Mark Limkeman | PubMed Health

PubMed2006

SmartPhantom--an fMRI simulator.

Many informatics tools have emerged to process the voluminous and complex data generated by functional magnetic resonance imaging (fMRI). The interpretation of fMRI exams is largely determined by these tools. However, their performance is hard to evaluate because there is no independent means of calibration. A novel fMRI calibration system called SmartPhantom has been developed to simulate functional blood oxygen level dependent (BOLD) imaging. SmartPhantom contains a quadrature radio frequency coil, comprising two perpendicular planar loops that can be externally activated or deactivated. The system is able to produce reasonably uniform signal enhancements in a calibration sample surrounded by the two loops during an MRI scan. The enhancement is controlled well in both magnitude and predefined timing and produces BOLD-like signals. Characteristics of SmartPhantom are discussed in detail, followed by a comparison of fMRI informatics tools. Two fMRI data sets are acquired with the SmartPhantom. One with high signal-to-noise ratio provides the calibration. Another with lower SNR is input into three software packages (BrainVoyager, FSL and Statistical Parametric Mapping 2) for data preprocessing and statistical analysis. Results from the three packages are compared in both sensitivity of detecting the activation and correlation between the predicted activation and calibration.

Calibration↗

PubMed2005

k-t GRAPPA: a k-space implementation for dynamic MRI with high reduction factor.

A novel technique called "k-t GRAPPA" is introduced for the acceleration of dynamic magnetic resonance imaging. Dynamic magnetic resonance images have significant signal correlations in k-space and time dimension. Hence, it is feasible to acquire only a reduced amount of data and recover the missing portion afterward. Generalized autocalibrating partially parallel acquisitions (GRAPPA), as an important parallel imaging technique, linearly interpolates the missing data in k-space. In this work, it is shown that the idea of GRAPPA can also be applied in k-t space to take advantage of the correlations and interpolate the missing data in k-t space. For this method, no training data, filters, additional parameters, or sensitivity maps are necessary, and it is applicable for either single or multiple receiver coils. The signal correlation is locally derived from the acquired data. In this work, the k-t GRAPPA technique is compared with our implementation of GRAPPA, TGRAPPA, and sliding window reconstructions, as described in Methods. The experimental results manifest that k-t GRAPPA generates high spatial resolution reconstruction without significant loss of temporal resolution when the reduction factor is as high as 4. When the reduction factor becomes higher, there might be a noticeable loss of temporal resolution since k-t GRAPPA uses temporal interpolation. Images reconstructed using k-t GRAPPA have less residue/folding artifacts than those reconstructed by sliding window, much less noise than those reconstructed by GRAPPA, and wider temporal bandwidth than those reconstructed by GRAPPA with residual k-space. k-t GRAPPA is applicable to a wide range of dynamic imaging applications and is not limited to imaging parts with quasi-periodic motion. Since only local information is used for reconstruction, k-t GRAPPA is also preferred for applications requiring real time reconstruction, such as monitoring interventional MRI.

Algorithms↗