Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

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

Advances in proton MR spectroscopy for quantifying pain associated metabolic changes in the human brain

In this work non-invasive in vivo detection of excitatory neurotransmitter glutamate andother cortical metabolites and their changes in the presence of acute and chronic pain wasperformed in the human brain with proton magnetic resonance spectroscopy (1H-MRS).This information can be used to better understand biochemical processes of cerebral painprocessing. Following introductory material, the first part of this thesis describes theimplemented method for post-processing of MR spectroscopic data to estimate absoluteconcentrations of the brain metabolites by considering the heterogeneous tissue compositionin the spectroscopic voxel. Phantom and in vivo brain studies demonstrated theadvantage of this method by reduced inter-individual variation of calculated metabolicconcentrations as well as enhanced quantitation accuracy. The second part of this workpresents the implemented method for the stimulus triggered data sampling permittingthe acquisition of in vivo 1H-MR spectra with a time resolution of few seconds. It wasshown that this method enables detection of changes of the neurotransmitter glutamateinduced by short acute pain stimuli. Considering these data, it was possible to characterisechanges of the glutamatergig neurotransmission associated with the sensation ofthe acute pain. The third part describes in vivo measurements on chronic pain patientsand healthy controls aiming to evaluate the changes of several brain metabolites in thedifferent cerebral pain processing regions associated with chronic pain. Patients revealeddecreased concentrations of the metabolic cell density markers and neurotransmitters indicatingthe degenerative processes as well as neurotransmitter dysfunctions, respectively.Results of this thesis indicate that pain induced metabolic changes in the human brainare traceable with the 1H-MRS by using experimental environment as it is used in clinicalroutine. This offers a broad spectrum of further applications aiming to explore thecerebral pain processing as well as to improve the specificity of the diagnostic assessmentof the chronic pain disease.Die vorliegende Arbeit beschreibt die Anwendung der Protonenmagnetresonanzspektroskopie(1H-MRS) zum nicht invasiven Nachweis von schmerzinduzierten Änderungen des erregenden Neurotransmitters Glutamat sowie anderer Metaboliten im menschlichen Gehirn. Diese Informationen könnten zu einem tieferen Verständnis der biochemischen Prozesse während der zerebralen Schmerzverarbeitung beitragen. Nach einer kurzen Einführung in die Problematik der Schmerzforschung sowie in die Grundlagen der MRSTechnikwird eine im Rahmen dieser Arbeit implementierte Methode zur Berechnung absoluter Metabolitenkonzentrationen unter Berücksichtigung der heterogenen Gewebezusammensetzung im spektroskopischen Volumen beschrieben. Der Vorteil dieses Verfahrens in Bezug auf die Verbesserung der Quantifizierungsgenauigkeit wird anhand von Ergebnissen spektroskopischer Messungen in einem Phantom sowie in Gehirnen gesunder Probanden belegt. Der zweite Teil befasst sich mit der Implementierung einer Technik zur reizgetriggerten Akquisition von MR Spektren, welche eine Abtastung verschiedener Stimulationszustände mit einer zeitlichen Auflösung von wenigen Sekunden zulässt und somit die Detektion dynamischer Änderungen von Metaboliten im Gehirn ermöglicht. Durch die Anwendung dieser Methode bei Messungen an gesunden Probanden konnten Änderungen im Glutamatstoffwechsel infolge einer Stimulation mit kurzen akuten Schmerzreizen nachgewiesen werden. Im dritten Teil der Arbeit wird schließlich eine an gesunden Probanden und Patienten mit chronischen Schmerzen durchgeführte Studievorgestellt, innerhalb derer die Auswirkungen der Schmerzchronifizierung auf den Metabolismus in schmerzverarbeitenden kortikalen Regionen untersucht wurden. Die Ergebnisse dieser Studie belegen die Hypothese, dass chronischer Schmerz mit Veränderungen imNeurotransmitterstoffwechsel sowie mit degenerativen Prozessen auf zellulärer Ebene einhergeht. Zusammenfassend lässt sich sagen, dass es mit der 1H-MRS möglich ist, schmerzinduzierte Änderungen der Metaboliten im menschlichen Gehirn unter Verwendung von klinischen Standartverfahren zu quantifizieren. Dies wiederum eröffnet ein breites Feld für weitere Untersuchungen, welche zur Erforschung der zerebralen Schmerzverarbeitung sowie zur Verbesserung der Spezifität diagnostischer Verfahren bei chronischen Schmerzen beitragen könnten

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain

In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 ​mm or smaller but degrades at 2 ​mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.Published versio