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We present a fast parallel algorithm to compute the nonequispaced fast Fourier transform on commodity graphics hardware (the GPU). We focus particularly on a novel implementation of the convolution step in the transform as it was previously its most time consuming part. We describe the performance for two common sample distributions in medical imaging (radial and spiral trajectories), and for different convolution kernels as these parameters all influence the speed of the algorithm. The GPU-accelerated convolution is up to 85 times faster as our reference, the open source NFFT library on a state-of-the-art 64 bit CPU. The accuracy of the proposed GPU implementation was quantitatively evaluated at the various settings. To illustrate the applicability of the transform in medical imaging, in which it is also known as gridding, we look specifically at non-Cartesian magnetic resonance imaging and reconstruct both a numerical phantom and an in vivo cardiac image. Index Terms--Discrete Fourier transforms, magnetic resonance imaging (MRI), parallel algorithms, parallel architectures.

A fast, robust, accurate, and automatic registration technique based on magnetic resonance (MR) active microcoils (active markers) for registration of tracked medical devices to preprocedural MR-images is presented. This allows for a straightforward integration of position measurement systems into clinical procedures. The presented method is useful for guidance purposes in clinical applications with high demands on accuracy and ease-of-use (e.g., neurosurgical or orthopedic applications). The determination of the positions of the active markers is integrated into the preparation phase of the actual MR imaging scan. The technique features a generic interface using DICOM standards for communication with navigation workstations linked to an MR system. The position of the active markers is fixed with respect to a reference system of an optical positioning measurement system (OPMS) and thus the coregistration of the MR system and the OPMS is established. In a phantom study, a mean overall targeting accuracy of 0.9 [+ or -] 0.1 mm was achieved and compared favorably to results obtained from manual registration tests (1.8 [+ or -] 0.3 mm) carried out in parallel. For a test person trained for both registration methods, workflow improvements of 3-6 min per registration step were found. The need for manual interaction is entirely eliminated thus avoiding user-bias, which is advantageous for the usage in clinical routine. The method improves the ease-of-use of tracking equipment during stereotactic guidance. The method is finally demonstrated in a volunteer study using a model of a Mayfield skull clamp with integrated active and optical reference markers. Index Terms--Guidance, MR-guided interventions, MR-guided surgery, navigation, tracking.

In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246[degrees]), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles. Index Terms--Projection reconstruction, radial signal-to-noise ratio (MRI), real-time imaging, time-resolved imaging.

The steady-state free precessing (SSFP) sequences, widely used in MRI today, acquire data only during a short fraction of the repetition time (TR). Thus, they exhibit a poor scan efficiency. In this paper, a novel approach to extending the acquisition window for a given TR without considerably modifying the basic sequence is explored for radial SSFP sequences. The additional data are primarily employed to increase the signal-to-noise ratio, rather than to improve the temporal resolution of the imaging. The approach is analyzed regarding its effect on the image SNR (signal to noise ratio) and the reconstruction algorithm. Results are presented for phantom experiments and cardiac functions studies. The gain in SNR is most notable in rapid imaging, since SNR enhancement for a constant repetition time may be used to compensate for the increase in noise resulting from angular undersampling. Index Terms--Averaging, magnetic resonance imaging, radial imaging, SNR, SSFP sequences.

Magnetic resonance (MR) imaging has faced a dramatic increase in real-time capabilities over the last years. However, the application of fast pulse sequences still suffers from low signal-to-noise ratios (SNRs), which can be the limiting factor for the actual acquisition speed. In MR fluoroscopy, filtering along the time and/or spatial domain can be applied to increase the image quality. In this paper, a projection-based noise filter is presented that significantly enhances the SNR in projection reconstruction (PR) fluoroscopy without apparent loss of resolution in the reconstructed images. In contrast to an imaged-based approach, this method allows a very efficient computational implementation. The filter algorithm was implemented on a digital signal processor and was applied to real-time processing during PR fluoroscopy. A quantitative analysis of the improvement in SNR and results for different fluoroscopic MR applications are given. Apart from MR fluoroscopy, the proposed technique has the potential to be applied to low dose computed tomography fluoroscopy. Index Terms--Adaptive filters, digital signal processors, magnetic resonance imaging, X-ray tomography.

This paper describes a predictive and adaptive single parameter motion model for updating roadmaps to correct for respiratory motion in image-guided interventions. The model can adapt its motion estimates to respond to changes in breathing pat- tern, such as deep or fast breathing, which normally would result in a decrease in the accuracy of the motion estimates. The adaptation is made possible by interpolating between the motion estimates of multiple submodels, each of which describes the motion of the target organ during cycles of different amplitudes. We describe a predictive technique which can predict the amplitude of a breathing cycle before it has finished. The predicted amplitude is used to interpolate between the motion estimates of the submodels to tune the adaptive model to the current breathing pattern. The proposed technique is validated on affine motion models formed from cardiac magnetic resonance imaging (MRI) datasets acquired from seven volunteers and one patient. The amplitude prediction technique showed errors of 1.9-6.5 mm. The combined predictive and adaptive technique showed 3-D motion prediction errors of 1.0-2.8 mm, which represents an improvement in modelling performance of up to 40% over a standard nonadaptive single parameter motion model. We also applied the combined technique in a clinical setting to test the feasibility of using it for respiratory motion correction of roadmaps in image-guided cardiac catheterisations. In this clinical case we show that 2-D registration errors due to respiratory motion are reduced from 7.7 to 2.8 mm using the proposed technique. [ABSTRACT FROM AUTHOR]

A barrier to the adoption of non-Cartesian parallel magnetic resonance imaging for real-time applications has been the times required for the image reconstructions. These times have exceeded the underlying acquisition time thus preventing real-time display of the acquired images. We present a reconstruction algorithm for commodity graphics hardware (GPUs) to enable real time reconstruction of sensitivity encoded radial imaging (radial SENSE). We demonstrate that a radial profile order based on the golden ratio facilitates reconstruction from an arbitrary number of profiles. This allows the temporal resolution to be adjusted on the fly. A user adaptable regularization term is also included and, particularly for highly undersampled data, used to interactively improve the reconstruction quality. Each reconstruction is fully self-contained from the profile stream, i.e., the required coil sensitivity profiles, sampling density compensation weights, regularization terms, and noise estimates are computed in real-time from the acquisition data itself. The reconstruction implementation is verified using a steady state free precession (SSFP) pulse sequence and quantitatively evaluated. Three applications are demonstrated; real-time imaging with real-time SENSE 1) or k-t SENSE 2) reconstructions, and 3) offline reconstruction with interactive adjustment of reconstruction settings. [ABSTRACT FROM AUTHOR]

We present a fast parallel algorithm to compute the nonequispaced fast Fourier transform on commodity graphics hardware (the GPU). We focus particularly on a novel implementation of the convolution step in the transform as it was previously its most time consuming part. We describe the performance for two common sample distributions in medical imaging (radial and spiral trajectories), and for different convolution kernels as these parameters all influence the speed of the algorithm. The GPU-accelerated convolution is up to 85 times faster as our reference, the open source NFFT library on a state-of-the-art 64 bit CPU. The accuracy of the proposed GPU implementation was quantitatively evaluated at the various settings. To illustrate the applicability of the transform in medical imaging, in which it is also known as gridding, we look specifically at non-Cartesian magnetic resonance imaging and reconstruct both a numerical phantom and an in vivo cardiac image. [ABSTRACT FROM AUTHOR]