radiology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Maria Bjerke
- Dept. of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- NEUR (Neuroprotection & Neuromodulation), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Av. du Laerbeek 101, 1090, Brussels, Belgium
- Laboratory of Neurochemistry, Dept. of Clinical Chemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Dept. of Biomedical Sciences, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- Dept. of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Av. du Laerbeek 101, 1090, Brussels, Belgium
- NEUR (Neuroprotection & Neuromodulation), Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Av. du Laerbeek 101, 1090, Brussels, Belgium
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Attyé A, Renard F, Anglade V, Krainik A, Kahane P, Mansencal B, Coupé P, Calamante F. Data-driven normative values based on generative manifold learning for quantitative MRI. Sci Rep 2024; 14:7563. [PMID: 38555415 PMCID: PMC10981723 DOI: 10.1038/s41598-024-58141-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
In medicine, abnormalities in quantitative metrics such as the volume reduction of one brain region of an individual versus a control group are often provided as deviations from so-called normal values. These normative reference values are traditionally calculated based on the quantitative values from a control group, which can be adjusted for relevant clinical co-variables, such as age or sex. However, these average normative values do not take into account the globality of the available quantitative information. For example, quantitative analysis of T1-weighted magnetic resonance images based on anatomical structure segmentation frequently includes over 100 cerebral structures in the quantitative reports, and these tend to be analyzed separately. In this study, we propose a global approach to personalized normative values for each brain structure using an unsupervised Artificial Intelligence technique known as generative manifold learning. We test the potential benefit of these personalized normative values in comparison with the more traditional average normative values on a population of patients with drug-resistant epilepsy operated for focal cortical dysplasia, as well as on a supplementary healthy group and on patients with Alzheimer's disease.
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Affiliation(s)
| | | | - Vanina Anglade
- Department of Neuroradiology and MRI, SFR RMN Neurosciences, University Grenoble Alpes Hospital, Grenoble, France
| | - Alexandre Krainik
- Department of Neuroradiology and MRI, SFR RMN Neurosciences, University Grenoble Alpes Hospital, Grenoble, France
| | - Philippe Kahane
- Department of Neurology, University Grenoble Alpes Hospital, Grenoble, France
| | - Boris Mansencal
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, 33400, Talence, France
| | - Pierrick Coupé
- CNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, 33400, Talence, France
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2006, Australia
- Sydney Imaging-The University of Sydney, Sydney, Australia
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7
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Nous A, Seynaeve L, Feys O, Wens V, De Tiège X, Van Mierlo P, Baroumand AG, Nieboer K, Allemeersch GJ, Mangelschots S, Michiels V, van der Zee J, Van Broeckhoven C, Ribbens A, Houbrechts R, De Witte S, Wittens MMJ, Bjerke M, Vanlersberghe C, Ceyssens S, Nagels G, Smolders I, Engelborghs S. Subclinical epileptiform activity in the Alzheimer continuum: association with disease, cognition and detection method. Alzheimers Res Ther 2024; 16:19. [PMID: 38263073 PMCID: PMC10804650 DOI: 10.1186/s13195-023-01373-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/17/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Epileptic seizures are an established comorbidity of Alzheimer's disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. AIMS 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. METHODS Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. RESULTS We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher's exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall-Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann-Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. CONCLUSION We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. TRIAL REGISTRATION Clinicaltrials.gov, NCT04131491. 12/02/2020.
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Affiliation(s)
- Amber Nous
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Laboratory of Pharmaceutical Chemistry, Drug Analysis and Drug Information (FASC), Research Group Experimental Pharmacology (EFAR), Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Laura Seynaeve
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
| | - Odile Feys
- Department of Neurology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Brussels, Belgium
- Laboratoire de Neuroimagerie Et Neuroanatomie Translationnelles (LN2T), Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Neuroimagerie Et Neuroanatomie Translationnelles (LN2T), Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
- Department of Translational Neuroimaging, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Neuroimagerie Et Neuroanatomie Translationnelles (LN2T), Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
- Department of Translational Neuroimaging, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Brussels, Belgium
| | | | | | - Koenraad Nieboer
- Department of Radiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Department of Radiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Shana Mangelschots
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
| | - Veronique Michiels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Julie van der Zee
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Neurodegenerative Brain Diseases, VIB Center for Molecular Neurology, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Neurodegenerative Brain Diseases, VIB Center for Molecular Neurology, Antwerp, Belgium
| | | | | | - Sara De Witte
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
| | - Mandy Melissa Jane Wittens
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
| | - Maria Bjerke
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Department of Clinical Biology, Laboratory of Clinical Neurochemistry, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Caroline Vanlersberghe
- Department of Anaesthesiology and Perioperative Medicine, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Sarah Ceyssens
- Department of Nuclear Medicine, Universitair Ziekenhuis Antwerpen, University of Antwerp, Antwerpen, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Artificial Intelligence Supported Modelling in Clinical Sciences (AIMS) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Ilse Smolders
- Laboratory of Pharmaceutical Chemistry, Drug Analysis and Drug Information (FASC), Research Group Experimental Pharmacology (EFAR), Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium.
