Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power.

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images after applying the inverse Fourier transform (IFT).

A fully Bayesian approach for comprehensive mapping of magnitude and phase brain activation in complex-valued fMRI data.

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data.

Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems.

The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical for nonlinear inverse problems, interpolation and embeddings are needed. To overcome this, we present a flexible framework to extend model-based learning directly to nonuniform meshes, by interpreting the mesh as a graph and formulating our network architectures using graph convolutional neural networks.

COMPLEX-VALUED TIME SERIES MODELING FOR IMPROVED ACTIVATION DETECTION IN FMRI STUDIES.

A complex-valued data-based model with th order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly-used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model.

Impacts of simultaneous multislice acquisition on sensitivity and specificity in fMRI.

Simultaneous multislice (SMS) imaging can be used to decrease the time between acquisition of fMRI volumes, which can increase sensitivity by facilitating the removal of higher-frequency artifacts and boosting effective sample size. The technique requires an additional processing step in which the slices are separated, or unaliased, to recover the whole brain volume. However, this may result in signal "leakage" between aliased locations, i.

Identifying the third dimension in 2D fluoroscopy to create 3D cardiac maps.

Three-dimensional cardiac mapping is important for optimal visualization of the heart during cardiac ablation for the treatment of certain arrhythmias. However, many hospitals and clinics worldwide cannot afford the high cost of the current mapping systems. We set out to determine if, using predefined algorithms, comparable 3D cardiac maps could be created by a new device that relies on data generated from single-plane fluoroscopy and patient recording and monitoring systems, without the need for costly equipment, infrastructure changes, or specialized catheters.

Complex-valued time-series correlation increases sensitivity in FMRI analysis.

Purpose: To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis.

Materials And Methods: The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data.

Separation of parallel encoded complex-valued slices (SPECS) from a single complex-valued aliased coil image.

Purpose: Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies.

Incorporating relaxivities to more accurately reconstruct MR images.

Purpose: To develop a mathematical model that incorporates the magnetic resonance relaxivities into the image reconstruction process in a single step.

Materials And Methods: In magnetic resonance imaging, the complex-valued measurements of the acquired signal at each point in frequency space are expressed as a Fourier transformation of the proton spin density weighted by Fourier encoding anomalies: T2(⁎), T1, and a phase determined by magnetic field inhomogeneity (∆B) according to the MR signal equation. Such anomalies alter the expected symmetry and the signal strength of the k-space observations, resulting in images distorted by image warping, blurring, and loss in image intensity.

Quantification of the statistical effects of spatiotemporal processing of nontask FMRI data.

Nontask functional magnetic resonance imaging (fMRI) has become one of the most popular noninvasive areas of brain mapping research for neuroscientists. In nontask fMRI, various sources of "noise" corrupt the measured blood oxygenation level-dependent signal. Many studies have aimed to attenuate the noise in reconstructed voxel measurements through spatial and temporal processing operations.

Quantifying the statistical impact of GRAPPA in fcMRI data with a real-valued isomorphism.

The interpolation of missing spatial frequencies through the generalized auto-calibrating partially parallel acquisitions (GRAPPA) parallel magnetic resonance imaging (MRI) model implies a correlation is induced between the acquired and reconstructed frequency measurements. As the parallel image reconstruction algorithms in many medical MRI scanners are based on the GRAPPA model, this study aims to quantify the statistical implications that the GRAPPA model has in functional connectivity studies. The linear mathematical framework derived in the work of Rowe , 2007, is adapted to represent the complex-valued GRAPPA image reconstruction operation in terms of a real-valued isomorphism, and a statistical analysis is performed on the effects that the GRAPPA operation has on reconstructed voxel means and correlations.

A statistical fMRI model for differential T2* contrast incorporating T1 and T2* of gray matter.

Relaxation parameter estimation and brain activation detection are two main areas of study in magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Relaxation parameters can be used to distinguish voxels containing different types of tissue whereas activation determines voxels that are associated with neuronal activity. In fMRI, the standard practice has been to discard the first scans to avoid magnetic saturation effects.

Ricean over Gaussian modelling in magnitude fMRI Analysis-Added Complexity with Negligible Practical Benefits.

It is well-known that Gaussian modeling of functional Magnetic Resonance Imaging (fMRI) magnitude time-course data, which are truly Rice-distributed, constitutes an approximation, especially at low signal-to-noise ratios (SNRs). Based on this fact, previous work has argued that Rice-based activation tests show superior performance over their Gaussian-based counterparts at low SNRs and should be preferred in spite of the attendant additional computational and estimation burden. Here, we revisit these past studies and after identifying and removing their underlying limiting assumptions and approximations, provide a more comprehensive comparison.

