High-fidelity direct contrast synthesis from magnetic resonance fingerprinting.

PURPOSE: This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations. METHODS: To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. The performance of our proposed method is demonstrated on in vivo MRF scans from healthy volunteers. Quantitative metrics, including normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID), were used to evaluate the performance of the proposed method and compare it with others. RESULTS: In-vivo experiments demonstrated excellent image quality with respect to that of simulation-based contrast synthesis and previous DCS methods, both visually and according to quantitative metrics. We also demonstrate cases in which our trained model is able to mitigate the in-flow and spiral off-resonance artifacts typically seen in MRF reconstructions, and thus more faithfully represent conventional spin echo-based contrast-weighted images. CONCLUSION: We present N-DCSNet to directly synthesize high-fidelity multicontrast MR images from a single MRF acquisition. This method can significantly decrease examination time. By directly training a network to generate contrast-weighted images, our method does not require any model-based simulation and therefore can avoid reconstruction errors due to dictionary matching and contrast simulation (code available at:https://github.com/mikgroup/DCSNet).

Caterpillar traps: A highly flexible, distributed system of toroidal cable traps.

PURPOSE: Coil arrays are connected to the main MRI system with long, shielded coaxial cables. RF coupling of these cables to the main transmit coil can cause high shield currents, which pose risks of heating and RF burns. High-blocking resonant RF traps are placed at distinct positions along cables to mitigate these currents. Traditional traps are designed to be stiff to avoid changes in their resonant frequency, hindering the overall system flexibility. Instead of using a few high-blocking traps, we propose the use of caterpillar traps-a distributed system of small, elastic traps that cover the full length of cables. METHODS: We leverage an array of resonant toroids as traps, forming a caterpillar-like structure whereby bending only impacts individual traps minimally. Benchtop measurements are used to determine the blocking of caterpillar traps and show their robustness to bending. We also compare an anterior array system cable covered with caterpillar traps to a commercial cable with B1 + and heating measurements. RESULTS: Benchtop experiments with caterpillar traps demonstrate high robustness to bending. B1 + mapping experiments of an anterior array cable show improved blocking and flexibility compared to a commercial cable. CONCLUSION: Caterpillar traps provide sufficient attenuation to shield currents while allowing cable flexibility. Our distributed design can provide high blocking efficiency at different positions and orientations, even in cases where commercial cable traps cannot.

High-resolution multi-shot diffusion-weighted MRI combining markerless prospective motion correction and locally low-rank constrained reconstruction.

PURPOSE: Subject head motion is a major challenge in DWI, leading to image blurring, signal losses, and biases in the estimated diffusion parameters. Here, we investigate a combined application of prospective motion correction and spatial-angular locally low-rank constrained reconstruction to obtain robust, multi-shot, high-resolution diffusion-weighted MRI under substantial motion. METHODS: Single-shot EPI with retrospective motion correction can mitigate motion artifacts and resolve any mismatching of gradient encoding orientations; however, it is limited by low spatial resolution and image distortions. Multi-shot acquisition strategies could achieve higher resolution and image fidelity but increase the vulnerability to motion artifacts and phase variations related to cardiac pulsations from shot to shot. We use prospective motion correction with optical markerless motion tracking to remove artifacts and reduce image blurring due to bulk motion, combined with locally low-rank regularization to correct for remaining artifacts due to shot-to-shot phase variations. RESULTS: The approach was evaluated on healthy adult volunteers at 3 Tesla under different motion patterns. In multi-shot DWI, image blurring due to motion with 20 mm translations and 30° rotations was successfully removed by prospective motion correction, and aliasing artifacts caused by shot-to-shot phase variations were addressed by locally low-rank regularization. The ability of prospective motion correction to preserve the orientational information in DTI without requiring a reorientation of the b-matrix is highlighted. CONCLUSION: The described technique is proved to hold valuable potential for mapping brain diffusivity and connectivity at high resolution for studies in subjects/cohorts where motion is common, including neonates, pediatrics, and patients with neurological disorders.

DiSpect: Displacement spectrum imaging of flow and tissue perfusion using spin-labeling and stimulated echoes.

