@article{RN315,
   author = {Dietrich, O. and Cai, M. and Tuladhar, A. M. and Jacob, M. A. and Drenthen, G. S. and Jansen, J. F. A. and Marques, J. P. and Topalis, J. and Ingrisch, M. and Ricke, J. and de Leeuw, F. E. and Duering, M. and Backes, W. H.},
   title = {Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition},
   journal = {NMR Biomed},
   pages = {e4905},
   note = {Dietrich, Olaf
Cai, Mengfei
Tuladhar, Anil Man
Jacob, Mina A
Drenthen, Gerald S
Jansen, Jacobus F A
Marques, Jose P
Topalis, Johanna
Ingrisch, Michael
Ricke, Jens
de Leeuw, Frank-Erik
Duering, Marco
Backes, Walter H
eng
England
NMR Biomed. 2023 Jan 13:e4905. doi: 10.1002/nbm.4905.},
   abstract = {The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data of the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 minutes and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm(2) and 6 noncollinear diffusion gradient directions for each b-value. 7 different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi(2) ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi(2) values were found with the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a 9-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.},
   keywords = {Akaike Information Criterion
Cerebral small vessel disease
Diffusion tensor imaging
Diffusion-weighted imaging
Intravoxel incoherent motion MRI
Model selection},
   ISSN = {1099-1492 (Electronic)
0952-3480 (Linking)},
   DOI = {10.1002/nbm.4905},
   url = {https://www.ncbi.nlm.nih.gov/pubmed/36637237},
   year = {2023},
   type = {Journal Article}
}

{"_id":"bHfSbLHGRcXZgpcNb","bibbaseid":"dietrich-cai-tuladhar-jacob-drenthen-jansen-marques-topalis-etal-integratedintravoxelincoherentmotiontensoranddiffusiontensorbrainmriinasinglefastacquisition-2023","author_short":["Dietrich, O.","Cai, M.","Tuladhar, A. M.","Jacob, M. A.","Drenthen, G. S.","Jansen, J. F. A.","Marques, J. P.","Topalis, J.","Ingrisch, M.","Ricke, J.","de Leeuw, F. E.","Duering, M.","Backes, W. H."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Dietrich"],"firstnames":["O."],"suffixes":[]},{"propositions":[],"lastnames":["Cai"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Tuladhar"],"firstnames":["A.","M."],"suffixes":[]},{"propositions":[],"lastnames":["Jacob"],"firstnames":["M.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Drenthen"],"firstnames":["G.","S."],"suffixes":[]},{"propositions":[],"lastnames":["Jansen"],"firstnames":["J.","F.","A."],"suffixes":[]},{"propositions":[],"lastnames":["Marques"],"firstnames":["J.","P."],"suffixes":[]},{"propositions":[],"lastnames":["Topalis"],"firstnames":["J."],"suffixes":[]},{"propositions":[],"lastnames":["Ingrisch"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Ricke"],"firstnames":["J."],"suffixes":[]},{"propositions":["de"],"lastnames":["Leeuw"],"firstnames":["F.","E."],"suffixes":[]},{"propositions":[],"lastnames":["Duering"],"firstnames":["M."],"suffixes":[]},{"propositions":[],"lastnames":["Backes"],"firstnames":["W.","H."],"suffixes":[]}],"title":"Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition","journal":"NMR Biomed","pages":"e4905","note":"Dietrich, Olaf Cai, Mengfei Tuladhar, Anil Man Jacob, Mina A Drenthen, Gerald S Jansen, Jacobus F A Marques, Jose P Topalis, Johanna Ingrisch, Michael Ricke, Jens de Leeuw, Frank-Erik Duering, Marco Backes, Walter H eng England NMR Biomed. 2023 Jan 13:e4905. doi: 10.1002/nbm.4905.","abstract":"The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data of the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 minutes and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm(2) and 6 noncollinear diffusion gradient directions for each b-value. 7 different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi(2) ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi(2) values were found with the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a 9-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.","keywords":"Akaike Information Criterion Cerebral small vessel disease Diffusion tensor imaging Diffusion-weighted imaging Intravoxel incoherent motion MRI Model selection","issn":"1099-1492 (Electronic) 0952-3480 (Linking)","doi":"10.1002/nbm.4905","url":"https://www.ncbi.nlm.nih.gov/pubmed/36637237","year":"2023","bibtex":"@article{RN315,\n author = {Dietrich, O. and Cai, M. and Tuladhar, A. M. and Jacob, M. A. and Drenthen, G. S. and Jansen, J. F. A. and Marques, J. P. and Topalis, J. and Ingrisch, M. and Ricke, J. and de Leeuw, F. E. and Duering, M. and Backes, W. H.