Diffusion-weighted magnetic resonance imaging of the prostate: improved robustness with stretched exponential modeling.

Purpose: This study aimed to compare the intraclass correlation coefficients of parameters estimated with stretched exponential and biexponential diffusion models of in vivo diffusion-weighted magnetic resonance imaging (MRI) of the prostate.

Methods: After the institutional review board issued a waiver of informed consent for this Health Insurance Portability and Accountability Act-compliant study, 25 patients with biopsy-proven prostate cancer underwent 3T endorectal MRI and diffusion-weighted MRI of the prostate at 10 b values (0, 45, 75, 105, 150, 225, 300, 600, 900, and 1200 s/mm). The full set of b values was collected twice within a single acquisition.

The SENSE-Isomorphism Theoretical Image Voxel Estimation (SENSE-ITIVE) model for reconstruction and observing statistical properties of reconstruction operators.

The acquisition of sub-sampled data from an array of receiver coils has become a common means of reducing data acquisition time in MRI. Of the various techniques used in parallel MRI, SENSitivity Encoding (SENSE) is one of the most common, making use of a complex-valued weighted least squares estimation to unfold the aliased images. It was recently shown in Bruce et al.

Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series.

As more evidence is presented suggesting that the phase, as well as the magnitude, of functional MRI (fMRI) time series may contain important information and that there are theoretical drawbacks to modeling functional response in the magnitude alone, removing noise in the phase is becoming more important. Previous studies have shown that retrospective correction of noise from physiologic sources can remove significant phase variance and that dynamic main magnetic field correction and regression of estimated motion parameters also remove significant phase fluctuations. In this work, we investigate the performance of physiologic noise regression in a framework along with correction for dynamic main field fluctuations and motion regression.

A statistical examination of SENSE image reconstruction via an isomorphism representation.

In magnetic resonance imaging, the parallel acquisition of subsampled spatial frequencies from an array of multiple receiver coils has become a common means of reducing data acquisition time. SENSitivity Encoding (SENSE) is a popular parallel image reconstruction model that uses a complex-valued least squares estimation process to unfold aliased images. In this article, the linear mathematical framework derived in Rowe et al.

Long-term vascular access ports as a means of sedative administration in a rodent fMRI survival model.

The purpose of this study is to develop a rodent functional magnetic resonance imaging (fMRI) survival model with the use of heparin-coated vascular access devices. Such a model would ease the administration of sedative agents, reduce the number of animals required in survival experiments and eliminate animal-to-animal variability seen in previous designs. Seven male Sprague-Dawley rats underwent surgical placement of an MRI-compatible vascular access port, followed by implantable electrode placement on the right median nerve.

Enhancing the utility of complex-valued functional magnetic resonance imaging detection of neurobiological processes through postacquisition estimation and correction of dynamic B(0) errors and motion.

Functional magnetic resonance imaging (fMRI) time series analysis is typically performed using only the magnitude portion of the data. The phase information remains unused largely due to its sensitivity to temporal variations in the magnetic field unrelated to the functional response of interest. These phase changes are commonly the result of physiologic processes such as breathing or motion either inside or outside the imaging field of view.

Quantitative analysis of arterial spin labeling FMRI data using a general linear model.

Arterial spin labeling techniques can yield quantitative measures of perfusion by fitting a kinetic model to difference images (tagged-control). Because of the noisy nature of the difference images investigators typically average a large number of tagged versus control difference measurements over long periods of time. This averaging requires that the perfusion signal be at a steady state and not at the transitions between active and baseline states in order to quantitatively estimate activation induced perfusion.

Regression Models for Identifying Noise Sources in Magnetic Resonance Images.

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models.

Complex-valued analysis of arterial spin labeling-based functional magnetic resonance imaging signals.

Cerebral blood flow-dependent phase differences between tagged and control arterial spin labeling images are reported. A biophysical model is presented to explain the vascular origin of this difference. Arterial spin labeling data indicated that the phase difference is largest when the arterial component of the signals is preserved but is greatly reduced as the arterial contribution is suppressed by postinversion delays or flow-crushing gradients.

Functional magnetic resonance imaging brain activation directly from k-space.

In functional magnetic resonance imaging (fMRI), the process of determining statistically significant brain activation is commonly performed in terms of voxel time series measurements after image reconstruction and magnitude-only time series formation. The image reconstruction and statistical activation processes are treated separately. In this manuscript, a framework is developed so that statistical analysis is performed in terms of the original, prereconstruction, complex-valued k-space measurements.