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium.
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8
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Yearley AG, Goedmakers CMW, Panahi A, Doucette J, Rana A, Ranganathan K, Smith TR. FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval. Artif Intell Med 2023; 143:102607. [PMID: 37673576 DOI: 10.1016/j.artmed.2023.102607] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 09/08/2023]
Abstract
Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.
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Affiliation(s)
- Alexander G Yearley
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA.
| | - Caroline M W Goedmakers
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands
| | - Armon Panahi
- The George Washington University School of Medicine and Health Sciences, 2300 I St NW, Washington, DC 20052, USA
| | - Joanne Doucette
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; School of Pharmacy, MCPHS University, 179 Longwood Ave, Boston, MA 02115, USA
| | - Aakanksha Rana
- Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA; Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
| | - Kavitha Ranganathan
- Division of Plastic Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115, USA
| | - Timothy R Smith
- Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA; Computational Neuroscience Outcomes Center (CNOC), Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115, USA
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9
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Weinstein SM, Davatzikos C, Doshi J, Linn KA, Shinohara RT. Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables. Biostatistics 2023; 24:653-668. [PMID: 35950944 PMCID: PMC10345990 DOI: 10.1093/biostatistics/kxac031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.
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Affiliation(s)
- Sarah M Weinstein
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 108/109B, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jimit Doshi
- Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Penn Statistics in Imaging and Visualization Center, 2nd Floor, Blockley Hall, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA and Department of Radiology, Perelman School of Medicine, Center for Biomedical Image Computing and Analytics, 3700 Hamilton Walk, Richards Building 7th Floor, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Ganzetti M, Graves JS, Holm SP, Dondelinger F, Midaglia L, Gaetano L, Craveiro L, Lipsmeier F, Bernasconi C, Montalban X, Hauser SL, Lindemann M. Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis. J Neurol 2023; 270:1624-1636. [PMID: 36469103 PMCID: PMC9970954 DOI: 10.1007/s00415-022-11494-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND A study was undertaken to evaluate remote monitoring via smartphone sensor-based tests in people with multiple sclerosis (PwMS). This analysis aimed to explore regional neural correlates of digital measures derived from these tests. METHODS In a 24-week, non-randomized, interventional, feasibility study (NCT02952911), sensor-based tests on the Floodlight Proof-of-Concept app were used to assess cognition (smartphone-based electronic Symbol Digit Modalities Test), upper extremity function (Draw a Shape Test, Pinching Test), and gait and balance (Static Balance Test, Two-Minute Walk Test, U-Turn Test). In this post-hoc analysis, digital measures and standard clinical measures (e.g., Nine-Hole Peg Test [9HPT]) were correlated against regional structural magnetic resonance imaging outcomes. Seventy-six PwMS aged 18-55 years with an Expanded Disability Status Scale score of 0.0-5.5 were enrolled from two different sites (USA and Spain). Sixty-two PwMS were included in this analysis. RESULTS Worse performance on digital and clinical measures was associated with smaller regional brain volumes and larger ventricular volumes. Whereas digital and clinical measures had many neural correlates in common (e.g., putamen, globus pallidus, caudate nucleus, lateral occipital cortex), some were observed only for digital measures. For example, Draw a Shape Test and Pinching Test measures, but not 9HPT score, correlated with volume of the hippocampus (r = 0.37 [drawing accuracy over time on the Draw a Shape Test]/ - 0.45 [touching asynchrony on the Pinching Test]), thalamus (r = 0.38/ - 0.41), and pons (r = 0.35/ - 0.35). CONCLUSIONS Multiple neural correlates were identified for the digital measures in a cohort of people with early MS. Digital measures showed associations with brain regions that clinical measures were unable to demonstrate, thus providing potential novel information on functional ability compared with standard clinical assessments.
