Iterative image reconstruction for PROPELLER-MRI using the nonuniform fast fourier transform

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4–1.0. The L1TV-LAD with a regularization strength of λ= 0.4–0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.

Non-sedated functional imaging based on deep synchronization of PROPELLER MRI and NIRS

BACKGROUND AND OBJECTIVE

Periodically rotated overlapping parallel lines with enhanced reconstruction-echo planar imaging (PROPELLER-EPI) is a promising technique for non-sedated functional imaging due to its unique advantage of motion correction. However, its multiple-blades sampling blood-oxygen-level dependent (BOLD) signal leads to low sampling rate and aliasing of higher frequency physiological signal components such as the cardiac pulsation.

METHODS

In this study, we use near infrared spectroscopy (NIRS) synchronized with pulse sequences of PROPELLER-EPI, utilizing the fact that the optical sensing speed is inherently high. NIRS measures changes of oxyhemoglobin and deoxyhemoglobin to identify the transient states of on-BOLD and off-BOLD, and then labels each blade by temporal co-registration. The labeled blades from multiple epochs of a functional experiment are then used for the k-space data combination and subsequent image reconstruction. An eigenfunction model is proposed for temporal co-registration and to quantify the temporal resolution of the hemodynamic response.

RESULT

The experiment of NIRS labeled PROPELLER-EPI was carried out with the optical sampling rate of 10 Hz and the magnetic pulses repetition time of 1000 ms, and the temporal resolution is 20 times better than that of the state-of-the-art sliding-window PROPELLER-EPI. We compared the functional imaging results against the conventional magnetic resonance echo planar imaging-measured activity and achieved an accuracy of 0.9.

CONCLUSIONS

Using the synchronization of NIRS, the proposed imaging scheme provides an effective way to implement PROPELLER-EPI, which features motion free, high SNR, and enhanced spatial-temporal resolution.

Rapid compressed sensing reconstruction of 3D non-Cartesian MRI

PURPOSE

Conventional non-Cartesian compressed sensing requires multiple nonuniform Fourier transforms every iteration, which is computationally expensive. Accordingly, time-consuming reconstructions have slowed the adoption of undersampled 3D non-Cartesian acquisitions into clinical protocols. In this work we investigate several approaches to minimize reconstruction times without sacrificing accuracy.

METHODS

The reconstruction problem can be reformatted to exploit the Toeplitz structure of matrices that are evaluated every iteration, but it requires larger oversampling than what is strictly required by nonuniform Fourier transforms. Accordingly, we investigate relative speeds of the two approaches for various nonuniform Fourier transform kernel sizes and oversampling for both GPU and CPU implementations. Second, we introduce a method to minimize matrix sizes by estimating the image support. Finally, density compensation weights have been used as a preconditioning matrix to improve convergence, but this increases noise. We propose a more general approach to preconditioning that allows a trade-off between accuracy and convergence speed.

RESULTS

When using a GPU, the Toeplitz approach was faster for all practical parameters. Second, it was found that properly accounting for image support can prevent aliasing errors with minimal impact on reconstruction time. Third, the proposed preconditioning scheme improved convergence rates by an order of magnitude with negligible impact on noise.

CONCLUSION

With the proposed methods, 3D non-Cartesian compressed sensing with clinically relevant reconstruction times (<2 min) is feasible using practical computer resources. Magn Reson Med 79:2685-2692, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Calibrationless parallel magnetic resonance imaging: a joint sparsity model

State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets-eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used-Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods-CS SENSE and l1SPIRiT and two calibration free techniques-Distributed CS and SAKE. Our method yields better reconstruction results than all of them.

Rapid PROPELLER-MRI: a combination of iterative reconstruction and under-sampling

PURPOSE

To develop a technique that is able to reduce acquisition time and remove uneven blurring in reconstructed image for PROPELLER MRI. By using under-sampling and iterative reconstruction, this proposed technique will be less sensitive to subject motion.

MATERIALS AND METHODS

Numerical simulations, as well as experiments on a phantom and healthy human subjects were performed to demonstrate advantages of a combination of under-sampled acquisition and iterative reconstruction. Method of motion correction was modified to increase accuracy of motion correction for the under-sampled PROPELLER acquisition.

RESULTS

It was demonstrated that the proposed approach achieved substantial acceleration of PROPELLER acquisition while maintaining its motion correction advantage.

CONCLUSION

An effective method for reducing imaging time in PROPELLER was introduced in this study, which minimizes typical under-sampling artifacts without uneven spatial resolution and maintains the ability of motion correction.