Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

© 2019 Haddad et al. The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data

Estudio del efecto del filtrado en resonancia magnética de difusión

En el mundo de la medicina, las imágenes de resonancia magnética de difusión (dMRI) son una herramienta de diagnóstico fundamental ya que nos proporcionan la posibilidad de visualizar los sistemas de fibras nerviosas que componen la sustancia blanca del cerebro. El inconveniente de esta herramienta es que los datos de las imágenes de resonancia magnética de difusión se ven damnificados por diversas fuentes de ruido que, irremediablemente, merman la calidad de las adquisiciones. Este ruido lo podemos modelar como un ruido de distribución Rician. En este Trabajo Fin de Grado se ha realizado un simulador de datos de resonancia magnética de difusión que permite el diseño de campos tensoriales y se estudian diferentes técnicas que tratan de eliminar el ruido en dMRI mediante la estimación de la difusión de resonancia magnética. En concreto, se presenta la implementación de varios filtros de ruido tales como el estimador de mínimo error cuadrático medio (LMMSE), el filtro Wiener, el filtro de promedios no locales sin sesgo (UNLM), el filtro de información anisotrópica conjunta y el filtro de eliminación del ruido Rician a través de la estabilización de la varianza. Estas estimaciones nos conceden la posibilidad de realizar una reconstrucción de las fibras que constituyen la materia blanca. Para obtener los tensores de difusión, se aplica a los volúmenes filtrados la técnica de imagen por resonancia magnética con tensores de difusión, conocida como DTI, la cual se trata de un modelo de resonancia magnética basado en la medida de la difusión de las partículas de agua en los tejidos. Esta difusión está establecida en la materia blanca por la orientación de los axones que componen las fibras nerviosas. Con posterioridad, a partir de la información obtenida por DTI, se pueden adquirir varias medidas escalares de la difusión tales como la anisotropía fraccional (FA), la anisotropía relativa (RA), la relación de volumen (VR), la difusividad radial (RD), la difusividad axial (AD), la difusividad media (MD) y el coeficiente de difusión aparente (ADC), las cuales nos permitirán alcanzar una mayor comprensión de los efectos del filtrado del ruido. Además, se pone a prueba el comportamiento de los filtros expuestos mediante simulaciones, es decir, se intenta observar la influencia que el ruido tiene sobre los datos de las imágenes de resonancias magnéticas. El entendimiento de los modelos de ruido que afectan a las imágenes de resonancia magnética de difusión puede ayudar a desarrollar sistemas para acrecentar la calidad final de dichas imágenes. Finalmente, este estudio es realizado con datos sintéticos para tener un mayor control sobre los datos, tanto de sujetos sanos como de sujetos con patologías neurológicas. Para analizar las zonas de fibras de neuronas dañadas, es decir, las zonas que presenten variaciones escalares, aplicamos la técnica de estadística espacial basada en tracto (TBSS) para comparar la sustancia blanca entre los sujetos sanos y con patologías.Grado en Ingeniería de Tecnologías de Telecomunicació

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

International audienceDiffusion-weighted imaging (DWI) allows imaging the geometry of water diffusion in biological tissues. However, DW images are noisy at high b-values and acquisitions are slow when using a large number of measurements, such as in Diffusion Spectrum Imaging (DSI). This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality. To capture the structure of DWI data, we use sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity. The method learns a dictionary of diffusion profiles on all the DW images at the same time and then scales to full brain data. Its performance is investigated with simulations and two real DSI datasets. We obtain better signal estimates from noisy measurements than by applying mirror symmetry through the q-space origin, Gaussian denoising or state-of- the-art non-local means denoising. Using a high-resolution dictionary learnt on another subject, we show that we can reduce the number of images acquired while still generating high resolution DSI data. Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible with approximately 40 measurements. This opens important perspectives for the connectomics community using DSI