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Affiliation(s)
- Marco Ganzetti
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Jennifer S. Graves
- grid.266100.30000 0001 2107 4242Department of Neurosciences, University of California San Diego, San Diego, CA USA
| | - Sven P. Holm
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Frank Dondelinger
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland ,grid.419481.10000 0001 1515 9979Present Address: Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Luciana Midaglia
- grid.411083.f0000 0001 0675 8654Department of Neurology-Neuroimmunology, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d’Hebron, Barcelona, Spain ,grid.7080.f0000 0001 2296 0625Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Laura Gaetano
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Licinio Craveiro
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | | | - Corrado Bernasconi
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Xavier Montalban
- grid.411083.f0000 0001 0675 8654Department of Neurology-Neuroimmunology, Centre d’Esclerosi Múltiple de Catalunya (Cemcat), Hospital Universitari Vall d’Hebron, Barcelona, Spain ,grid.7080.f0000 0001 2296 0625Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Stephen L. Hauser
- grid.266102.10000 0001 2297 6811Department of Neurology, University of California San Francisco, San Francisco, CA USA
| | - Michael Lindemann
- grid.417570.00000 0004 0374 1269F. Hoffmann-La Roche Ltd, Basel, Switzerland
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11
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Sarikhani A, Alamzadeh Z, Beik J, Irajirad R, Mirrahimi M, Pirhajati Mahabadi V, Kamrava SK, Ghaznavi H, Khoei S. Ultrasmall Fe3O4 and Gd2O3 hybrid nanoparticles for T1-weighted MR imaging of cancer. Cancer Nanotechnol 2022. [DOI: 10.1186/s12645-022-00148-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
AbstractGadolinium-based contrast agents (GdCAs) have been the most frequently used T1-weighted magnetic resonance imaging (MRI) contrast agents for decades. However, they have serious disadvantages such as low longitudinal relaxivity value (r1) and high dose associated-nephrotoxicity that restrict their wide applications. These emphasize the need for an ideal stable and biocompatible T1-weighted CA with high contrast enhancement performance. Here, we propose a wet-chemical synthesis technique to form a nanocomposite consisting of ultrasmall iron oxide nanoparticles (US-IO) and Gd2O3 hybrid nanoparticles stabilized with dextran (FG-HNPs) for T1-weighted MR imaging. Relaxometry study showed that FG-HNPs have a high r1 value (42.28 mM−1S−1) and low relaxivity ratio (r2/r1: 1.416) at 3.0T. In vivo MRI contrast enhancement factor (ΔSNR) for FG-HNPs (257.025 ± 17.4%) was found to be 1.99-fold higher than US-IO (129.102 ± 15%) and 3.35-fold higher than Dotarem (76.71 ± 14.2%) as routinely used T1-weighted CA. The cytotoxicity assay and histological examination confirmed the biocompatibility of FG-HNPs. The biodistribution study, transmission electron microscopy (TEM) and Prussian blue (PB) staining of tumor tissue proved the effective tumor localization of FG-HNPs. Therefore, FG-HNPs can be suggested as a promising CA for T1-weighted MRI of tumors by virtue of their remarkable relaxivities and high biocompatibility.
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12
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Genish T, Kavitha S, Vijayalakshmi S. A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer’s Disease. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2022. [DOI: 10.1142/s1469026822500201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.
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Affiliation(s)
- T. Genish
- School of Computing Science, KPR College of Arts Science and Research, Avinashi Road, Coimbatore, India
| | - S. Kavitha
- PG and Research, Department of Computer Science, Sakthi College of Arts and Science for Women, Oddanchatram, Dindigul, India
| | - S. Vijayalakshmi
- Department of Data Science, CHRIST (Deemed to be University), Pune, Lavasa Campus, India
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Abstract
PURPOSE OF REVIEW This article discusses neuroimaging in dementia diagnosis, with a focus on new applications of MRI and positron emission tomography (PET). RECENT FINDINGS Although the historical use of MRI in dementia diagnosis has been supportive to exclude structural etiologies, recent innovations allow for quantification of atrophy patterns that improve sensitivity for supporting the diagnosis of dementia causes. Neuronuclear approaches allow for localization of specific amyloid and tau neuropathology on PET and are available for clinical use, in addition to dopamine transporter scans in dementia with Lewy bodies and metabolic studies with fludeoxyglucose PET (FDG-PET). SUMMARY Using computerized software programs for MRI analysis and cross-sectional and longitudinal evaluations of hippocampal, ventricular, and lobar volumes improves sensitivity in support of the diagnosis of Alzheimer disease and frontotemporal dementia. MRI protocol requirements for such quantification are three-dimensional T1-weighted volumetric imaging protocols, which may need to be specifically requested. Fluid-attenuated inversion recovery (FLAIR) and 3.0T susceptibility-weighted imaging (SWI) sequences are useful for the detection of white matter hyperintensities as well as microhemorrhages in vascular dementia and cerebral amyloid angiopathy. PET studies for amyloid and/or tau pathology can add additional specificity to the diagnosis but currently remain largely inaccessible outside of research settings because of prohibitive cost constraints in most of the world. Dopamine transporter PET scans can help identify Lewy body dementia and are thus of potential clinical value.
