Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement.

Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping.

Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.

Background: Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.

Purpose: To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring.

Study Type: Retrospective.

Super-resolution musculoskeletal MRI using deep learning.

Purpose: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods.

Methods: We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies.

Optogenetic Functional MRI.

The investigation of the functional connectivity of precise neural circuits across the entire intact brain can be achieved through optogenetic functional magnetic resonance imaging (ofMRI), which is a novel technique that combines the relatively high spatial resolution of high-field fMRI with the precision of optogenetic stimulation. Fiber optics that enable delivery of specific wavelengths of light deep into the brain in vivo are implanted into regions of interest in order to specifically stimulate targeted cell types that have been genetically induced to express light-sensitive trans-membrane conductance channels, called opsins. fMRI is used to provide a non-invasive method of determining the brain's global dynamic response to optogenetic stimulation of specific neural circuits through measurement of the blood-oxygen-level-dependent (BOLD) signal, which provides an indirect measurement of neuronal activity.

Combining optogenetic stimulation and fMRI to validate a multivariate dynamical systems model for estimating causal brain interactions.

State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood.

Frequency-selective control of cortical and subcortical networks by central thalamus.

Central thalamus plays a critical role in forebrain arousal and organized behavior. However, network-level mechanisms that link its activity to brain state remain enigmatic. Here, we combined optogenetics, fMRI, electrophysiology, and video-EEG monitoring to characterize the central thalamus-driven global brain networks responsible for switching brain state.

High spatial resolution compressed sensing (HSPARSE) functional MRI.

Purpose: To propose a novel compressed sensing (CS) high spatial resolution functional MRI (fMRI) method and demonstrate the advantages and limitations of using CS for high spatial resolution fMRI.

Methods: A randomly undersampled variable density spiral trajectory enabling an acceleration factor of 5.3 was designed with a balanced steady state free precession sequence to achieve high spatial resolution data acquisition.

Optogenetic fMRI reveals distinct, frequency-dependent networks recruited by dorsal and intermediate hippocampus stimulations.

Although the connectivity of hippocampal circuits has been extensively studied, the way in which these connections give rise to large-scale dynamic network activity remains unknown. Here, we used optogenetic fMRI to visualize the brain network dynamics evoked by different frequencies of stimulation of two distinct neuronal populations within dorsal and intermediate hippocampus. Stimulation of excitatory cells in intermediate hippocampus caused widespread cortical and subcortical recruitment at high frequencies, whereas stimulation in dorsal hippocampus led to activity primarily restricted to hippocampus across all frequencies tested.

High-throughput optogenetic functional magnetic resonance imaging with parallel computations.

Optogenetic functional magnetic resonance imaging (of MRI) technology enables cell-type-specific, temporally precise neuronal control and the accurate, in vivo readout of the resulting activity across the entire brain. With the ability to precisely control excitation and inhibition parameters and accurately record the resulting activity, there is an increased need for a high-throughput method to bring the of MRI studies to their full potential. In this paper, an advanced system facilitating real-time fMRI with interactive control and analysis in a fraction of the MRI acquisition repetition time (TR) is proposed.