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1.
Surg Radiol Anat ; 36(2): 111-24, 2014 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-23807198

RESUMEN

PURPOSE: Cerebral hemispheres represent both structural and functional asymmetry, which differs among right- and left-handers. The left hemisphere is specialised for language and task execution of the right hand in right-handers. We studied the corticospinal tract in right- and left-handers by diffusion tensor imaging and tractography. The present study aimed at revealing a morphological difference resulting from a region of interest (ROI) obtained by functional MRI (fMRI). METHODS: Twenty-five healthy participants (right-handed: 15, left-handed: 10) were enrolled in our assessment of morphological, functional and diffusion tensor MRI. Assessment of brain fibre reconstruction (tractography) was done using a deterministic algorithm. Fractional anisotropy (FA) and mean diffusivity (MD) were studied on the tractography traces of the reference slices. RESULTS: We observed a significant difference in number of leftward fibres based on laterality. The significant difference in regard to FA and MD was based on the slices obtained at different levels and the laterality index. We found left-hand asymmetry and right-hand asymmetry, respectively, for the MD and FA. CONCLUSIONS: Our study showed the presence of hemispheric asymmetry based on laterality index in right- and left-handers. These results are inconsistent with some studies and consistent with others. The reported difference in hemispheric asymmetry could be related to dexterity (manual skill).


Asunto(s)
Mapeo Encefálico/métodos , Encéfalo/anatomía & histología , Imagen de Difusión Tensora/métodos , Lateralidad Funcional/fisiología , Tractos Piramidales/anatomía & histología , Adolescente , Adulto , Algoritmos , Femenino , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Valores de Referencia , Adulto Joven
2.
Surg Radiol Anat ; 34(8): 709-19, 2012 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-22427107

RESUMEN

PURPOSE: Diffusion tensor imaging permits study of white matter fibre bundles; however, its main limitation is lack of validation on anatomical data, especially in crossing fibre regions. Our study aimed to compare four deterministic tractography algorithms used in clinical routine. We studied the corticospinal tract, the bundle mediating voluntary movement. Our study seeks to evaluate tractography provided by algorithms through comparative analysis by expert neuroradiologists. METHODS: MRI data from 15 right-handed volunteers (30.8 years) were studied. Regions of interest (ROIs) were segmented on morphological and functional MRI. Diffusion weighted images (15 directions) were performed, then for each voxel the tensor was estimated. Tractography of the corticospinal tract was performed using four fibre-tracking algorithms. Three numerical integration methods Euler, Runge-Kutta second (RK2) and fourth order (RK4), and a tensor deflection method (TEND). Quantitative measurement was performed. Qualitative evaluation was carried out by two expert neuroradiologists using Kappa test concordance. RESULTS: For the quantitative aspect, only RK2 and TEND presented no significant difference concerning the number of fibres (p = 0.58). There was no difference between right and left side for each algorithm. Regarding the qualitative aspects, there was a lack of fibres from the ventrolateral part of the functional ROIs. Comparison by expert neuroradiologists revealed low rather than high concordance. The algorithm ranked first was RK2 according to expert preferences. CONCLUSIONS: Different algorithms used in clinical routine failed to show realistic anatomical bundles. The most mathematically robust algorithm was not selected, nor was the algorithm defining more fibres. Validation of anatomical data provided by tractography remains a challenge.


Asunto(s)
Algoritmos , Imagen de Difusión Tensora/métodos , Tractos Piramidales/anatomía & histología , Adulto , Imagen Eco-Planar/métodos , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Masculino , Persona de Mediana Edad , Valores de Referencia , Adulto Joven
3.
Stem Cell Reports ; 17(10): 2349-2364, 2022 10 11.
Artículo en Inglés | MEDLINE | ID: mdl-36179692

RESUMEN

Combining multiple Parkinson's disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic neuron (mDAN) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control, and genetically unrelated iPSCs into mDANs. Using automated fluorescence microscopy in 384-well-plate format, we identified elevated levels of α-synuclein (αSyn) and serine 129 phosphorylation, reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDANs according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or αSyn levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient-derived SNCA gene triplication mDAN model which overexpresses αSyn. This phenotyping and classification strategy improves the practical exploitability of mDANs for disease modeling and the identification of novel LRRK2-associated drug targets.