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Affiliation(s)
- Cyrus A. Raji
- Washington University in St. Louis Mallinckrodt Institute of Radiology, Division of Neuroradiology
- Washington University in St. Louis Department of Neurology
- Washington University in St. Louis Neuroimaging Laboratories
- Knight Alzheimer Disease Research Center, Washington University in St. Louis
| | - Tammie L. S. Benzinger
- Washington University in St. Louis Mallinckrodt Institute of Radiology, Division of Neuroradiology
- Washington University in St. Louis Neuroimaging Laboratories
- Knight Alzheimer Disease Research Center, Washington University in St. Louis
- Washington University in St. Louis Department of Neurosurgery
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14
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Park HY, Suh CH, Heo H, Shim WH, Kim SJ. Diagnostic performance of hippocampal volumetry in Alzheimer's disease or mild cognitive impairment: a meta-analysis. Eur Radiol 2022; 32:6979-6991. [PMID: 35507052 DOI: 10.1007/s00330-022-08838-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/18/2022] [Accepted: 04/22/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To evaluate the diagnostic performance of hippocampal volumetry for Alzheimer's disease (AD) or mild cognitive impairment (MCI). METHODS The MEDLINE and Embase databases were searched for articles that evaluated the diagnostic performance of hippocampal volumetry in differentiating AD or MCI from normal controls, published up to March 6, 2022. The quality of the articles was evaluated by the QUADAS-2 tool. A bivariate random-effects model was used to pool sensitivity, specificity, and area under the curve. Sensitivity analysis and meta-regression were conducted to explain study heterogeneity. The diagnostic performance of entorhinal cortex volumetry was also pooled. RESULTS Thirty-three articles (5157 patients) were included. The pooled sensitivity and specificity for AD were 82% (95% confidence interval [CI], 77-86%) and 87% (95% CI, 82-91%), whereas those for MCI were 60% (95% CI, 51-69%) and 75% (95% CI, 67-81%), respectively. No difference in the diagnostic performance was observed between automatic and manual segmentation (p = 0.11). MMSE scores, study design, and the reference standard being used were associated with study heterogeneity (p < 0.01). Subgroup analysis demonstrated a higher diagnostic performance of entorhinal cortex volumetry for both AD (pooled sensitivity: 88% vs. 79%, specificity: 92% vs. 89%, p = 0.07) and MCI (pooled sensitivity: 71% vs. 55%, specificity: 83% vs. 68%, p = 0.06). CONCLUSIONS Our meta-analysis demonstrated good diagnostic performance of hippocampal volumetry for AD or MCI. Entorhinal cortex volumetry might have superior diagnostic performance to hippocampal volumetry. However, due to a small number of studies, the diagnostic performance of entorhinal cortex volumetry is yet to be determined. KEY POINTS • The pooled sensitivity and specificity of hippocampal volumetry for Alzheimer's disease were 82% and 87%, whereas those for mild cognitive impairment were 60% and 75%, respectively. • No significant difference in the diagnostic performance was observed between automatic and manual segmentation. • Subgroup analysis demonstrated superior diagnostic performance of entorhinal cortex volumetry for AD (pooled sensitivity: 88%, specificity: 92%) and MCI (pooled sensitivity: 71%, specificity: 83%).
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Affiliation(s)
- Ho Young Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
| | - Hwon Heo
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
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15
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Emsell L, Vanhaute H, Vansteelandt K, De Winter FL, Christiaens D, Van den Stock J, Vandenberghe R, Van Laere K, Sunaert S, Bouckaert F, Vandenbulcke M. An optimized MRI and PET based clinical protocol for improving the differential diagnosis of geriatric depression and Alzheimer's disease. Psychiatry Res Neuroimaging 2022; 320:111443. [PMID: 35091333 DOI: 10.1016/j.pscychresns.2022.111443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/28/2021] [Accepted: 01/12/2022] [Indexed: 11/16/2022]
Abstract
Amyloid positron emission tomography (PET) and hippocampal volume derived from magnetic resonance imaging may be useful clinical biomarkers for differentiating between geriatric depression and Alzheimer's disease (AD). Here we investigated the incremental value of using hippocampal volume and 18F-flutemetmol amyloid PET measures in tandem and sequentially to improve discrimination in unclassified participants. Two approaches were compared in 41 participants with geriatric depression and 27 participants with probable AD: (1) amyloid and hippocampal volume combined in one model and (2) classification based on hippocampal volume first and then subsequent stratification using standardized uptake value ratio (SUVR)-determined amyloid positivity. Hippocampal volume and amyloid SUVR were significant diagnostic predictors of depression (sensitivity: 95%, specificity: 89%). 51% of participants were correctly classified according to clinical diagnosis based on hippocampal volume alone, increasing to 87% when adding amyloid data (sensitivity: 94%, specificity: 78%). Our results suggest that hippocampal volume may be a useful gatekeeper for identifying depressed individuals at risk for AD who would benefit from additional amyloid biomarkers when available.
