Dynamic frame pairing in real-time freehand elastography.

Quasi-static ultrasound (US) elastography is now a well-established technique that involves acquiring US (RF/envelope) signals from an imaging plane before and after a small quasi-static compression to form axial strain elastograms (ASE). The image quality of the ASEs is a function of the applied axial strain. This relationship was extensively investigated and formalized in terms of strain filter in the literature.

Real-space processing of helical filaments in SPARX.

We present a major revision of the iterative helical real-space refinement (IHRSR) procedure and its implementation in the SPARX single particle image processing environment. We built on over a decade of experience with IHRSR helical structure determination and we took advantage of the flexible SPARX infrastructure to arrive at an implementation that offers ease of use, flexibility in designing helical structure determination strategy, and high computational efficiency. We introduced the 3D projection matching code which now is able to work with non-cubic volumes, the geometry better suited for long helical filaments, we enhanced procedures for establishing helical symmetry parameters, and we parallelized the code using distributed memory paradigm.

Effect of cocaine on structural changes in brain: MRI volumetry using tensor-based morphometry.

Magnetic resonance imaging (MRI) was performed in cocaine-dependent subjects to determine the structural changes in brain compared to non-drug using controls. Cocaine-dependent subjects and controls were carefully screened to rule out brain pathology of undetermined origin. Magnetic resonance images were analyzed using tensor-based morphometry (TBM) and voxel-based morphometry (VBM) without and with modulation to adjust for volume changes during normalization.

Improved cerebellar tissue classification on magnetic resonance images of brain.

Purpose: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.

Materials And Methods: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template.

Symmetric inverse consistent nonlinear registration driven by mutual information.

A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE).

Deep gray matter atrophy in multiple sclerosis: a tensor based morphometry.

Tensor based morphometry (TBM) was applied to determine the atrophy of deep gray matter (DGM) structures in 88 relapsing multiple sclerosis (MS) patients. For group analysis of atrophy, an unbiased atlas was constructed from 20 normal brains. The MS brain images were co-registered with the unbiased atlas using a symmetric inverse consistent nonlinear registration.

Mutual information driven inverse consistent nonlinear registration.

A fully symmetric nonlinear viscoelastic image registration method, under the demons paradigm is developed. The symmetric cost function includes mutual information (MI) as a similarity measure, regularization of the transformation, and inverse consistent constraint (ICC). Alternative strategy is used to minimize the divergent terms with different properties in the cost function to avoid the difficulties in balancing between the performance of registration and low Inverse Consistency Error (ICE).

Information measures-based intensity standardization of MRI.

Scan-to-scan intensity variation, even with the same imaging modality, affects a number of intensity-based image processing methods such as feature map based segmentation and non-rigid registration techniques that minimize sum of squared differences (SSD). Current intensity standardization techniques based on either percentile alignment or polynomial mapping suffer from a number of limitations. We present a novel intensity standardization techniques that exploits information measures obtained from the images.

Automated detection of videotaped neonatal seizures of epileptic origin.

Purpose: This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and random infant movements.

Methods: The motion of the infants' body parts was quantified by temporal motion-strength signals extracted from video segments by motion-segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities.

Automated detection of videotaped neonatal seizures based on motion segmentation methods.

Objective: This study was aimed at the development of a seizure detection system by training neural networks using quantitative motion information extracted by motion segmentation methods from short video recordings of infants monitored for seizures.

Methods: The motion of the infants' body parts was quantified by temporal motion strength signals extracted from video recordings by motion segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by direct thresholding, by clustering of the pixel velocities, and by clustering the motion parameters obtained by fitting an affine model to the pixel velocities.

Quantifying motion in video recordings of neonatal seizures by regularized optical flow methods.

This paper presents the development of regularized optical flow computation methods and an evaluation of their performance in the extraction of quantitative motion information from video recordings of neonatal seizures. A general formulation of optical flow computation is presented and a mathematical framework for the development of practical tools for computing optical flow is outlined. In addition, this paper proposes an alternative formulation of the optical flow problem that relies on a discrete approximation of a family of quadratic functionals.

Computerized motion analysis of videotaped neonatal seizures of epileptic origin.

Purpose: The main objective of this research is the development of automated video processing and analysis procedures aimed at the recognition and characterization of the types of neonatal seizures. The long-term goal of this research is the integration of these computational procedures into the development of a stand-alone automated system that could be used as a supplement in the neonatal intensive care unit (NICU) to provide 24-h per day noninvasive monitoring of infants at risk for seizures.

Methods: We developed and evaluated a variety of computational tools and procedures that may be used to carry out the three essential tasks involved in the development of a seizure recognition and characterization system: the extraction of quantitative motion information from video recordings of neonatal seizures in the form of motion-strength and motor-activity signals, the selection of quantitative features that convey some unique behavioral characteristics of neonatal seizures, and the training of artificial neural networks to distinguish neonatal seizures from random infant behaviors and to differentiate between myoclonic and focal clonic seizures.