PURPOSE: We propose a new method, displacement spectrum (DiSpect) imaging, for probing in vivo complex tissue dynamics such as motion, flow, diffusion, and perfusion. Based on stimulated echoes and image phase, our flexible approach enables observations of the spin dynamics over short (milliseconds) to long (seconds) evolution times. METHODS: The DiSpect method is a Fourier-encoded variant of displacement encoding with stimulated echoes, which encodes bulk displacement of spins that occurs between tagging and imaging in the image phase. However, this method fails to capture partial volume effects as well as blood flow. The DiSpect variant mitigates this by performing multiple scans with increasing displacement-encoding steps. Fourier analysis can then resolve the multidimensional spectrum of displacements that spins exhibit over the mixing time. In addition, repeated imaging following tagging can capture dynamic displacement spectra with increasing mixing times. RESULTS: We demonstrate properties of DiSpect MRI using flow phantom experiments as well as in vivo brain scans. Specifically, the ability of DiSpect to perform retrospective vessel-selective perfusion imaging at multiple mixing times is highlighted. CONCLUSION: The DiSpect variant is a new tool in the arsenal of MRI techniques for probing complex tissue dynamics. The flexibility and the rich information it provides open the possibility of alternative ways to quantitatively measure numerous complex spin dynamics, such as flow and perfusion within a single exam.

Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging.

Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite differences, and low rank extensions. Though powerful, these image priors are phenomenological in nature and do not account for the mechanism behind the image formation. On the other hand, MRI signal dynamics are governed by physical laws, which can be explicitly modeled and used as priors for reconstruction. These explicit and implicit signal priors can be synergistically combined in an inverse problem framework to recover sharp, multi-contrast images from highly accelerated scans. Furthermore, the physics-based constraints provide a recipe for recovering quantitative, bio-physical parameters from the data. This article introduces physics-based modeling constraints in MRI and shows how they can be used in conjunction with compressed sensing for image reconstruction and quantitative imaging. We describe model-based quantitative MRI, as well as its linear subspace approximation. We also discuss approaches to selecting user-controllable scan parameters given knowledge of the physical model. We present several MRI applications that take advantage of this framework for the purpose of multi-contrast imaging and quantitative mapping.

An Enhanced Mechanistic Model For Capnography, With Application To CHF-COPD Discrimination.

Capnography records CO2 partial pressure in exhaled breath as a function of time or exhaled volume. Time-based capnography, which is our focus, is a point-of-care, noninvasive, effort-independent and widely available clinical monitoring modality. The generated waveform, or capnogram, reflects the ventilation-perfusion dynamics of the lung, and thus has value in the diagnosis of respiratory conditions such as chronic obstructive pulmonary disease (COPD). Effective discrimination between normal respiration and obstructive lung disease can be performed using capnogram-derived estimates of respiratory parameters in a simple mechanistic model of CO2 exhalation. We propose an enhanced mechanistic model that can capture specific capnogram characteristics in congestive heart failure (CHF) by incorporating a representation of the inertance associated with fluid in the lungs. The 4 associated parameters are estimated on a breath-by-breath basis by fitting the model output to the exhalations in the measured capnogram. Estimated parameters from 40 exhalations of 7 CHF and 7 COPD patients were used as a training set to design a quadratic discriminator in the parameter space, aimed at distinguishing between CHF and COPD patients. The area under the ROC curve for the training set was 0.94, and the corresponding equal-error-rate value of approximately 0.1 suggests classification accuracies of the order of 90% are attainable. Applying this discriminator without modification to 40 exhalations from each CHF and COPD patient in a fresh test set, and deciding on a simple majority basis whether the patient has CHF or COPD, results in correctly labeling all 8 out of the 8 CHF patients and 6 out of the 8 COPD patients in the test set, corresponding to a classification accuracy of 87.5%.

Subcellular probes for neurochemical recording from multiple brain sites.

Dysregulation of neurochemicals, in particular, dopamine, is epitomized in numerous debilitating disorders that impair normal movement and mood aspects of our everyday behavior. Neurochemical transmission is a neuron-specific process, and further exhibits region-specific signaling in the brain. Tools are needed to monitor the heterogeneous spatiotemporal dynamics of dopamine neurotransmission without compromising the physiological processes of the neuronal environment. We developed neurochemical probes that are ten times smaller than any existing dopamine sensor, based on the size of the entire implanted shaft and its sensing tip. The microfabricated probe occupies a spatial footprint (9 µm) coordinate with the average size of individual neuronal cells (∼10 µm). These cellular-scale probes were shown to reduce inflammatory response of the implanted brain tissue environment. The probes are further configured in the form of a microarray to permit electrochemical sampling of dopamine and other neurotransmitters at unprecedented spatial densities and distributions. Dopamine recording was performed concurrently from up to 16 sites in the striatum of rats, revealing a remarkable spatiotemporal contrast in dopamine transmission as well as site-specific pharmacological modulation. Collectively, the reported platform endeavors to enable high density mapping of the chemical messengers fundamentally involved in neuronal communication through the use of minimally invasive probes that help preserve the neuronal viability of the implant environment.