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Affiliation(s)
- Louise Emsell
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium; Department of Imaging & Pathology, Translational MRI, Medical Imaging Research Center, KU Leuven, UZ Leuven (Gasthuisberg), Leuven 3000, Belgium.
| | - Heleen Vanhaute
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Imaging & Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium; Nuclear Medicine, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium
| | - Kristof Vansteelandt
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - François-Laurent De Winter
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - Danny Christiaens
- Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium
| | - Jan Van den Stock
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - Rik Vandenberghe
- Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium.
| | - Koen Van Laere
- Department of Imaging & Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Leuven, Belgium; Nuclear Medicine, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium.
| | - Stefan Sunaert
- Department of Imaging & Pathology, Translational MRI, Medical Imaging Research Center, KU Leuven, UZ Leuven (Gasthuisberg), Leuven 3000, Belgium; Department of Radiology, University Hospitals Leuven, Herestraat 49, Leuven 3000, Belgium.
| | - Filip Bouckaert
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
| | - Mathieu Vandenbulcke
- Geriatric Psychiatry, UPC KU Leuven, Leuven, Belgium; Department of Neurosciences, Neuropsychiatry, KU Leuven, Leuven, Belgium.
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16
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Machine Learning in Medical Imaging – Clinical Applications and Challenges in Computer Vision. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Pemberton HG, Zaki LAM, Goodkin O, Das RK, Steketee RME, Barkhof F, Vernooij MW. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 2021; 63:1773-1789. [PMID: 34476511 PMCID: PMC8528755 DOI: 10.1007/s00234-021-02746-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/02/2021] [Indexed: 12/22/2022]
Abstract
Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lara A M Zaki
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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18
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Wittens MMJ, Allemeersch GJ, Sima DM, Naeyaert M, Vanderhasselt T, Vanbinst AM, Buls N, De Brucker Y, Raeymaekers H, Fransen E, Smeets D, van Hecke W, Nagels G, Bjerke M, de Mey J, Engelborghs S. Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer's Disease and Controls. Front Aging Neurosci 2021; 13:746982. [PMID: 34690745 PMCID: PMC8530224 DOI: 10.3389/fnagi.2021.746982] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 09/08/2021] [Indexed: 12/02/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).
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Affiliation(s)
- Mandy Melissa Jane Wittens
- Reference Center for Biological Markers of Dementia, Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | | | - Maarten Naeyaert
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Tim Vanderhasselt
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Nico Buls
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Yannick De Brucker
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Hubert Raeymaekers
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, Antwerp, Belgium
| | | | | | - Guy Nagels
- Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia, Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Johan de Mey
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia, Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N) and Department of Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
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19
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Meyer MI, de la Rosa E, Pedrosa de Barros N, Paolella R, Van Leemput K, Sima DM. A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI. Front Neurosci 2021; 15:708196. [PMID: 34531715 PMCID: PMC8439197 DOI: 10.3389/fnins.2021.708196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/27/2021] [Indexed: 11/30/2022] Open
Abstract
Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly available brain MRI datasets originate from the same center and are homogeneous in terms of scanner and used protocol. As such, devising robust methods that generalize to multi-scanner and multi-center data is crucial for transferring these techniques into clinical practice. We propose a novel data augmentation approach based on Gaussian Mixture Models (GMM-DA) with the goal of increasing the variability of a given dataset in terms of intensities and contrasts. The approach allows to augment the training dataset such that the variability in the training set compares to what is seen in real world clinical data, while preserving anatomical information. We compare the performance of a state-of-the-art U-Net model trained for segmenting brain structures with and without the addition of GMM-DA. The models are trained and evaluated on single- and multi-scanner datasets. Additionally, we verify the consistency of test-retest results on same-patient images (same and different scanners). Finally, we investigate how the presence of bias field influences the performance of a model trained with GMM-DA. We found that the addition of the GMM-DA improves the generalization capability of the DL model to other scanners not present in the training data, even when the train set is already multi-scanner. Besides, the consistency between same-patient segmentation predictions is improved, both for same-scanner and different-scanner repetitions. We conclude that GMM-DA could increase the transferability of DL models into clinical scenarios.
