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

BackgroundVoxel-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 methodsFluid-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).ResultsThe 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.ConclusionsFor 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

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph )

Correction: Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study.

<div><p>Motivation</p><p>Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and appearance, render it a difficult task.</p><p>Methods</p><p>In this article, nine classification methods (e.g. Generalized Linear Models, Random Decision Forests and Convolutional Neural Networks) are evaluated and compared with each other using 37 multiparametric MRI datasets of ischemic stroke patients in the sub-acute phase in terms of their accuracy and reliability for ischemic stroke lesion segmentation. Within this context, a multi-spectral classification approach is compared against mono-spectral classification performance using only FLAIR MRI datasets and two sets of expert segmentations are used for inter-observer agreement evaluation.</p><p>Results and Conclusion</p><p>The results of this study reveal that high-level machine learning methods lead to significantly better segmentation results compared to the rather simple classification methods, pointing towards a difficult non-linear problem. The overall best segmentation results were achieved by a Random Decision Forest and a Convolutional Neural Networks classification approach, even outperforming all previously published results. However, none of the methods tested in this work are capable of achieving results in the range of the human observer agreement and the automatic ischemic stroke lesion segmentation remains a complicated problem that needs to be explored in more detail to improve the segmentation results.</p></div

Gray Matter Growth is Accompanied by Increasing Blood Flow and Decreasing Apparent Diffusion Coefficient During Childhood

BACKGROUND AND PURPOSE: Normal values of gray matter volume, cerebral blood flow, and water diffusion have not been established for healthy children. We sought to determine reference values for age-dependent changes of these parameters in healthy children. MATERIALS AND METHODS: We retrospectively reviewed MR imaging data from 100 healthy children. Using an atlas-based approach, age-related normal values for regional CBF, apparent diffusion coefficient, and volume were determined for the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. RESULTS: All gray matter structures grew rapidly before the age of 10 years and then plateaued or slightly declined thereafter. The ADC of all structures decreased with age, with the most rapid changes occurring prior to the age of 5 years. With the exception of the globus pallidus, CBF increased rather linearly with age. CONCLUSIONS: Normal brain gray matter is characterized by rapid early volume growth and increasing CBF with concomitantly decreasing ADC. The extracted reference data that combine CBF and ADC parameters during brain growth may provide a useful resource when assessing pathologic changes in children

Gray Matter Growth is Accompanied by Increasing Blood Flow and Decreasing Apparent Diffusion Coefficient During Childhood

BACKGROUND AND PURPOSE: Normal values of gray matter volume, cerebral blood flow, and water diffusion have not been established for healthy children. We sought to determine reference values for age-dependent changes of these parameters in healthy children. MATERIALS AND METHODS: We retrospectively reviewed MR imaging data from 100 healthy children. Using an atlas-based approach, age-related normal values for regional CBF, apparent diffusion coefficient, and volume were determined for the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. RESULTS: All gray matter structures grew rapidly before the age of 10 years and then plateaued or slightly declined thereafter. The ADC of all structures decreased with age, with the most rapid changes occurring prior to the age of 5 years. With the exception of the globus pallidus, CBF increased rather linearly with age. CONCLUSIONS: Normal brain gray matter is characterized by rapid early volume growth and increasing CBF with concomitantly decreasing ADC. The extracted reference data that combine CBF and ADC parameters during brain growth may provide a useful resource when assessing pathologic changes in children

Hämodynamische Analyse und Klassifikation der Gefäßstrukturen bei Patienten mit zerebralen arteriovenösen Malformationen