Subject–Motion Correction in HARDI Acquisitions: Choices and Consequences

Diffusion-weighted imaging (DWI) is known to be prone to artifacts related to motion originating from subject movement, cardiac pulsation, and breathing, but also to mechanical issues such as table vibrations. Given the necessity for rigorous quality control and motion correction, users are often left to use simple heuristics to select correction schemes, which involves simple qualitative viewing of the set of DWI data, or the selection of transformation parameter thresholds for detection of motion outliers. The scientific community offers strong theoretical and experimental work on noise reduction and orientation distribution function (ODF) reconstruction techniques for HARDI data, where post-acquisition motion correction is widely performed, e.g., using the open-source DTIprep software (1), FSL (the FMRIB Software Library) (2), or TORTOISE (3). Nonetheless, effects and consequences of the selection of motion correction schemes on the final analysis, and the eventual risk of introducing confounding factors when comparing populations, are much less known and far beyond simple intuitive guessing. Hence, standard users lack clear guidelines and recommendations in practical settings. This paper reports a comprehensive evaluation framework to systematically assess the outcome of different motion correction choices commonly used by the scientific community on different DWI-derived measures. We make use of human brain HARDI data from a well-controlled motion experiment to simulate various degrees of motion corruption and noise contamination. Choices for correction include exclusion/scrubbing or registration of motion corrupted directions with different choices of interpolation, as well as the option of interpolation of all directions. The comparative evaluation is based on a study of the impact of motion correction using four metrics that quantify (1) similarity of fiber orientation distribution functions (fODFs), (2) deviation of local fiber orientations, (3) global brain connectivity via graph diffusion distance (GDD), and (4) the reproducibility of prominent and anatomically defined fiber tracts. Effects of various motion correction choices are systematically explored and illustrated, leading to a general conclusion of discouraging users from setting ad hoc thresholds on the estimated motion parameters beyond which volumes are claimed to be corrupted