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Affiliation(s)
- Maria Ines Meyer
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.,Icometrix, Leuven, Belgium
| | - Ezequiel de la Rosa
- Icometrix, Leuven, Belgium.,Department of Computer Science, Technical University of Munich, Munich, Germany
| | | | - Roberto Paolella
- Icometrix, Leuven, Belgium.,Imec Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Koen Van Leemput
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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20
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Wittens MMJ, Sima DM, Houbrechts R, Ribbens A, Niemantsverdriet E, Fransen E, Bastin C, Benoit F, Bergmans B, Bier JC, De Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Lemper JC, Mormont E, Picard G, de la Rosa E, Salmon E, Segers K, Sieben A, Smeets D, Struyfs H, Thiery E, Tournoy J, Triau E, Vanbinst AM, Versijpt J, Bjerke M, Engelborghs S. Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study. J Alzheimers Dis 2021; 83:623-639. [PMID: 34334402 PMCID: PMC8543261 DOI: 10.3233/jad-210450] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts. Objective: This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis. Methods: The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm’s (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages. Results: icobrain dm outperformed FreeSurfer in processing time (15–30 min versus 9–32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%). Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.
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Affiliation(s)
- Mandy Melissa Jane Wittens
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | | | | | | | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, Belgium
| | - Christine Bastin
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium
| | - Florence Benoit
- Department of Geriatrics, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Bruno Bergmans
- Department of Neurology and Center for Cognitive Disorders, Brugge, Belgium
| | | | - Peter Paul De Deyn
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA), Antwerp, Belgium
| | - Olivier Deryck
- Department of Neurology and Center for Cognitive Disorders, Brugge, Belgium
| | - Bernard Hanseeuw
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Jean-Claude Lemper
- Department of Geriatrics, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel, Brussels, Belgium.,Silva medical Scheutbos, Molenbeek-Saint-Jean (Brussels), Belgium
| | - Eric Mormont
- UCLouvain, CHU UCL Namur, service de Neurologie, Yvoir, Belgium.,UCLouvain, Institute of NeuroScience, Louvain-la-Neuve, Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Anne Sieben
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | | | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Geriatric Medicine and Memory Clinic, University Hospitals Leuven & Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | | | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium.,Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
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21
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Phan TV, Sima D, Smeets D, Ghesquière P, Wouters J, Vandermosten M. Structural brain dynamics across reading development: A longitudinal MRI study from kindergarten to grade 5. Hum Brain Mapp 2021; 42:4497-4509. [PMID: 34197028 PMCID: PMC8410537 DOI: 10.1002/hbm.25560] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/12/2021] [Accepted: 06/04/2021] [Indexed: 01/05/2023] Open
Abstract
Primary education is the incubator for learning academic skills that help children to become a literate, communicative, and independent person. Over this learning period, nonlinear and regional changes in the brain occur, but how these changes relate to academic performance, such as reading ability, is still unclear. In the current study, we analyzed longitudinal T1 MRI data of 41 children in order to investigate typical cortical development during the early reading stage (end of kindergarten-end of grade 2) and advanced reading stage (end of grade 2-middle of grade 5), and to detect putative deviant trajectories in children with dyslexia. The structural brain change was quantified with a reliable measure that directly calculates the local morphological differences between brain images of two time points, while considering the global head growth. When applying this measure to investigate typical cortical development, we observed that left temporal and temporoparietal regions belonging to the reading network exhibited an increase during the early reading stage and stabilized during the advanced reading stage. This suggests that the natural plasticity window for reading is within the first years of primary school, hence earlier than the typical period for reading intervention. Concerning neurotrajectories in children with dyslexia compared to typical readers, we observed no differences in gray matter development of the left reading network, but we found different neurotrajectories in right IFG opercularis (during the early reading stage) and in right isthmus cingulate (during the advanced reading stage), which could reflect compensatory neural mechanisms.
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Affiliation(s)
- Thanh Van Phan
- icometrix, Research and Development, Leuven, Belgium.,Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
| | - Diana Sima
- icometrix, Research and Development, Leuven, Belgium
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Pol Ghesquière
- Parenting and Special Education, Faculty of Psychology and Education Sciences, KU Leuven, Leuven, Belgium
| | - Jan Wouters
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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22
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Schwarz AJ. The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders. Neurotherapeutics 2021; 18:686-708. [PMID: 33846962 PMCID: PMC8423963 DOI: 10.1007/s13311-021-01027-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.
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Affiliation(s)
- Adam J Schwarz
- Takeda Pharmaceuticals Ltd., 40 Landsdowne Street, Cambridge, MA, 02139, USA.
