MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T2 -induced blurring in spin echo sequences

PURPOSE

An end-to-end differentiable 2D Bloch simulation is used to reduce T₂ induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network.

METHODS

Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard.

RESULTS

The optimized RARE was able to successfully lower T₂ -induced blurring for single-shot RARE sequences in proton density-weighted and T₂ -weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance.

CONCLUSIONS

This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T₂ -induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.

MR-double-zero - Proof-of-concept for a framework to autonomously discover MRI contrasts

PURPOSE

A framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed.

METHODS

A derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression.

RESULTS

It is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction.

CONCLUSIONS

New MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by 're-discovery' of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design.

The Achievement Motive in the Brain: BOLD Responses to Pictures of Challenging Activities Predicted by Implicit Versus Explicit Achievement Motives

The achievement motive refers to a preference for mastering challenges and competing with some standard of excellence. Along with affiliation and power motives, the achievement motive is typically considered to occur on the level of implicit versus explicit representations. Specifically, whereas implicit motives involve pictorial, emotional goal representations and facilitate corresponding action effortlessly, explicit motives involve propositional (“verbalized”) goal representations but need some effort to translate into action (McClelland et al., 1989). We used functional magnetic resonance imaging (fMRI) to investigate whether and to which degree the implicit and explicit achievement motives differentially predict blood-oxygen-level-dependent (BOLD) responses to pictures of individuals engaging in challenging activities. Whereas the implicit AM predicted activity in areas associated with emotion (orbitofrontal cortex) and visual processing (right dorsolateral prefrontal cortex, premotor and occipital cortices), the explicit AM predicted activity in areas associated with cognitive self-control or verbal goal processing (dorsal anterior cingulate cortex, left dorsolateral prefrontal cortex). The findings support the commonly assumed distinction between implicit and explicit motives with neuronal data. They also suggest that explicit motives require cognitive self-control to overcome potential lacks of motivation.

High-resolution neural network-driven mapping of multiple diffusion metrics leveraging asymmetries in the balanced steady-state free precession frequency profile

We propose to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, that is, mean, axial, and radial diffusivity (MD, AD, and RD), fractional anisotropy (FA), as well as the spherical coordinates (azimuth Φ and inclination ϴ) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased (e.g. in corpus callosum). The inclination ϴ was well predicted for anisotropic WM structures, while the azimuth Φ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultrahigh field strengths in clinically relevant scan times.

MRzero - Automated discovery of MRI sequences using supervised learning

PURPOSE

A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient exploration of novel MR sequence strategies.

METHODS

The scanning and reconstruction process is simulated end-to-end in terms of RF events, gradient moment events in x and y, and delay times, acting on the input model spin system given in terms of proton density, T 1 and T 2 , and Δ B 0 . As a proof of concept, we use both conventional MR images and T 1 maps as targets and optimize from scratch using the loss defined by data fidelity, SAR penalty, and scan time.

RESULTS

In a first attempt, MRzero learns gradient and RF events from zero, and is able to generate a target image produced by a conventional gradient echo sequence. Using a neural network within the reconstruction module allows arbitrary targets to be learned successfully. Experiments could be translated to image acquisition at the real system (3T Siemens, PRISMA) and could be verified in the measurements of phantoms and a human brain in vivo.

CONCLUSIONS

Automated MR sequence generation is possible based on differentiable Bloch equation simulations and a supervised learning approach.

Blind multirigid retrospective motion correction of MR images

PURPOSE

Physiological nonrigid motion is inevitable when imaging, e.g., abdominal viscera, and can lead to serious deterioration of the image quality. Prospective techniques for motion correction can handle only special types of nonrigid motion, as they only allow global correction. Retrospective methods developed so far need guidance from navigator sequences or external sensors. We propose a fully retrospective nonrigid motion correction scheme that only needs raw data as an input.

METHODS

Our method is based on a forward model that describes the effects of nonrigid motion by partitioning the image into patches with locally rigid motion. Using this forward model, we construct an objective function that we can optimize with respect to both unknown motion parameters per patch and the underlying sharp image.

RESULTS

We evaluate our method on both synthetic and real data in 2D and 3D. In vivo data was acquired using standard imaging sequences. The correction algorithm significantly improves the image quality. Our compute unified device architecture (CUDA)-enabled graphic processing unit implementation ensures feasible computation times.

CONCLUSION

The presented technique is the first computationally feasible retrospective method that uses the raw data of standard imaging sequences, and allows to correct for nonrigid motion without guidance from external motion sensors.

Blind retrospective motion correction of MR images

PURPOSE

Subject motion can severely degrade MR images. A retrospective motion correction algorithm, Gradient-based motion correction, which significantly reduces ghosting and blurring artifacts due to subject motion was proposed. The technique uses the raw data of standard imaging sequences; no sequence modifications or additional equipment such as tracking devices are required. Rigid motion is assumed.

METHODS

The approach iteratively searches for the motion trajectory yielding the sharpest image as measured by the entropy of spatial gradients. The vast space of motion parameters is efficiently explored by gradient-based optimization with a convergence guarantee.

RESULTS

The method has been evaluated on both synthetic and real data in two and three dimensions using standard imaging techniques. MR images are consistently improved over different kinds of motion trajectories. Using a graphics processing unit implementation, computation times are in the order of a few minutes for a full three-dimensional volume.

CONCLUSION

The presented technique can be an alternative or a complement to prospective motion correction methods and is able to improve images with strong motion artifacts from standard imaging sequences without requiring additional data.

Existential neuroscience: a functional magnetic resonance imaging investigation of neural responses to reminders of one's mortality

A considerable body of evidence derived from terror management theory indicates that the awareness of mortality represents a potent psychological threat engendering various forms of psychological defense. However, extant research has yet to examine the neurological correlates of cognitions about one's inevitable death. The present study thus investigated in 17 male participants patterns of neural activation elicited by mortality threat. To induce mortality threat, participants answered questions arranged in trial blocks that referred to fear of death and dying. In the control condition participants answered questions about fear of dental pain. Neural responses to mortality threat were greater than to pain threat in right amygdala, left rostral anterior cingulate cortex, and right caudate nucleus. We discuss implications of these findings for stimulating further research into the neurological correlates of managing existential fear.