Magnetic resonance imaging (MRI) offers a unique opportunity to non-invasively study the human brain, equipped with a wealth of contrasts (to disentangle structures and functions), and a favourable window of spatiotemporal resolution.
But to expand its clinical and neuroscientific scope, MRI has to become faster and more robust.
A key ingredient to this end is a mechanistic understanding of the imaging physics, and its consideration in the way we acquire and reconstruct MR images. This comprises
- the choice of optimal sequence and sampling strategy for the target application (e.g., spiral trajectories for short echo-time)
- a detailed characterization of both hardware imperfections (e.g., heating/field drift and trajectory errors) and subject-related noise sources (e.g., breathing, motion) giving rise to image artifacts,
- the accurate inclusion of this information into an expanded signal model of the MR imaging process and
- advanced image reconstruction techniques capable of inverting these intricate models.
This integrated approach leads to better imaging data tailored to the analysis and diagnostic purpose of the MR exams. The characterization of each scan's imaging peculiarities allows for better comparability of multi-site studies in the era of big data. At the other end of the spectrum, personalized medicine requires individualized MR imaging protocols that are enabled by flexible and extendable signal models and reconstructions.
- Lars Kasper
- Brian Nghiem
- Zhe Wu ("Tim")
- Sriranga Kashyap
- Optimal sampling (wave-CAIPI, spirals)
- Motion Correction
- Field Monitoring
- System Characterization / Inter-Site reliability
Diffusion Spiral MRI (Lars Kasper)
Diffusion MRI is a widely used diagnostic tool in Neuroradiology, as well as an established measure of structural connectivity in neuroscientific research. However, it suffers from low signal-to-noise ratio (SNR) and geometric distortions, which often reduces its practical diagnostic value or enforces prolonged scantime - which is burdensome for many patient populations.
Spiral imaging, due to its shorter echo time and faster readout, remedies both of these drawbacks. Previous research has shown that by embracing spirals in the mechanistic improvement cycle for MRI outlined above, i.e, system/subject monitoring, expanded signal modeling and image reconstruction, SNR improvements of 50-100% are possible compared to state-of-the-art EPI diffusion imaging at 3 Tesla.
Here, we are aiming at establishing a robust spiral acquisition and online reconstruction pipeline that enables deployment of spiral diffusion MRI in a clinical research environment.
Motion-corrected wave-CAIPI Imaging (Zhe "Tim" Wu)
for fast and robust structural imaging, e.g., MPRAGE (T1-weighted)
Motion Correction (Brian Nghiem)
Patient motion is a common problem in MRI. When left unaccounted for, motion leads to mistakes in the spatial encoding of the MR signal, producing artifacts in the reconstructed image. Motion correction methods involve the estimation of the patient’s motion trajectory, whether through acquiring navigator data (ex. Butterfly navigator , probes/sensors) or using optimization algorithms (ex. image entropy minimization, least-squares optimization).
 Lustig M, Cunningham CH, Daniyalzade E, Pauly JM. Butterfly: a self navigating Cartesian trajectory. Proceedings of the Joint Annual Meeting ISMRM-ESMRMB; Berlin, Germany. 2007; p. 865
Figure adapted from Haskell MW, Cauley SF, Bilgic B, et al. doi:10.1002/mrm.27771. Brain phantom image from MNI Simulated Brain Database.