Purpose Parallel imaging allows the reconstruction of images from undersampled multi-coil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k-space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm Theory and Methods A theoretical analysis shows: 1. The correlations in k-space are encoded in the null space of a calibration matrix. 2. Both approaches restrict the solution to a subspace spanned by the sensitivities. 3. The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods Results The existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA. Conclusion In this paper the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both.

Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require trade offs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ1/ℓ2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the ℓ1/ℓ2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning based CS schemes as well as with deeplearning based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which is two orders of magnitude faster than current state-of-the-art CS-MRI schemes.

Purpose A new acquisition and reconstruction method called T2 Shuffling is presented for volumetric fast spin-echo (3D FSE) imaging. T2 Shuffling reduces blurring and recovers many images at multiple T2 contrasts from a single acquisition at clinically feasible scan times (6 to 7 minutes). Theory and Methods The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train. Scan efficiency is improved by acquiring data during the transient signal decay and by increasing echo train lengths without loss in SNR. By (1) randomly shuffling the phase encode view ordering, (2) constraining the temporal signal evolution to a low-dimensional subspace, and (3) promoting spatio-temporal correlations through locally low rank regularization, a time series of virtual echo time images is recovered from a single scan. A convex formulation is presented that is robust to partial voluming and RF field inhomogeneity. Results Retrospective under-sampling and in vivo scans confirm the increase in sharpness afforded by T2 Shuffling. Multiple image contrasts are recovered and used to highlight pathology in pediatric patients. A proof-of-principle method is integrated into a clinical musculoskeletal imaging workflow. Conclusion The proposed T2 Shuffling method improves the diagnostic utility of 3D FSE by reducing blurring and producing multiple image contrasts from a single scan.

Epigenetic “clocks” can now surpass chronological age in accuracy for estimating biological age. Here, we use four such age estimators to show that epigenetic aging can be reversed in humans. Using a protocol intended to regenerate the thymus, we observed protective immunological changes, improved risk indices for many age‐related diseases, and a mean epigenetic age approximately 1.5 years less than baseline after 1 year of treatment (−2.5‐year change compared to no treatment at the end of the study). The rate of epigenetic aging reversal relative to chronological age accelerated from −1.6 year/year from 0–9 month to −6.5 year/year from 9–12 month. The GrimAge predictor of human morbidity and mortality showed a 2‐year decrease in epigenetic vs. chronological age that persisted six months after discontinuing treatment. This is to our knowledge the first report of an increase, based on an epigenetic age estimator, in predicted human lifespan by means of a currently accessible aging intervention.

Refocused steady-state free precession (SSFP) imaging sequences have recently regained popularity as faster gradient hardware has allowed shorter repetition times, thereby reducing SSFP's sensitivity to off-resonance effects. Although these sequences offer fast scanning with good signal-to-noise efficiency, the "transient response," or time taken to reach a steady-state, can be long compared with the total imaging time, particularly when using 2D sequences. This results in lost imaging time and has made SSFP difficult to use for real-time and cardiac-gated applications. Key words: MRI; SSFP; FISP; fast imaging; transient responseRefocused steady-state free precession (SSFP) sequences have recently gained popularity in magnetic resonance imaging, due to improved gradient hardware (1-4). One drawback with such sequences is the slow and often oscillatory signal progression as a steady-state is established. Imaging during this transient period can result in image artifacts. Alternatively, waiting for magnetization to reach a steady-state reduces the scan-time efficiency of the imaging method.In this article we characterize the transient response of arbitrary steady-state sequences. Using a linear systems analysis, we propose a two-stage method to "catalyze" or speed up the evolution of a steady-state. The two stages, "magnitude scaling" and "direction selection," first scale the magnetization to its steady-state length and then direct it to the steady-state direction. Although the method we present uses both stages, each is useful by itself and the magnitude-scaling stage can be combined with other methods. We have simulated the two-stage catalyzing method and successfully tested it experimentally.The Theory section presents a general analysis of the transient response of steady-state sequences. In Methods and Results we use this analysis to develop and test a flexible two-stage method for catalyzing the steady state. THEORYThe steady-state magnetization in steady-state sequences is a nontrivial function of many parameters. Before attempting to generate a sequence that catalyzes the steadystate, it is useful to characterize both the steady-state and the transient response, and to attempt to gain some intuition about how they are affected by the different parameters. Steady-State SignalIn any periodic sequence of RF excitations and gradients, the magnetization change from one period to the next can be expressed in the form of a discrete-time system:where M k is a 3D vector representing the magnetization (M x , M y , M z ) at the k th period. B is a 3D vector and A is a 3 ϫ 3 matrix. Typically, A and B are functions of T 1 , T 2 , and various rotation and precession angles. In the steadystate, M kϩ1 ϭ M k ϭ M ss and Eq.[1] is easily solved for the steady-state magnetization, M ss :where I is the 3 ϫ 3 identity matrix. The derivation of A and B is relatively simple for most sequences and is shown for a refocused-SSFP sequence in Appendix A. Transient ResponseThe transient response is the magnetization during the initi...