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Register a new userT2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions
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Relaxometry studies in preterm and at-term newborns have provided insight into brain microstructure, thus opening new avenues for studying normal brain development and supporting diagnosis in equivocal neurological situations. However, such quantitative techniques require long acquisition times and therefore cannot be straightforwardly translated to in utero brain developmental studies. In clinical fetal brain magnetic resonance imaging routine, 2D low-resolution T2-weighted fast spin echo sequences are used to minimize the effects of unpredictable fetal motion during acquisition. As super-resolution techniques make it possible to reconstruct a 3D high-resolution volume of the fetal brain from clinical low-resolution images, their combination with quantitative acquisition schemes could provide fast and accurate T2 measurements. In this context, the present work demonstrates the feasibility of using super-resolution reconstruction from conventional T2-weighted fast spin echo sequences for 3D isotropic T2 mapping. A quantitative magnetic resonance phantom was imaged using a clinical T2-weighted fast spin echo sequence at variable echo time to allow for super-resolution reconstruction at every echo time and subsequent T2 mapping of samples whose relaxometric properties are close to those of fetal brain tissue. We demonstrate that this approach is highly repeatable, accurate and robust when using six echo times (total acquisition time under 9 minutes) as compared to gold-standard single-echo spin echo sequences (several hours for one single 2D slice).
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Computed Tomography (CT) is an advanced imaging technology used in many important applications. Here we present a deep-learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high resolution (HR) CT images. The method synergistically combines a SR model in sinogram domain, a deblur model in image domain, and the iterative framework into a CT SR algorithm super resolution and deblur based iterative reconstruction (SADIR). We incorporated the CT domain knowledge into the SADIR and unrolled it into a DL network (SADIR Net). The SADIR Net is a zero shot learning (ZSL) network, which can be trained and tested with a single sinogram in the test time. The SADIR was evaluated via SR CT imaging of a Catphan700 physical phantom and a biological ham, and its performance was compared to the other state of the art (SotA) DL-based methods. The results show that the zero-shot SADIR-Net can indeed provide a performance comparable to the other SotA methods for CT SR reconstruction, especially in situations where training data is limited. The SADIR method can find use in improving CT resolution beyond hardware limits or lowering requirement on CT hardware.
To rapidly obtain high resolution T2, T2* and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient echo-planar imaging (EPI) sequence for quantitative mapping. The acquisition includes multiple T2*-, T2- and T2-weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate geometric distortion. A self-supervised MR-Self2Self (MR-S2S) neural network (NN) was utilized to perform denoising after BUDA reconstruction to boost SNR. Employing Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on BUDA-SAGE volumes acquired with 2 mm slice thickness. Quantitative T2 and T2* maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion (NDI) on the gradient echoes. Starting from the estimated R2 and R2* maps, R2 information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps. In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution multi-contrast images and quantitative T2 and T2* maps, as well as yielding para- and dia-magnetic susceptibility maps. Derived quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements. BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enabled rapid, distortion-free, whole-brain T2, T2* mapping at 1 mm3 isotropic resolution in 90 seconds.
The generation of high frequency oscillatory magnetic fields represents a fundamental component underlying the successful implementation of neutron resonant spin-echo spectrometers, a class of instrumentation critical for the high-resolution extraction of dynamical excitations (structural and magnetic) in materials. In this paper, the setup of the resonant circuits at the longitudinal resonant spin-echo spectrometer RESEDA is described in comprehensive technical detail. We demonstrate that these circuits are capable of functioning at frequencies up to 3.6 MHz and over a broad bandwidth down to 35 kHz using a combination of signal generators, amplifiers, impedance matching transformers, and a carefully designed cascade of tunable capacitors and customized coils.
Background: RSN whole-heart CMRA is a technique that estimates and corrects for respiratory motion. However, RSN has been limited to a 1D rigid correction which is often insufficient for patients with complex respiratory patterns. The goal of this work is therefore to improve the robustness and quality of 3D radial CMRA by incorporating both 3D motion information and nonrigid intra-acquisition correction of the data into a framework called focused navigation (fNAV). Methods: We applied fNAV to 500 data sets from a numerical simulation, 22 healthy volunteers, and 549 cardiac patients. We compared fNAV to RSN and respiratory resolved XD-GRASP reconstructions of the same data and recorded reconstruction times. Motion accuracy was measured as the correlation between fNAV and ground truth for simulations, and fNAV and image registration for in vivo data. Vessel sharpness was measured using Soap-Bubble. Finally, image quality analysis was performed by a blinded expert reviewer who chose the best image for each data set. Results The reconstruction time for fNAV images was significantly higher than RSN (6.1 +/- 2.1 minutes vs 1.4 +/- 0.3, minutes, p<0.025) but significantly lower than XD-GRASP (25.6 +/- 7.1, minutes, p<0.025). There is high correlation between the fNAV, and reference displacement estimates across all data sets (0.73 +/- 0.29). For all data, fNAV lead to significantly sharper vessels than all other reconstructions (p < 0.01). Finally, a blinded reviewer chose fNAV as the best image in 239 out of 571 cases (p = 10-5). Conclusion: fNAV is a promising technique for improving free-breathing 3D radial whole-heart CMRA. This novel approach to respiratory self-navigation can derive 3D nonrigid motion estimations from an acquired 1D signal yielding statistically significant improvement in image sharpness relative to 1D translational correction as well as XD-GRASP reconstructions.
Multi-shot echo planar imaging (msEPI) is a promising approach to achieve high in-plane resolution with high sampling efficiency and low T2* blurring. However, due to the geometric distortion, shot-to-shot phase variations and potential subject motion, msEPI continues to be a challenge in MRI. In this work, we introduce acquisition and reconstruction strategies for robust, high-quality msEPI without phase navigators. We propose Blip Up-Down Acquisition (BUDA) using interleaved blip-up and -down phase encoding, and incorporate B0 forward-modeling into Hankel structured low-rank model to enable distortion- and navigator-free msEPI. We improve the acquisition efficiency and reconstruction quality by incorporating simultaneous multi-slice acquisition and virtual-coil reconstruction into the BUDA technique. We further combine BUDA with the novel RF-encoded gSlider acquisition, dubbed BUDA-gSlider, to achieve rapid high isotropic-resolution MRI. Deploying BUDA-gSlider with model-based reconstruction allows for distortion-free whole-brain 1mm isotropic T2 mapping in about 1 minute. It also provides whole-brain 1mm isotropic diffusion imaging with high geometric fidelity and SNR efficiency. We finally incorporate sinusoidal wave gradients during the EPI readout to better use coil sensitivity encoding with controlled aliasing.
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