Gradient-echo MRI of resonance-frequency shift and T2* values exhibits unique tissue contrast and offers relevant physiological information. However, acquiring 3D-phase images and T2* maps [A1] with the standard spoiled gradient echo (SPGR) sequence is lengthy for routine imaging at high-spatial resolution and whole-brain coverage. In addition, with the standard SPGR sequence, optimal signal-to-noise ratio (SNR) cannot be achieved for every tissue type given their distributed resonance frequency and T2* value. To address these two issues, a SNR optimized multi-echo sequence with a stack-of-spiral acquisition is proposed and implemented for achieving fast and simultaneous acquisition of image phase and T2* maps. The analytical behavior of the phase SNR is derived as a function of resonance frequency, T2* and echo time. This relationship is utilized to achieve tissue optimized SNR by combining phase images with different echo times. Simulations and in vivo experiments were designed to verify the theoretical predictions. Using the multi-echo spiral acquisition, whole-brain coverage with 1 mm isotropic resolution can be achieved within 2.5 minutes, shortening the scan time by a factor of 8. The resulting multi-echo phase map shows similar SNR to that of the standard SPGR. The acquisition can be further accelerated with non-Cartesian parallel imaging. The technique can be readily extended to other multi-shot readout trajectories besides spiral. It may provide a practical acquisition strategy for high resolution and simultaneous 3D mapping of magnetic susceptibility and T2*.

Synthetic MRI is useful for the evaluation of T relaxation times of the breast cancers.

Multidelay arterial spin-labeling with transit time-corrected CBF showed developmental changes and regional differences of CBF in neonates and infants.

Purpose Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T1, T2, and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI. Methods The JPI and JDL methods are extended and combined to improve reconstruction for better‐quality, synthesized images. JPI is performed as a first step to estimate the missing k‐space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS‐Net) is modified and extended to form a joint variable splitting network (JVS‐Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under‐sampling using acceleration factors between 4 and 8. Results It is demonstrated that the normalized root‐mean‐square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast‐weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts. Conclusion Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.