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23
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Costers L, Van Schependom J, Laton J, Baijot J, Sjøgård M, Wens V, De Tiège X, Goldman S, D'Haeseleer M, D'hooghe MB, Woolrich M, Nagels G. The role of hippocampal theta oscillations in working memory impairment in multiple sclerosis. Hum Brain Mapp 2021; 42:1376-1390. [PMID: 33247542 PMCID: PMC7927306 DOI: 10.1002/hbm.25299] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 11/11/2020] [Accepted: 11/13/2020] [Indexed: 01/04/2023] Open
Abstract
Working memory (WM) problems are frequently present in people with multiple sclerosis (MS). Even though hippocampal damage has been repeatedly shown to play an important role, the underlying neurophysiological mechanisms remain unclear. This study aimed to investigate the neurophysiological underpinnings of WM impairment in MS using magnetoencephalography (MEG) data from a visual-verbal 2-back task. We analysed MEG recordings of 79 MS patients and 38 healthy subjects through event-related fields and theta (4-8 Hz) and alpha (8-13 Hz) oscillatory processes. Data was source reconstructed and parcellated based on previous findings in the healthy subject sample. MS patients showed a smaller maximum theta power increase in the right hippocampus between 0 and 400 ms than healthy subjects (p = .014). This theta power increase value correlated negatively with reaction time on the task in MS (r = -.32, p = .029). Evidence was provided that this relationship could not be explained by a 'common cause' confounding relationship with MS-related neuronal damage. This study provides the first neurophysiological evidence of the influence of hippocampal dysfunction on WM performance in MS.
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Affiliation(s)
- Lars Costers
- AIMS Lab, Center For NeurosciencesUZ Brussel, Vrije Universiteit BrusselBrusselBelgium
| | - Jeroen Van Schependom
- AIMS Lab, Center For NeurosciencesUZ Brussel, Vrije Universiteit BrusselBrusselBelgium
- Departement of Electronics and Informatics (ETRO)Vrije Universiteit BrusselBrusselBelgium
- Departement of RadiologyUZ BrusselBrusselBelgium
| | - Jorne Laton
- AIMS Lab, Center For NeurosciencesUZ Brussel, Vrije Universiteit BrusselBrusselBelgium
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Johan Baijot
- AIMS Lab, Center For NeurosciencesUZ Brussel, Vrije Universiteit BrusselBrusselBelgium
| | - Martin Sjøgård
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC)UNI—ULB Neuroscience Institute, Université libre de Bruxelles (ULB)BruxellesBelgium
| | - Vincent Wens
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC)UNI—ULB Neuroscience Institute, Université libre de Bruxelles (ULB)BruxellesBelgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear MedicineCUB‐Hôpital ErasmeBruxellesBelgium
| | - Xavier De Tiège
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC)UNI—ULB Neuroscience Institute, Université libre de Bruxelles (ULB)BruxellesBelgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear MedicineCUB‐Hôpital ErasmeBruxellesBelgium
| | - Serge Goldman
- Laboratoire de Cartographie Fonctionnelle du Cerveau (LCFC)UNI—ULB Neuroscience Institute, Université libre de Bruxelles (ULB)BruxellesBelgium
- Magnetoencephalography Unit, Department of Functional Neuroimaging, Service of Nuclear MedicineCUB‐Hôpital ErasmeBruxellesBelgium
| | - Miguel D'Haeseleer
- Department of NeurologyNational MS Center MelsbroekMelsbroekBelgium
- Department of NeurologyUZ BrusselsBruxellesBelgium
| | - Marie Beatrice D'hooghe
- Department of NeurologyNational MS Center MelsbroekMelsbroekBelgium
- Department of NeurologyUZ BrusselsBruxellesBelgium
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA)University of OxfordOxfordUK
- Oxford University Centre for Functional MRI of the Brain (FMRIB)University of OxfordOxfordUK
| | - Guy Nagels
- AIMS Lab, Center For NeurosciencesUZ Brussel, Vrije Universiteit BrusselBrusselBelgium
- Department of NeurologyUZ BrusselsBruxellesBelgium
- St Edmund HallUniversity of OxfordOxfordUK
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24
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Pemberton HG, Goodkin O, Prados F, Das RK, Vos SB, Moggridge J, Coath W, Gordon E, Barrett R, Schmitt A, Whiteley-Jones H, Burd C, Wattjes MP, Haller S, Vernooij MW, Harper L, Fox NC, Paterson RW, Schott JM, Bisdas S, White M, Ourselin S, Thornton JS, Yousry TA, Cardoso MJ, Barkhof F. Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study. Eur Radiol 2021; 31:5312-5323. [PMID: 33452627 PMCID: PMC8213665 DOI: 10.1007/s00330-020-07455-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 10/01/2020] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Objectives We examined whether providing a quantitative report (QReport) of regional brain volumes improves radiologists’ accuracy and confidence in detecting volume loss, and in differentiating Alzheimer’s disease (AD) and frontotemporal dementia (FTD), compared with visual assessment alone. Methods Our forced-choice multi-rater clinical accuracy study used MRI from 16 AD patients, 14 FTD patients, and 15 healthy controls; age range 52–81. Our QReport was presented to raters with regional grey matter volumes plotted as percentiles against data from a normative population (n = 461). Nine raters with varying radiological experience (3 each: consultants, registrars, ‘non-clinical image analysts’) assessed each case twice (with and without the QReport). Raters