Hintergrund: Eine zerebrale arteriovenöse Malformation (AVM) ist eine Gefäßmissbildung im Gehirn, die sich durch das Fehlen eines kapillaren Gefäßbettes mit abnormem Kurzschluss zwischen dem arteriellen und dem folgendem venösen System auszeichnet, dem sog. Nidus. Die veränderten hämodynamischen Bedingungen resultieren in neurologischen Ausfällen sowie in dysplastischen Veränderungen der zu- und abführenden Gefäße und daraus folgenden erhöhten Blutungsrisiko. Zielsetzung: Für die diagnostische Beurteilung der AVM sind Informationen über die individuelle Gefäßstruktur und die Hämodynamik von besonderem Interesse. In diesem Beitrag wird ein Verfahren zur Extraktion von Parametern zur Beschreibung der Hämodynamik präsentiert. Aufbauend hierauf werden Verfahren zur automatischen Detektion des Nidus der arteriovenösen Malformation sowie der zuleitenden (Feeder), ableitenden (Drainagevenen) und „en passage“-Gefäße vorgestellt. Als Eingabe hierfür dienen hochaufgelöste 3D- sowie zeitlich-räumliche 4D-MRT-Bildsequenzen. Methoden: Bei der vorgestellten Methode wird zunächst in den 3D-MRT-Bilddaten das Gefäßsystem semi-automatisch segmentiert. Auf Basis eines neuen Verfahrens zur Charakterisierung der Hämodynamik durch Bestimmung des Einflusszeitpunktes des Kontrastmittels mittels referenzbasierter Kurvenanpassung wird in einem weiteren Schritt in den zeitlich-räumlichen MR-Bildfolgen für jedes Voxel der zeitliche Signalverlauf analysiert. Zusätzlich wird die Flussgeschwindigkeit des Kontrastmittels diskret approximiert. Anschließend werden die extrahierten Parameterbilder mittels eines nicht-linearen Registrierungsverfahrens automatisch auf das segmentierte Gefäßsystem übertragen. Durch eine kombinierte Analyse der Intensität, der Geschwindigkeit und des relativen Einflusszeitpunktes des Blutes werden Gefäßstrukturen automatisch charakterisiert. Ergebnisse: Zur Evaluation der vorgestellte Methode standen 19 Datensätze von Patienten mit diagnostizierter AVM zur Verfügung. Durch Anwendung der neuen Methode zur Beschreibung der Einströmzeitpunkte konnten Artefakte in Form von starken zeitlichen Sprüngen zwischen den Einflusszeitpunkten benachbarter Voxel deutlich verringert werden. Die Detektion des Nidus wurde anhand von manuellen Segmentierungen validiert und ergab eine mittlere Volumenübereinstimmung von ca. 88%. Drainagevenen und Feeder konnten mit einer Genauigkeit von 95% detektiert werden. Schlussfolgerung: Die vorgestellte Methode ermöglicht eine robuste automatische Detektion des AVM-Nidus sowie eine Klassifikation der Gefäße. Eine visuelle Begutachtung durch erfahrene Neuroradiologen ergab, dass bei Verwendung der vorgestellten Methode zur Charakterisierung des Blutflusses mittels referenzbasierter Kurvenanpassung dieser besser dargestellt werden kann, als bei der Verwendung konventioneller Parameter. Die Detektion von zuleitenden und ableitenden Gefäßen unterstützt den Mediziner bei der räumlichen Beurteilung der arteriovenösen Malformation. Die Detektion der „en passage“-Gefäße ist besonders hinsichtlich der Planung von neurochirurgischen Eingriffen von hoher Bedeutung

Stroke subtype classification by geometrical descriptors of lesion shape.

Inference of etiology from lesion pattern in acute magnetic resonance imaging is valuable for management and prognosis of acute stroke patients. This study aims to assess the value of three-dimensional geometrical lesion-shape descriptors for stroke-subtype classification, specifically regarding stroke of cardioembolic origin.Stroke Etiology was classified according to ASCOD in retrospectively selected patients with acute stroke. Lesions were segmented on diffusion-weighed datasets, and descriptors of lesion shape quantified: surface area, sphericity, bounding box volume, and ratio between bounding box and lesion volume. Morphological measures were compared between stroke subtypes classified by ASCOD and between patients with embolic stroke of cardiac and non-cardiac source.150 patients (mean age 77 years; 95% CI, 65-80 years; median NIHSS 6, range 0-22) were included. Group comparison of lesion shape measures demonstrated that lesions caused by small-vessel disease were smaller and more spherical compared to other stroke subtypes. No significant differences of morphological measures were detected between patients with cardioembolic and non-cardioembolic stroke.Stroke lesions caused by small vessel disease can be distinguished from other stroke lesions based on distinctive morphological properties. However, within the group of embolic strokes, etiology could not be inferred from the morphology measures studied in our analysis

Correction: Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features.

[This corrects the article DOI: 10.1371/journal.pone.0228113.]