Faculty Bibliography

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background

PURPOSE:To develop and evaluate a rapid spherical navigator echo (SNAV) motion correction technique, then apply it for retrospective correction of brain images. METHODS:The pre-rotated, template matching SNAV method (preRot-SNAV) was developed in combination with a novel hybrid baseline strategy, which includes acquired and interpolated templates. Specifically, the SNAV templates are only rotated around X- and Y-axis; for each rotated SNAV, simulated baseline templates that mimic object rotation about the Z-axis were interpolated. The new method was first evaluated with phantom experiments. Then, a customized SNAV-interleaved gradient echo sequence was used to image three volunteers performing directed head motion. The SNAV motion measurements were used to retrospectively correct the brain images. Experiments were performed using a 3.0T whole-body MRI scanner and both single and 8-channel head coils. RESULTS:Phantom rotations and translations measured using the hybrid baselines agreed to within 0.9° and 1mm compared to those measured with the original preRot-SNAV method. Retrospective motion correction of in vivo images using the hybrid preRot-SNAV effectively corrected for head rotation up to 4° and 4mm. CONCLUSIONS:The presented hybrid approach enables the acquisition of pre-rotated baseline templates in as little as 2.5s, and results in accurate measurement of rotations and translations. Retrospective 3D motion correction successfully reduced motion artifacts in vivo.

PURPOSE/OBJECTIVE:To develop and evaluate a tool for accurate, reproducible, and programmable motion control of imaging phantoms for use in motion sensitive magnetic resonance imaging (MRI) appli cations. METHODS:In this paper, the authors introduce a compact linear motion stage that is made of nonmagnetic material and is actuated with an ultrasonic motor. The stage can be positioned at arbitrary positions and orientations inside the scanner bore to move, push, or pull arbitrary phantoms. Using optical trackers, measuring microscopes, and navigators, the accuracy of the stage in motion control was evaluated. Also, the effect of the stage on image signal-to-noise ratio (SNR), artifacts, and B0 field homogeneity was evaluated. RESULTS:The error of the stage in reaching fixed positions was 0.025 ± 0.021 mm. In execution of dynamic motion profiles, the worst-case normalized root mean squared error was below 7% (for frequencies below 0.33 Hz). Experiments demonstrated that the stage did not introduce artifacts nor did it degrade the image SNR. The effect of the stage on the B0 field was less than 2 ppm. CONCLUSIONS:The results of the experiments indicate that the proposed system is MRI-compatible and can create reliable and reproducible motion that may be used for validation and assessment of motion related MRI applications.