Diffusion Weighted Image Denoising using overcomplete Local PCA

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovacion. This work has been also partially supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Manjón Herrera, JV.; Coupé, P.; Concha, L.; Buades, A.; Collins, L.; Robles Viejo, M. (2013). Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE. 8(9):1-12. https://doi.org/10.1371/journal.pone.0073021S11289Sundgren, P. C., Dong, Q., Gómez-Hassan, D., Mukherji, S. K., Maly, P., & Welsh, R. (2004). Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiology, 46(5), 339-350. doi:10.1007/s00234-003-1114-xJohansen-Berg, H., & Behrens, T. E. (2006). Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology, 19(4), 379-385. doi:10.1097/01.wco.0000236618.82086.01Jones DK, Basser PJ (2004) Squashing peanuts and smashing pumpkins: how noise distorts diffusion-weighted MR data. Magnetic Resonance in Medicine 52, 979–993.Chen, B., & Hsu, E. W. (2005). Noise removal in magnetic resonance diffusion tensor imaging. Magnetic Resonance in Medicine, 54(2), 393-401. doi:10.1002/mrm.20582Aja-Fernandez, S., Niethammer, M., Kubicki, M., Shenton, M. E., & Westin, C.-F. (2008). Restoration of DWI Data Using a Rician LMMSE Estimator. IEEE Transactions on Medical Imaging, 27(10), 1389-1403. doi:10.1109/tmi.2008.920609Basu S, Fletcher T, Whitaker R (2006) Rician noise removal in diffusion tensor MRI. MICCAI2006: 9,117–25.Hamarneh, G., & Hradsky, J. (2007). Bilateral Filtering of Diffusion Tensor Magnetic Resonance Images. IEEE Transactions on Image Processing, 16(10), 2463-2475. doi:10.1109/tip.2007.904964Xu, Q., Anderson, A. W., Gore, J. C., & Ding, Z. (2010). Efficient anisotropic filtering of diffusion tensor images. Magnetic Resonance Imaging, 28(2), 200-211. doi:10.1016/j.mri.2009.10.001Parker, G. J. M., Schnabel, J. A., Symms, M. R., Werring, D. J., & Barker, G. J. (2000). Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging. Journal of Magnetic Resonance Imaging, 11(6), 702-710. doi:10.1002/1522-2586(200006)11:63.0.co;2-aWeickert J, Brox T (2002) Diffusion and regularization of vector and matrix valued images. Saarland Department of Mathematics, Saarland University. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.195Wang, Z., Vemuri, B. C., Chen, Y., & Mareci, T. H. (2004). A Constrained Variational Principle for Direct Estimation and Smoothing of the Diffusion Tensor Field From Complex DWI. IEEE Transactions on Medical Imaging, 23(8), 930-939. doi:10.1109/tmi.2004.831218Reisert, M., & Kiselev, V. G. (2011). Fiber Continuity: An Anisotropic Prior for ODF Estimation. IEEE Transactions on Medical Imaging, 30(6), 1274-1283. doi:10.1109/tmi.2011.2112769Fillard, P., Pennec, X., Arsigny, V., & Ayache, N. (2007). Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics. IEEE Transactions on Medical Imaging, 26(11), 1472-1482. doi:10.1109/tmi.2007.899173Poon PK, Wei-Ren Ng, Sridharan V (2009) Image Denoising with Singular Value Decompositon and Principal Component Analysis. http://www.u.arizona.edu/~ppoon/ImageDenoisingWithSVD.pdfZhang, L., Dong, W., Zhang, D., & Shi, G. (2010). Two-stage image denoising by principal component analysis with local pixel grouping. Pattern Recognition, 43(4), 1531-1549. doi:10.1016/j.patcog.2009.09.023Deledalle C, Salmon J, Dalalyan A (2011) Image denoising with patch based PCA: local versus global. BMVC2011.Manjón JV, Thacker N, Lull JJ, Garcia-Martí G, Martí-Bonmatí L, et al.. (2009) Multicomponent MR Image Denoising. International Journal of Biomedical imaging, Article ID 756897.Bao, L., Robini, M., Liu, W., & Zhu, Y. (2013). Structure-adaptive sparse denoising for diffusion-tensor MRI. Medical Image Analysis, 17(4), 442-457. doi:10.1016/j.media.2013.01.006Strang G (1976) Linear Algebra and Its Applications Academic. New York,19802.Jolliffe IT (1986) Principal component analysis (Vol. 487). New York: Springer-Verlag.Manjón, J. V., Coupé, P., Buades, A., Louis Collins, D., & Robles, M. (2012). New methods for MRI denoising based on sparseness and self-similarity. Medical Image Analysis, 16(1), 18-27. doi:10.1016/j.media.2011.04.003Coifman R, Donoho DL (1995) Translation Invariant Denoising, Wavelets and Statistics. Anestis Antoniadis, ed. Springer Verlag Lecture Notes.Nowak, R. D. (1999). Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing, 8(10), 1408-1419. doi:10.1109/83.791966Koay CG, Basser PJ (2006) Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. J Magn Reson, 179,317–322.Coupé, P., Manjón, J. V., Gedamu, E., Arnold, D., Robles, M., & Collins, D. L. (2010). Robust Rician noise estimation for MR images. Medical Image Analysis, 14(4), 483-493. doi:10.1016/j.media.2010.03.001Close, T. G., Tournier, J.-D., Calamante, F., Johnston, L. A., Mareels, I., & Connelly, A. (2009). A software tool to generate simulated white matter structures for the assessment of fibre-tracking algorithms. NeuroImage, 47(4), 1288-1300. doi:10.1016/j.neuroimage.2009.03.077Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., & Barillot, C. (2008). An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images. IEEE Transactions on Medical Imaging, 27(4), 425-441. doi:10.1109/tmi.2007.906087Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Coupé P, Hellier P, Prima S, Kervrann C, Barillot C (2008) 3D Wavelet Subbands Mixing for Image Denoising. International Journal of Biomedical Imaging. Article ID 590183.Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208-S219. doi:10.1016/j.neuroimage.2004.07.051Basser, P. J., Mattiello, J., & Lebihan, D. (1994). Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. Journal of Magnetic Resonance, Series B, 103(3), 247-254. doi:10.1006/jmrb.1994.103

Non local spatial and angular matching : enabling higher spatial resolution diffusion MRI datasets through adaptive denoising