Asunto(s)
Células Madre Pluripotentes Inducidas , Enfermedad de Parkinson , Neuronas Dopaminérgicas/metabolismo , Humanos , Células Madre Pluripotentes Inducidas/metabolismo , Proteína Coestimuladora de Linfocitos T Inducibles/genética , Proteína 2 Quinasa Serina-Treonina Rica en Repeticiones de Leucina/genética , Proteína 2 Quinasa Serina-Treonina Rica en Repeticiones de Leucina/metabolismo , Aprendizaje Automático , Mesencéfalo/metabolismo , Mutación , Enfermedad de Parkinson/genética , Enfermedad de Parkinson/terapia , Serina , alfa-Sinucleína/genética , alfa-Sinucleína/metabolismo
4.
Med Image Comput Comput Assist Interv ; 15(Pt 2): 163-70, 2012.
Artículo en Inglés | MEDLINE | ID: mdl-23286045

RESUMEN

We propose an iterative two-step method to compute a diffeomorphic non-rigid transformation between images of anatomical structures with rigid parts, without any user intervention or prior knowledge on the image intensities. First we compute spatially sparse, locally optimal rigid transformations between the two images using a new block matching strategy and an efficient numerical optimiser (BOBYQA). Then we derive a dense, regularised velocity field based on these local transformations using matrix logarithms and M-smoothing. These two steps are iterated until convergence and the final diffeomorphic transformation is defined as the exponential of the accumulated velocity field. We show our algorithm to outperform the state-of-the-art log-domain diffeomorphic demons method on dynamic cervical MRI data.


Asunto(s)
Puntos Anatómicos de Referencia , Vértebras Cervicales/patología , Interpretación de Imagen Asistida por Computador/métodos , Imagen por Resonancia Magnética/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Traumatismos de la Médula Espinal/patología , Técnica de Sustracción , Algoritmos , Humanos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
5.
Med Image Comput Comput Assist Interv ; 11(Pt 2): 122-30, 2008.
Artículo en Inglés | MEDLINE | ID: mdl-18982597

RESUMEN

In this paper we study the impact of denoising the raw high angular resolution diffusion imaging (HARDI) data with the Non-Local Means filter adapted to Rician noise (NLMr). We first show that NLMr filtering improves robustness of apparent diffusion coefficient (ADC) and orientation distribution function (ODF) reconstructions from synthetic HARDI datasets. Our results suggest that the NLMr filtering improve the quality of anisotropy maps computed from ADC and ODF and improve the coherence of q-ball ODFs with the underlying anatomy while not degrading angular resolution. These results are shown on a biological phantom with known ground truth and on a real human brain dataset. Most importantly, we show that multiple measurements of diffusion-weighted (DW) images and averaging these images along each direction can be avoided because NLMr filtering of the individual DW images produces better quality generalized fractional anisotropy maps and more accurate ODF fields than when computed from the averaged DW datasets.


Asunto(s)
Algoritmos , Artefactos , Imagen de Difusión por Resonancia Magnética/métodos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
6.
Artículo en Inglés | MEDLINE | ID: mdl-18982603

RESUMEN

Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods. However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans. High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical. In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data. Validation is performed on synthetic data and on real data for both conventional MR images and DT images. Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI.


Asunto(s)
Algoritmos , Artefactos , Imagen de Difusión por Resonancia Magnética/métodos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Imagen por Resonancia Magnética/métodos , Humanos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
7.
Med Image Comput Comput Assist Interv ; 10(Pt 2): 344-51, 2007.
Artículo en Inglés | MEDLINE | ID: mdl-18044587

RESUMEN

Diffusion tensor imaging (DT-MRI) is very sensitive to corrupting noise due to the non linear relationship between the diffusion-weighted image intensities (DW-MRI) and the resulting diffusion tensor. Denoising is a crucial step to increase the quality of the estimated tensor field. This enhanced quality allows for a better quantification and a better image interpretation. The methods proposed in this paper are based on the Non-Local (NL) means algorithm. This approach uses the natural redundancy of information in images to remove the noise. We introduce three variations of the NL-means algorithms adapted to DW-MRI and to DT-MRI. Experiments were carried out on a set of 12 diffusion-weighted images (DW-MRI) of the same subject. The results show that the intensity based NL-means approaches give better results in the context of DT-MRI than other classical denoising methods, such as Gaussian Smoothing, Anisotropic Diffusion and Total Variation.


Asunto(s)
Algoritmos , Artefactos , Imagen de Difusión por Resonancia Magnética/métodos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Interpretación Estadística de Datos , Humanos , Reproducibilidad de los Resultados , Sensibilidad y Especificidad
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