Method of generalized projections algorithm for image-based reduction of artifacts in radial imaging

Trajectory correction based on the gradient impulse response function improves high-resolution UTE imaging of the musculoskeletal system

PURPOSE

UTE sequences typically acquire data during the ramping up of the gradient fields, which makes UTE imaging prone to eddy current and system delay effects. The purpose of this work was to use a simple gradient impulse response function (GIRF) measurement to estimate the real readout gradient waveform and to demonstrate that precise knowledge of the gradient waveform is important in the context of high-resolution UTE musculoskeletal imaging.

METHODS

The GIRF was measured using the standard hardware of a 3 Tesla scanner and applied on 3D radial UTE data (TE: 0.14 ms). Experiments were performed on a phantom, in vivo on a healthy knee, and in vivo on patients with spine fractures. UTE images were reconstructed twice, first using the GIRF-corrected gradient waveforms and second using nominal-corrected waveforms, correcting for the low-pass filter characteristic of the gradient chain.

RESULTS

Images reconstructed with the nominal-corrected gradient waveforms exhibited blurring and showed edge artifacts. The blurring and the edge artifacts were reduced when the GIRF-corrected gradient waveforms were used, as shown in single-UTE phantom scans and in vivo dual-UTE gradient-echo scans in the knee. Further, the importance of the GIRF-based correction was indicated in UTE images of the lumbar spine, where thin bone structures disappeared when the nominal correction was employed.

CONCLUSION

The presented GIRF-based trajectory correction method using standard scanner hardware can improve the quality of high-resolution UTE musculoskeletal imaging.

Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints

PURPOSE

To correct gradient timing delays in non-Cartesian MRI while simultaneously recovering corruption-free auto-calibration data for parallel imaging, without additional calibration scans.

METHODS

The calibration matrix constructed from multi-channel k-space data should be inherently low-rank. This property is used to construct reconstruction kernels or sensitivity maps. Delays between the gradient hardware across different axes and RF receive chain, which are relatively benign in Cartesian MRI (excluding EPI), lead to trajectory deviations and hence data inconsistencies for non-Cartesian trajectories. These in turn lead to higher rank and corrupted calibration information which hampers the reconstruction. Here, a method named Simultaneous Auto-calibration and Gradient delays Estimation (SAGE) is proposed that estimates the actual k-space trajectory while simultaneously recovering the uncorrupted auto-calibration data. This is done by estimating the gradient delays that result in the lowest rank of the calibration matrix. The Gauss-Newton method is used to solve the non-linear problem. The method is validated in simulations using center-out radial, projection reconstruction and spiral trajectories. Feasibility is demonstrated on phantom and in vivo scans with center-out radial and projection reconstruction trajectories.

RESULTS

SAGE is able to estimate gradient timing delays with high accuracy at a signal to noise ratio level as low as 5. The method is able to effectively remove artifacts resulting from gradient timing delays and restore image quality in center-out radial, projection reconstruction, and spiral trajectories.

CONCLUSION

The low-rank based method introduced simultaneously estimates gradient timing delays and provides accurate auto-calibration data for improved image quality, without any additional calibration scans.

Effect of k-space-weighted image contrast and ultrasound focus size on the accuracy of proton resonance frequency thermometry

PURPOSE

To construct a predictive model that describes how the duration and symmetry of a k-space-weighted image contrast (KWIC) window affects the temporal resolution of differently sized ultrasound foci when using a pseudo-golden angle stack-of-stars acquisition.

METHODS

We performed a modulation analysis of proton resonance frequency temperature measurements to create the temporal modulation transfer function for KWIC windows of different symmetry and temporal duration. We reconstructed simulated ultrasound heating trajectories and stack-of-stars k-space data as well as experimental phantom data using the same trajectories. Images were reconstructed using symmetric and asymmetric KWIC windows of 3 different temporal durations. Simulated results were compared against temporal modulation transfer function predictions, experimental results, and the original simulated temperatures.

RESULTS

The temporal modulation transfer function shows that temporal resolution with KWIC reconstructions depend on the object size. The KWIC window duration affected SNR and severity of undersampling artifacts. Accuracy and response delay improved as the KWIC window duration decreased or the size of the heated region within the KWIC plane increased. Precision worsened as the window duration decreased. Using a symmetric window eliminated the response delay to heated region size but introduced a large reconstruction delay.