Diffusion magnetic resonance imaging (MRI) datasets suffer from low Signal-to-Noise Ratio (SNR), especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian nature of the noise, which in turn can lead to a false and biased estimation of the diffusion parameters. Additionally, the usage of in-plane acceleration techniques during the acquisition leads to a spatially varying noise distribution, which depends on the parallel acceleration method implemented on the scanner. This paper proposes a novel diffusion MRI denoising technique that can be used on all existing data, without adding to the scanning time. We first apply a statistical framework to convert both stationary and non stationary Rician and non central Chi distributed noise to Gaussian distributed noise, effectively removing the bias. We then introduce a spatially and angular adaptive denoising technique, the Non Local Spatial and Angular Matching (NLSAM) algorithm. Each volume is first decomposed in small 4D overlapping patches, thus capturing the spatial and angular structure of the diffusion data, and a dictionary of atoms is learned on those patches. A local sparse decomposition is then found by bounding the reconstruction error with the local noise variance. We compare against three other state-of-the-art denoising methods and show quantitative local and connectivity results on a synthetic phantom and on an in-vivo high resolution dataset. Overall, our method restores perceptual information, removes the noise bias in common diffusion metrics, restores the extracted peaks coherence and improves reproducibility of tractography on the synthetic dataset. On the 1.2 mm high resolution in-vivo dataset, our denoising improves the visual quality of the data and reduces the number of spurious tracts when compared to the noisy acquisition. Our work paves the way for higher spatial resolution acquisition of diffusion MRI datasets, which could in turn reveal new anatomical details that are not discernible at the spatial resolution currently used by the diffusion MRI community

Imagerie de diffusion en temps-réel (correction du bruit et inférence de la connectivité cérébrale)

La plupart des constructeurs de systèmes d'imagerie par résonance magnétique (IRM) proposent un large choix d'applications de post-traitement sur les données IRM reconstruites a posteriori, mais très peu de ces applications peuvent être exécutées en temps réel pendant l'examen. Mises à part certaines solutions dédiées à l'IRM fonctionnelle permettant des expériences relativement simples ainsi que d'autres solutions pour l'IRM interventionnelle produisant des scans anatomiques pendant un acte de chirurgie, aucun outil n'a été développé pour l'IRM pondérée en diffusion (IRMd). Cependant, comme les examens d'IRMd sont extrêmement sensibles à des perturbations du système hardware ou à des perturbations provoquées par le sujet et qui induisent des données corrompues, il peut être intéressant d'investiguer la possibilité de reconstruire les données d'IRMd directement lors de l'examen. Cette thèse est dédiée à ce projet innovant. La contribution majeure de cette thèse a consisté en des solutions de débruitage des données d'IRMd en temps réel. En effet, le signal pondéré en diffusion peut être corrompu par un niveau élevé de bruit qui n'est plus gaussien, mais ricien ou chi non centré. Après avoir réalisé un état de l'art détaillé de la littérature sur le bruit en IRM, nous avons étendu l'estimateur linéaire qui minimise l'erreur quadratique moyenne (LMMSE) et nous l'avons adapté à notre cadre de temps réel réalisé avec un filtre de Kalman. Nous avons comparé les performances de cette solution à celles d'un filtrage gaussien standard, difficile à implémenter car il nécessite une modification de la chaîne de reconstruction pour y être inséré immédiatement après la démodulation du signal acquis dans l'espace de Fourier. Nous avons aussi développé un filtre de Kalman parallèle qui permet d'appréhender toute distribution de bruit et nous avons montré que ses performances étaient comparables à celles de notre méthode précédente utilisant un filtre de Kalman non parallèle. Enfin, nous avons investigué la faisabilité de réaliser une tractographie en temps-réel pour déterminer la connectivité structurelle en direct, pendant l'examen. Nous espérons que ce panel de développements méthodologiques permettra d'améliorer et d'accélérer le diagnostic en cas d'urgence pour vérifier l'état des faisceaux de fibres de la substance blanche.Most magnetic resonance imaging (MRI) system manufacturers propose a huge set of software applications to post-process the reconstructed MRI data a posteriori, but few of them can run in real-time during the ongoing scan. To our knowledge, apart from solutions dedicated to functional MRI allowing relatively simple experiments or for interventional MRI to perform anatomical scans during surgery, no tool has been developed in the field of diffusion-weighted MRI (dMRI). However, because dMRI scans are extremely sensitive to lots of hardware or subject-based perturbations inducing corrupted data, it can be interesting to investigate the possibility of processing dMRI data directly during the ongoing scan and this thesis is dedicated to this challenging topic. The major contribution of this thesis aimed at providing solutions to denoise dMRI data in real-time. Indeed, the diffusion-weighted signal may be corrupted by a significant level of noise which is not Gaussian anymore, but Rician or noncentral chi. After making a detailed review of the literature, we extended the linear minimum mean square error (LMMSE) estimator and adapted it to our real-time framework with a Kalman filter. We compared its efficiency to the standard Gaussian filtering, difficult to implement, as it requires a modification of the reconstruction pipeline to insert the filter immediately after the demodulation of the acquired signal in the Fourier space. We also developed a parallel Kalman filter to deal with any noise distribution and we showed that its efficiency was quite comparable to the non parallel Kalman filter approach. Last, we addressed the feasibility of performing tractography in real-time in order to infer the structural connectivity online. We hope that this set of methodological developments will help improving and accelerating a diagnosis in case of emergency to check the integrity of white matter fiber bundles.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Collaborative patch-based super-resolution for diffusion-weighted images