were blinded to clinical and demographic information; they classified scans as ‘normal’ or ‘abnormal’ and if ‘abnormal’ as ‘AD’ or ‘FTD’. Results The QReport improved sensitivity for detecting volume loss and AD across all raters combined (p = 0.015* and p = 0.002*, respectively). Only the consultant group’s accuracy increased significantly when using the QReport (p = 0.02*). Overall, raters’ agreement (Cohen’s κ) with the ‘gold standard’ was not significantly affected by the QReport; only the consultant group improved significantly (κs 0.41➔0.55, p = 0.04*). Cronbach’s alpha for interrater agreement improved from 0.886 to 0.925, corresponding to an improvement from ‘good’ to ‘excellent’. Conclusion Our QReport referencing single-subject results to normative data alongside visual assessment improved sensitivity, accuracy, and interrater agreement for detecting volume loss. The QReport was most effective in the consultants, suggesting that experience is needed to fully benefit from the additional information provided by quantitative analyses. Key Points • The use of quantitative report alongside routine visual MRI assessment improves sensitivity and accuracy for detecting volume loss and AD vs visual assessment alone. • Consultant neuroradiologists’ assessment accuracy and agreement (kappa scores) significantly improved with the use of quantitative atrophy reports. • First multi-rater radiological clinical evaluation of visual quantitative MRI atrophy report for use as a diagnostic aid in dementia. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-020-07455-8.
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Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK. .,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK. .,Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ferran Prados
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - James Moggridge
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Elizabeth Gordon
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ryan Barrett
- Department of Neuroradiology, Brighton and Sussex University Hospitals, Brighton, UK
| | - Anne Schmitt
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Hefina Whiteley-Jones
- Department of Neuroradiology, Brighton and Sussex University Hospitals, Brighton, UK
| | | | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Lorna Harper
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Mark White
- Digital Services, University College London Hospital, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John S Thornton
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Tarek A Yousry
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.,Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.,Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK.,Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
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Nael K, Pawha PS, Fleysher L, George K, Stueben J, Roas-Loeffler M, Delman BN, Fayad ZA. Prospective Motion Correction for Brain MRI Using an External Tracking System. J Neuroimaging 2020; 31:57-61. [PMID: 33146946 DOI: 10.1111/jon.12806] [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] [Received: 08/27/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE A wide range of strategies have been developed to mitigate motion, as a major source of image quality degradation in clinical MRI. We aimed to assess the efficiency of a commercially available prospective motion correction (PMC) system in reducing motion in acquiring high-resolution 3D magnetization-prepared rapid gradient-echo (MPRAGE). METHODS A total of 100 patients who referred for brain MRI studies were prospectively imaged using a 3.0T scanner. 3D MPRAGE acquisition was obtained with and without application of PMC. The motion tracking system (KinetiCor Inc.) consisted of a quad camera apparatus, which tracks a specific marker on patient's head by evaluating the marker's optical pattern. The patient's head motion in 6 degrees of freedom throughout the acquisition was then incorporated into the MRI sequence, updating the image acquisition in real time based on the most recent head pose data. MPRAGE images with and without motion correction were assessed independently by two board-certified neuroradiologists using a 5-point Likert scale. Statistical analysis included kappa and Wilcoxon Rank-Sum tests. RESULTS Observers 1 and 2 identified nondiagnostic studies in 17.2% and 20.7% of patients (K = .78, 95% CI .70-.86) without motion correction and in 5.7% and 8% of the studies with motion correction (K = .84, 95% CI .76-.92). The number of nondiagnostic studies was significantly (P = .001) reduced from 19.5% to 5.7% after motion correction in consensus read analysis. CONCLUSION The described motion tracking system can be used effectively in clinical practice reducing motion artifact and improving image quality of 3D MPRAGE sequence.
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Affiliation(s)
- Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA.,Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Puneet S Pawha
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Kezia George
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Julianne Stueben
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Bradley N Delman
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Zahi A Fayad
- Department of Radiology, Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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