Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches

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Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches. / Jühling, Daniel; Rajashekar, Deepthi; Cheng, Bastian; Hilgetag, Claus Christian; Forkert, Nils Daniel; Werner, Rene.

in: FRONT NEUROSCI-SWITZ, Jahrgang 18, 17.01.2024, S. 1296357.

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@article{d2b4bf8f0cef496da1097251c709ee4b,
title = "Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches",
abstract = "BACKGROUND: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.MATERIALS AND METHODS: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).RESULTS: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.CONCLUSIONS: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.",
author = "Daniel J{\"u}hling and Deepthi Rajashekar and Bastian Cheng and Hilgetag, {Claus Christian} and Forkert, {Nils Daniel} and Rene Werner",
note = "Copyright {\textcopyright} 2024 J{\"u}hling, Rajashekar, Cheng, Hilgetag, Forkert and Werner.",
year = "2024",
month = jan,
day = "17",
doi = "10.3389/fnins.2024.1296357",
language = "English",
volume = "18",
pages = "1296357",
journal = "FRONT NEUROSCI-SWITZ",
issn = "1662-453X",
publisher = "Frontiers Media S. A.",

}

RIS

TY - JOUR

T1 - Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches

AU - Jühling, Daniel

AU - Rajashekar, Deepthi

AU - Cheng, Bastian

AU - Hilgetag, Claus Christian

AU - Forkert, Nils Daniel

AU - Werner, Rene

N1 - Copyright © 2024 Jühling, Rajashekar, Cheng, Hilgetag, Forkert and Werner.

PY - 2024/1/17

Y1 - 2024/1/17

N2 - BACKGROUND: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.MATERIALS AND METHODS: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).RESULTS: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.CONCLUSIONS: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

AB - BACKGROUND: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.MATERIALS AND METHODS: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).RESULTS: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.CONCLUSIONS: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

U2 - 10.3389/fnins.2024.1296357

DO - 10.3389/fnins.2024.1296357

M3 - SCORING: Journal article

C2 - 38298911

VL - 18

SP - 1296357

JO - FRONT NEUROSCI-SWITZ

JF - FRONT NEUROSCI-SWITZ

SN - 1662-453X

ER -