CONCLUSION

The accuracy and precision of proton resonance frequency temperature measurements from a stack-of-stars acquisition using a sliding KWIC window reconstruction are dependent on the size of the KWIC window and the size and shape of the heated region. The temporal modulation transfer function of KWIC reconstructions for any object size can predict the temporal response to changes in signal being acquired, such as temperature and contrast enhancement.

Multiecho pseudo-golden angle stack of stars thermometry with high spatial and temporal resolution using k-space weighted image contrast

PURPOSE

Implement and evaluate a 3D MRI method to measure temperature changes with high spatial and temporal resolution and large field of view.

METHODS

A multiecho pseudo-golden angle stack-of-stars (SOS) sequence with k-space weighted image contrast (KWIC) reconstruction was implemented to simultaneously measure multiple quantities, including temperature, initial signal magnitude M(0), transverse relaxation time ( T2*), and water/fat images. Respiration artifacts were corrected using self-navigation. KWIC artifacts were removed using a multi-baseline library. The phases of the multiple echo images were combined to improve proton resonance frequency precision. Temperature precision was tested through in vivo breast imaging (N = 5 healthy volunteers) using both coronal and sagittal orientations and with focused ultrasound (FUS) heating in a pork phantom using a breast specific MR-guided FUS system.

RESULTS

Temperature measurement precision was significantly improved after echo combination when compared with the no echo combination case (spatial average of the standard deviation through time of 0.3-1.0 and 0.7-1.9°C, respectively). Temperature measurement accuracy during heating was comparable to a 3D seg-EPI sequence. M(0) and T2* values showed temperature dependence during heating in pork adipose tissue.

CONCLUSION

A self-navigated 3D multiecho SOS sequence with dynamic KWIC reconstruction is a promising thermometry method that provides multiple temperature sensitive quantitative values. Magn Reson Med 79:1407-1419, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

K-space trajectory mapping and its application for ultrashort Echo time imaging

MR images are affected by system delays and gradient field imperfections which induce discrepancies between prescribed and actual k-space trajectories. This could be even more critical for non-Cartesian data acquisitions where even a small deviation from the assumed k-space trajectory results in severe image degradation and artifacts. Knowledge of the actual k-space trajectories is therefore crucial and can be incorporated in the reconstruction of high quality non-Cartesian images. A novel MR method for the calibration of actual gradient waveforms was developed using a combination of phase encoding increments and subsequent detection of the exact time point at which the corresponding trajectory is crossing the k-space origin. The measured sets of points were fitted to a parametrical model to calculate the complete actual acquisition trajectory. Measurements performed on phantoms and volunteers, positioned both in- and off-isocenter of the magnet, clearly demonstrate the improvement in reconstructed ultrashort echo time (UTE) images, when information from calibration of k-space sampling trajectories is employed in the MR image reconstruction procedure. The unique feature of the proposed method is its robustness and simple experimental setup, making it suitable for quick acquisition trajectory calibration procedures e.g. for non-Cartesian radial fast imaging.

Self-calibrated trajectory estimation and signal correction method for robust radial imaging using GRAPPA operator gridding

PURPOSE

In radial imaging, projections may become "miscentered" due to gradient errors such as delays and eddy currents. These errors may result in image artifacts and can disrupt the reliability of direct current (DC) navigation. The proposed parallel imaging-based technique retrospectively estimates trajectory error from miscentered radial data without extra acquisitions, hardware, or sequence modification.

THEORY AND METHODS

After phase correction, self-calibrated GRAPPA operator gridding (GROG) weights are iteratively applied to shift-miscentered projections toward the center of k-space. A search algorithm identifies the shift that aligns the peak k-space signals by maximizing the sum-of-squares DC signal estimate of each projection. The algorithm returns a trajectory estimate and a corrected radial k-space signal.

RESULTS

Data from a spherical phantom, the head, and the heart demonstrate that image reconstruction with the estimated trajectory restores image quality and reduces artifacts such as streaks and signal voids. The DC signal level is increased and variability is reduced.

CONCLUSION

Retrospective phase correction and iterative application of GROG can be used to successfully estimate the trajectory error in two-dimensional radial acquisitions for improved image reconstruction without requiring extra data acquisition or sequence modification.