Target-point combination of MR images

High dynamic range processing for magnetic resonance imaging

Purpose

To minimize feature loss in T₁- and T₂-weighted MRI by merging multiple MR images acquired at different T_R and T_E to generate an image with increased dynamic range.

Materials and Methods

High Dynamic Range (HDR) processing techniques from the field of photography were applied to a series of acquired MR images. Specifically, a method to parameterize the algorithm for MRI data was developed and tested. T₁- and T₂-weighted images of a number of contrast agent phantoms and a live mouse were acquired with varying T_R and T_E parameters. The images were computationally merged to produce HDR-MR images. All acquisitions were performed on a 7.05 T Bruker PharmaScan with a multi-echo spin echo pulse sequence.

Results

HDR-MRI delineated bright and dark features that were either saturated or indistinguishable from background in standard T₁- and T₂-weighted MRI. The increased dynamic range preserved intensity gradation over a larger range of T₁ and T₂ in phantoms and revealed more anatomical features in vivo.

Conclusions

We have developed and tested a method to apply HDR processing to MR images. The increased dynamic range of HDR-MR images as compared to standard T₁- and T₂-weighted images minimizes feature loss caused by magnetization recovery or low SNR.

Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients

The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.

A multidimensional nonlinear edge-preserving filter for magnetic resonance image restoration

The paper presents a multidimensional nonlinear edge-preserving filter for restoration and enhancement of magnetic resonance images (MRI). The filter uses both interframe (parametric or temporal) and intraframe (spatial) information to filter the additive noise from an MRI scene sequence. It combines the approximate maximum likelihood (equivalently, least squares) estimate of the interframe pixels, using MRI signal models, with a trimmed spatial smoothing algorithm, using a Euclidean distance discriminator to preserve partial volume and edge information. (Partial volume information is generated from voxels containing a mixture of different tissues.) Since the filter's structure is parallel, its implementation on a parallel processing computer is straightforward. Details of the filter implementation for a sequence of four multiple spin-echo images is explained, and the effects of filter parameters (neighborhood size and threshold value) on the computation time and performance of the filter is discussed. The filter is applied to MRI simulation and brain studies, serving as a preprocessing procedure for the eigenimage filter. (The eigenimage filter generates a composite image in which a feature of interest is segmented from the surrounding interfering features.) It outperforms conventional pre and post-processing filters, including spatial smoothing, low-pass filtering with a Gaussian kernel, median filtering, and combined vector median with average filtering.

Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach

We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.