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. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.

Fast magnetic resonance temperature imaging for focused ultrasound thermal therapy

The current standard for temperature sensitive imaging using magnetic resonance (MR) is 2-D, spoiled, fast gradient-echo (fGRE) phase-difference imaging exploiting temperature dependent changes in the proton resonance frequency (PRF). The echo-time (TE) for optimal sensitivity is larger than the typical repetition time (TR) of an fGRE sequence. Since TE must be less than TR in the fGRE sequence, this limits the technique's achievable sensitivity, spatial, and temporal resolution. This adversely affects both accuracy and volume coverage of the measurements. Accurate measurement of the rapid temperature changes associated with pulsed thermal therapies, such as high-intensity focused ultrasound (FUS), at optimal temperature sensitivity requires faster acquisition times than those currently available. ^ Use of fast MR acquisition strategies, such as interleaved echo-planar and spiral imaging, can provide the necessary increase in temporal performance and sensitivity while maintaining adequate signal-to-noise and in-plane spatial resolution. This research explored the adaptation and optimization of several fast MR acquisition methods for thermal monitoring of pulsed FUS thermal therapy. Temperature sensitivity, phase-difference noise and phase-difference to phase-difference-to noise ratio for the different pulse sequences were evaluated under varying imaging parameters in an agar gel phantom to establish optimal sequence parameters for temperature monitoring. The temperature sensitivity coefficient of the gel phantom was measured, allowing quantitative temperature extrapolations. ^ Optimized fast sequences were compared based on the ability to accurately monitor temperature changes at the focus of a high-intensity focused ultrasound unit, volume coverage, and contrast-to-noise ratio in the temperature maps. Operating parameters, which minimize complex phase-difference measurement errors introduced by use of the fast-imaging methods, were established. ^

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.