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Kerkelä, Leevi
20  Ergebnisse:
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1

Spherical convolutional neural networks can improve brain m..:

Kerkelä, Leevi ; Seunarine, Kiran ; Szczepankiewicz, Filip.
Frontiers in Neuroimaging.  3 (2024)  - p. , 2024
Link: https://doi.org/10.3389/..
 
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2

Correlation Tensor MRI deciphers underlying kurtosis source..:

Alves, Rita ; Henriques, Rafael Neto ; Kerkelä, Leevi...
NeuroImage.  247 (2022)  - p. 118833 , 2022
Link: https://doi.org/10.1016/..
 
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3

Comparative analysis of signal models for microscopic fract..:

Kerkelä, Leevi ; Nery, Fabio ; Callaghan, Ross...
NeuroImage.  242 (2021)  - p. 118445 , 2021
Link: https://doi.org/10.1016/..
 
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4

High‐resolution microscopic diffusion anisotropy imaging in..:

Yoo, Jiyoon ; Kerkelä, Leevi ; Hales, Patrick W...
Magnetic Resonance in Medicine.  87 (2021)  4 - p. 1903-1913 , 2021
Link: https://doi.org/10.1002/..
 
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5

Disimpy: A massively parallel Monte Carlo simulator for gen..:

Kerkelä, Leevi ; Nery, Fabio ; Hall, Matt.
Journal of Open Source Software.  5 (2020)  52 - p. 2527 , 2020
Link: https://doi.org/10.21105..
 
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6

In vivo demonstration of microscopic anisotropy in the huma..:

Nery, Fabio ; Szczepankiewicz, Filip ; Kerkelä, Leevi...
Magnetic Resonance in Medicine.  82 (2019)  6 - p. 2160-2168 , 2019
Link: https://doi.org/10.1002/..
 
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7

Validation and noise robustness assessment of microscopic a..:

Kerkelä, Leevi ; Henriques, Rafael Neto ; Hall, Matt G...
Magnetic Resonance in Medicine.  83 (2019)  5 - p. 1698-1710 , 2019
Link: https://doi.org/10.1002/..
 
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8

Microstructural parameter estimation using spherical convol..:

Kerkelä, Leevi ; Seunarine, Kiran ; Szczepankiewicz, Filip.
http://arxiv.org/abs/2211.09887.  , 2022
Link: http://arxiv.org/abs/221..
 
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9

Correlation Tensor MRI deciphers underlying kurtosis source..:

Alves, Rita ; Henriques, Rafael Neto ; Kerkelä, Leevi...
Alves , R , Henriques , R N , Kerkelä , L , Chavarrías , C , Jespersen , S N & Shemesh , N 2022 , ' Correlation Tensor MRI deciphers underlying kurtosis sources in stroke ' , NeuroImage , vol. 247 , 118833 . https://doi.org/10.1016/j.neuroimage.2021.118833.  , 2022
Link: https://pure.au.dk/porta..
 
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10

Improved reproducibility of diffusion kurtosis imaging usin..:

Kerkelä, Leevi ; Seunarine, Kiran ; Henriques, Rafael Neto..
http://arxiv.org/abs/2203.07327.  , 2022
Link: http://arxiv.org/abs/220..
 
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11

Example dataset of linear and planar tensor encoding in the..:

Kerkelä, Leevi
doi:10.5281/zenodo.4624865.  , 2021
Link: https://zenodo.org/recor..
 
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12

Quantifying Brain Microstructure with Diffusion MRI: An Ass..:

Kerkelä, Leevi
https://discovery.ucl.ac.uk/id/eprint/10133595/1/Leevi_Kerkela_PhD_thesis.pdf.  , 2021
Link: https://discovery.ucl.ac..
 
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13

Disimpy: A massively parallel Monte Carlo simulator for gen..:

Kerkelä, Leevi ; Nery, Fabio ; Hall, Matt G.
doi:10.5281/zenodo.4001686.  , 2020
Link: https://zenodo.org/recor..
 
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14

Disimpy: A massively parallel Monte Carlo simulator for gen..:

Kerkelä, Leevi ; Nery, Fabio ; Hall, Matt.
https://discovery.ucl.ac.uk/id/eprint/10185801/1/10.21105.joss.02527.pdf.  , 2020
Link: https://discovery.ucl.ac..
 
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15

Validation and noise robustness assessment of microscopic a..:

Kerkelä, Leevi ; Henriques, Rafael Neto ; Hall, Matt G..
http://arxiv.org/abs/1911.01148.  , 2019
Link: http://arxiv.org/abs/191..
 
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