},\n title = {Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition},\n journal = {NMR Biomed},\n pages = {e4905},\n note = {Dietrich, Olaf\nCai, Mengfei\nTuladhar, Anil Man\nJacob, Mina A\nDrenthen, Gerald S\nJansen, Jacobus F A\nMarques, Jose P\nTopalis, Johanna\nIngrisch, Michael\nRicke, Jens\nde Leeuw, Frank-Erik\nDuering, Marco\nBackes, Walter H\neng\nEngland\nNMR Biomed. 2023 Jan 13:e4905. doi: 10.1002/nbm.4905.},\n abstract = {The acquisition of intravoxel incoherent motion (IVIM) data and diffusion tensor imaging (DTI) data of the brain can be integrated into a single measurement, which offers the possibility to determine orientation-dependent (tensorial) perfusion parameters in addition to established IVIM and DTI parameters. The purpose of this study was to evaluate the feasibility of such a protocol with a clinically feasible scan time below 6 minutes and to use a model-selection approach to find a set of DTI and IVIM tensor parameters that most adequately describes the acquired data. Diffusion-weighted images of the brain were acquired at 3 T in 20 elderly participants with cerebral small vessel disease using a multiband echoplanar imaging sequence with 15 b-values between 0 and 1000 s/mm(2) and 6 noncollinear diffusion gradient directions for each b-value. 7 different IVIM-diffusion models with 4 to 14 parameters were implemented, which modeled diffusion and pseudo-diffusion as scalar or tensor quantities. The models were compared with respect to their fitting performance based on the goodness of fit (sum of squared fit residuals, chi(2) ) and their Akaike weights (calculated from the corrected Akaike information criterion). Lowest chi(2) values were found with the model with the largest number of model parameters. However, significantly highest Akaike weights indicating the most appropriate models for the acquired data were found with a 9-parameter IVIM-DTI model (with isotropic perfusion modeling) in normal-appearing white matter (NAWM), and with an 11-parameter model (IVIM-DTI with additional pseudo-diffusion anisotropy) in white matter with hyperintensities (WMH) and in gray matter (GM). The latter model allowed for the additional calculation of the fractional anisotropy of the pseudo-diffusion tensor (with a median value of 0.45 in NAWM, 0.23 in WMH, and 0.36 in GM), which is not accessible with the usually performed IVIM acquisitions based on three orthogonal diffusion-gradient directions.},\n keywords = {Akaike Information Criterion\nCerebral small vessel disease\nDiffusion tensor imaging\nDiffusion-weighted imaging\nIntravoxel incoherent motion MRI\nModel selection},\n ISSN = {1099-1492 (Electronic)\n0952-3480 (Linking)},\n DOI = {10.1002/nbm.4905},\n url = {https://www.ncbi.nlm.nih.gov/pubmed/36637237},\n year = {2023},\n type = {Journal Article}\n}\n\n","author_short":["Dietrich, O.","Cai, M.","Tuladhar, A. M.","Jacob, M. A.","Drenthen, G. S.","Jansen, J. F. A.","Marques, J. P.","Topalis, J.","Ingrisch, M.","Ricke, J.","de Leeuw, F. E.","Duering, M.","Backes, W. H."],"key":"RN315","id":"RN315","bibbaseid":"dietrich-cai-tuladhar-jacob-drenthen-jansen-marques-topalis-etal-integratedintravoxelincoherentmotiontensoranddiffusiontensorbrainmriinasinglefastacquisition-2023","role":"author","urls":{"Paper":"https://www.ncbi.nlm.nih.gov/pubmed/36637237"},"keyword":["Akaike Information Criterion Cerebral small vessel disease Diffusion tensor imaging Diffusion-weighted imaging Intravoxel incoherent motion MRI Model selection"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://raw.githubusercontent.com/jansenjfa1/bibbase.github.io/master/jansenjfa.bib","dataSources":["TCkfRWJAZvbLAZi29","uWMC5v24T7d5wGjeH"],"keywords":["akaike information criterion cerebral small vessel disease diffusion tensor imaging diffusion-weighted imaging intravoxel incoherent motion mri model selection"],"search_terms":["integrated","intravoxel","incoherent","motion","tensor","diffusion","tensor","brain","mri","single","fast","acquisition","dietrich","cai","tuladhar","jacob","drenthen","jansen","marques","topalis","ingrisch","ricke","de leeuw","duering","backes"],"title":"Integrated intravoxel incoherent motion tensor and diffusion tensor brain MRI in a single fast acquisition","year":2023}