about
Strengths and weaknesses of state of the art fiber tractography pipelines--A comprehensive in-vivo and phantom evaluation study using Tractometer.Widespread white matter degeneration preceding the onset of dementia.The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for NeurosurgeryEarly Brain Loss in Circuits Affected by Alzheimer's Disease is Predicted by Fornix Microstructure but may be Independent of Gray Matter.The Medical Imaging Interaction Toolkit: challenges and advances : 10 years of open-source development.Early Detection of Malignant Transformation in Resected WHO II Low-Grade Glioma Using Diffusion Tensor-Derived Quantitative Measures.Hippocampal formation alterations differently contribute to autobiographic memory deficits in mild cognitive impairment and Alzheimer's disease.Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis.DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images.White matter microstructure variations contribute to neurological soft signs in healthy adults.Neurological soft signs in recent-onset schizophrenia: Focus on the cerebellum.Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI.Fiberfox: facilitating the creation of realistic white matter software phantoms.Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer.Disorder-specific white matter alterations in adolescent borderline personality disorder.Intravoxel incoherent motion (IVIM) diffusion imaging in prostate cancer - what does it add?Prediction of treatment response in head and neck carcinomas using IVIM-DWI: Evaluation of lymph node metastasis.Tractography-based connectomes are dominated by false-positive connectionsCorrelation between genomic index lesions and mpMRI and Ga-PSMA-PET/CT imaging features in primary prostate cancerMITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomicsMethodological considerations on tract-based spatial statistics (TBSS)Computer-assisted trajectory planning for percutaneous needle insertionsMITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRIIVIM DW-MRI of autoimmune pancreatitis: therapy monitoring and differentiation from pancreatic cancerThe Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based PhenotypingClassification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS AssessmentGoing Beyond Diffusion Tensor Imaging Tractography in Eloquent Glioma Surgery-High-Resolution Fiber Tractography: Q-Ball or Constrained Spherical Deconvolution?Author Correction: The challenge of mapping the human connectome based on diffusion tractographyIJCARS: BVM 2019 special issueAssociation of Cerebrospinal Fluid Volume with Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage: A Retrospective Volumetric AnalysisExploring cortical predictors of clinical response to electroconvulsive therapy in major depressionTransdiagnostic modulation of brain networks by electroconvulsive therapy in schizophrenia and major depressionWeight Loss and Changes in Adipose Tissue and Skeletal Muscle Volume after Laparoscopic Sleeve Gastrectomy and Roux-en-Y Gastric Bypass: a Prospective Study with 12-Month Follow-UpAutomated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosisAutomated brain extraction of multisequence MRI using artificial neural networksTractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challengeOptimal Statistical Incorporation of Independent Feature Stability Information into Radiomics StudiesAutomated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective studyAutomatic bone segmentation in whole-body CT imagesBundle-specific tractography with incorporated anatomical and orientational priors
P50
Q30488579-E6E06E14-D82F-4322-B7D7-3AD851672B6CQ30837017-A7AB6B7C-810E-4F94-B428-34AC695C08EEQ30986290-A49BF684-90BA-4A20-97C2-3576D7F77DC7Q33672857-25022166-5201-4C2E-9737-D13C3A4CFBBEQ34669777-E541305F-49EB-4C66-81F3-19AA7BB34FCBQ36164058-853A86AB-40B3-45C4-A34B-4599AAEEDD23Q38379615-ACD5A43F-983C-4534-870F-8CE211FC7B20Q39265216-57382A1C-6541-432B-9B7C-811F4BDDDA7BQ40640886-4873CA83-503C-433F-9D92-68B1BE940DCBQ47743215-ED62212F-D8D6-4AFA-AFD9-EA40EE155586Q48350141-2738EF5F-739E-459D-A28C-E188792642C4Q48792441-FA4608AC-9D62-4120-ABCD-44D4F736B02EQ48810117-9432160F-DD0B-4167-8FD3-4D251DAE28EDQ49815946-56BA8896-EDEF-4FE5-90A1-519DE3F89BCAQ50738847-95ECD87E-3062-480F-A009-B7ECE93A3A58Q51094012-B1EADE32-DB27-4EFF-900C-AD55488E32E7Q51101820-8BAE58CE-ED86-48B7-A6CF-CB55ECB0A37AQ56386206-CCF09886-F4B9-4110-AEEA-A8BF68E8CCC7Q59335329-1CC8734C-EB25-4AF8-BADD-24DDB8AA1022Q60636656-B08DB444-FBA0-44A1-AB3E-120C71D1E8B2Q60636657-A3A8B4AD-22EF-4ACC-968B-60A1252D23BAQ60636665-69699363-4C2B-45C6-A798-E5A46FF3A56FQ61447491-CE8941DA-4436-49F6-BD66-0808CF47C192Q86423210-06F6CE0D-1E2D-48DF-A30E-919A29F5FBF8Q90184582-AEC0DCE5-36A8-4FA9-A82E-B5E21DBC675DQ90585399-BC583B9D-A4CF-4074-81CD-3D991CADEF29Q91070661-AE62C5F4-46A8-45BC-AB26-5F66BBE1F462Q91105727-824B36DD-BDD6-4E25-A049-23F9C9F60878Q91131557-F5DC9069-5A81-4E71-AE29-98B339710557Q91481439-8187D531-474C-4BB2-9325-8D3C66016EAFQ91666780-7E666BC5-CF6F-4D3C-B8FD-5F5893D44C07Q91675572-C4DE0DFD-3A3D-4204-BFA9-01BC2677929CQ91913835-6AF677D4-4190-4C04-A1D3-9090B78E86F0Q92409290-6AA613A6-6421-4B5A-8457-EF1CB06A92A2Q92558872-D5963872-A584-4742-98E3-61BB94CED6E9Q92622580-12AD8D7A-9736-4239-8A13-1D4E4BBD857CQ92783194-1FDA0054-C807-46DE-92A4-286FB1131C49Q92883812-3E1C1FA4-B7AC-4BA9-BC9E-4D69EE14734DQ93062972-34DFEB05-04F4-4AE2-B238-8A833A118D8BQ93213200-D5A1FA78-E3B9-4C74-B05E-C437CBAD59F6
P50
description
researcher ORCID ID = 0000-0002-6626-2463
@en
wetenschapper
@nl
name
Klaus H Maier-Hein
@ast
Klaus H Maier-Hein
@en
Klaus H Maier-Hein
@es
Klaus H Maier-Hein
@nl
type
label
Klaus H Maier-Hein
@ast
Klaus H Maier-Hein
@en
Klaus H Maier-Hein
@es
Klaus H Maier-Hein
@nl
prefLabel
Klaus H Maier-Hein
@ast
Klaus H Maier-Hein
@en
Klaus H Maier-Hein
@es
Klaus H Maier-Hein
@nl
P106
P1153
23972210400
P214
97146462701127772063
P2456
P31
P496
0000-0002-6626-2463
P735
P7859
viaf-97146462701127772063