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquis itions. A comparison with classical interpo- lation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in termsofimprovementsonimagereconstruction,fractiona lanisotropy(FA)estimation,generalizedFAandangular reconstruction for tensor and high angular resolut ion diffusion imaging (HARDI) models. Besides, fi rst results of reconstructed ultra high resolution DW images are presented at 0.6 × 0.6 × 0.6 mm 3 and0.4×0.4×0.4mm 3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fi ber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.We thank the reviewers for their useful comments that helped improve the paper. We also want to thank the Pr Louis Collins for proofreading this paper and his fruitful comments. Finally, we want to thank Martine Bordessoules for her help during image acquisition of DWI used to build the phantom. This work has been supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. This work has been also partially supported by the French National Agency for Research (Project MultImAD; ANR-09-MNPS-015-01) and by the Spanish grant TIN2011-26727 from the Ministerio de Ciencia e Innovacion. This work benefited from the use of FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), FiberNavigator (code.google.com/p/fibernavigator/), MRtrix software (http://www. brain.org.au/software/mrtrix/) and ITKsnap (www.itk.org).Coupé, P.; Manjón Herrera, JV.; Chamberland, M.; Descoteaux, M.; Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage. 83:245-261. https://doi.org/10.1016/j.neuroimage.2013.06.030S2452618

Local estimation of the noise level in MRI using structural adaptation

We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1-data corrupted with artificial noise. Simulation results are compared with a recently proposed estimate. The method was applied to a high-resolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Autism spectrum disorders (ASDs) denote a significant growing public health concern. Currently, one in 68 children has been diagnosed with ASDs in the United States, and most children are diagnosed after the age of four, despite the fact that ASDs can be identified as early as age two. The ultimate goal of this thesis is to develop a computer-aided diagnosis (CAD) system for the accurate and early diagnosis of ASDs using diffusion tensor imaging (DTI). This CAD system consists of three main steps. First, the brain tissues are segmented based on three image descriptors: a visual appearance model that has the ability to model a large dimensional feature space, a shape model that is adapted during the segmentation process using first- and second-order visual appearance features, and a spatially invariant second-order homogeneity descriptor. Secondly, discriminatory features are extracted from the segmented brains. Cortex shape variability is assessed using shape construction methods, and white matter integrity is further examined through connectivity analysis. Finally, the diagnostic capabilities of these extracted features are investigated. The accuracy of the presented CAD system has been tested on 25 infants with a high risk of developing ASDs. The preliminary diagnostic results are promising in identifying autistic from control patients