Fast direct fourier reconstruction of radial and PROPELLER MRI data using the chirp transform algorithm on graphics hardware

USING FAST FOURIER TRANSFORM AND POLYNOMIAL FITTING ON DORSAL FOOT KINEMATICS DATA TO IDENTIFY SIMULATED ANKLE SPRAIN MOTIONS FROM COMMON SPORTING MOTIONS

Ankle sprain is very common in sports, and a commonly suggested etiology is the delayed peroneal muscle reaction time. Recent studies showed the successful attempts to deliver electrical stimulation to the peroneal muscles externally to initiate contraction before it could react, however, the success relies on a workable method to detect ankle sprain injury in time. This study presented a fast Fourier transform and polynomial fitting method with dorsal foot kinematics data for quick ankle sprain detection. Five males performed 100 simulated ankle sprain and 250 common sporting motion trials. Eight gyrometers recorded the three-dimensional angular velocities at 500[Formula: see text]Hz. Data were trimmed with a 0.11[Formula: see text]s window size, the suggested duration of preinjury phase in ankle sprain, and were transformed from time to frequency domain by fast Fourier transform and fitted with a fifth-order polynomial. First-order coefficients from polynomial fitting on frequency space were obtained. The method achieved 97.0% sensitivity and 91.4% specificity in identifying simulated sprains, vertical jump–landing, cutting, stepping-down, running, and walking motions, with vertical jump–landing excluded due to its relatively low specificity (67.3%). The method can be used to detect ankle sprain in sports with mainly floor movements and minimal vertical jump–landing motion.

Distributed MRI reconstruction using Gadgetron-based cloud computing

PURPOSE

To expand the open source Gadgetron reconstruction framework to support distributed computing and to demonstrate that a multinode version of the Gadgetron can be used to provide nonlinear reconstruction with clinically acceptable latency.

METHODS

The Gadgetron framework was extended with new software components that enable an arbitrary number of Gadgetron instances to collaborate on a reconstruction task. This cloud-enabled version of the Gadgetron was deployed on three different distributed computing platforms ranging from a heterogeneous collection of commodity computers to the commercial Amazon Elastic Compute Cloud. The Gadgetron cloud was used to provide nonlinear, compressed sensing reconstruction on a clinical scanner with low reconstruction latency (eg, cardiac and neuroimaging applications).

RESULTS

The proposed setup was able to handle acquisition and 11 -SPIRiT reconstruction of nine high temporal resolution real-time, cardiac short axis cine acquisitions, covering the ventricles for functional evaluation, in under 1 min. A three-dimensional high-resolution brain acquisition with 1 mm(3) isotropic pixel size was acquired and reconstructed with nonlinear reconstruction in less than 5 min.

CONCLUSION

A distributed computing enabled Gadgetron provides a scalable way to improve reconstruction performance using commodity cluster computing. Nonlinear, compressed sensing reconstruction can be deployed clinically with low image reconstruction latency.