Linda Vu, Simon So, Sergei Obruchkov, Andrew Cenko, Jeff Meade, Ken Bradshaw, Claude Lemaire, Hartwig Peemoeller, Sarbast Rasheed, Arsen Hajian, Jae Kim, Cameron Piron
In magnetic resonance imaging (MRI), an object within a field-of-view (FOV) is spatially encoded with a broad
spectrum of frequency components generating signals that decohere with one another to create a decaying echo
with a large peak amplitude. The echo is short and decays at a rapid rate relative to the readout period when
performing high resolution imaging of a sizable object where many frequency components are encoded resulting
in faster decoherence of the generated signals. This makes it more difficult to resolve fine details as the echo
quickly decays down to the quantization limit. Samples collected away from the peak signal, which are required
to produce high resolution images, have very low amplitudes and therefore, poor dynamic range.
We propose a novel data acquisition system, Calculated Readout in Spectral Parallelism (CRISP), that
spectrally separates the radio frequency (RF) signal into multiple narrowband channels before digitization. The
frequency bandwidth of each channel is smaller than the FOV and centered over a part of the image with minimal
overlap with the other channels. The power of the corresponding temporal signal in each channel is reduced
and spread across a broader region in time with a slower decay rate. This allows the signal from each channel
to be independently amplified such that a larger portion of the signal is digitized at higher bits. Therefore, the
dynamic range of the signal is improved and sensitivity to quantization noise is reduced. We present a realization
of CRISP using inexpensive analog filters and preliminary results from high resolution images.
In MRI, non-rectilinear sampling trajectories are applied in k-space to enable faster imaging. Traditional image
reconstruction methods such as a fast Fourier transform (FFT) can not process datasets sampled in non-rectilinear
forms (e.g., radial, spiral, random, etc.) and more advanced algorithms are required. The Fourier reduction of
optical interferometer data (FROID) algorithm is a novel image reconstruction method1-3 proven to be successful
in reconstructing spectra from sparsely and unevenly sampled astronomical interferometer data. The framework
presented allows a priori information, such as the locations of the sampled points, to be incorporated into the
reconstruction of images. In this paper, the FROID algorithm has been adapted and implemented to reconstruct
magnetic resonance (MR) images from data acquired in k-space where the sampling positions are known. Also,
simulated data, including randomly sampled data, are tested and analyzed.
In this paper, we describe a practical implementation of an image reconstruction method designed to generate
a map of the brightness distribution from data consisting of squared visibilities and complex closure amplitudes
resulting from observations of an astronomical target with a broadband, multichannel, spatial optical interferometer.
Given the data, the method estimates the true brightness distribution with a model sampled on a
rectangular grid of discrete positions on the sky with the assumption that the model intensities in the region
not defined by the discrete positions being described by bilinear interpolation of the discrete intensities. The developed
image reconstruction method has been applied to real observational data obtained from existing optical
interferometer facilities.
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