Highly accelerated contrast-enhanced MR angiography: improved reconstruction accuracy and reduced noise amplification with complex subtraction

Highly accelerated subtractive femoral non-contrast-enhanced MRA using compressed sensing with k-space subtraction, phase and intensity correction

PURPOSE

To develop an improved reconstruction method, k-space subtraction with phase and intensity correction (KSPIC), for highly accelerated, subtractive, non-contrast-enhanced MRA.

METHODS

The KSPIC method is based on k-space subtraction of complex raw data. It applies a phase-correction procedure to restore the polarity of negative signals caused by subtraction and an intensity-correction procedure to improve background suppression and thereby sparsity. Ten retrospectively undersampled data sets and 10 groups of prospectively undersampled data sets were acquired in 12 healthy volunteers. The performance of KSPIC was compared with another improved reconstruction based on combined magnitude subtraction, as well as with conventional k-space subtraction reconstruction and magnitude subtraction reconstruction, both using quantitative metrics and using subjective quality scoring.

RESULTS

In the quantitative evaluation, KSPIC had the best performance in terms of peak SNR, structural similarity index measure, contrast-to-noise ratio of artery-to-background and sharpness, especially at high acceleration factors. The KSPIC method also had the highest subjective scores for all acceleration factors in terms of vessel delineation, image noise and artifact, and background contamination. The acquisition can be accelerated by a factor of 20 without significant decreases of subjective scores. The optimal size of the phase-correction region was found to be 12-20 pixels in this study.

CONCLUSION

Compared with combined magnitude subtraction and conventional reconstructions, KSPIC has the best performance in all of the quantitative and qualitative measurements, permitting good image quality to be maintained up to higher accelerations. The KSPIC method has the potential to further reduce the acquisition time of subtractive MRA for clinical examinations.

Background suppressed magnetization transfer MRI

PURPOSE

Up to 30% of the hydrogen atoms in brain tissue are part of molecules ("semisolids") other than water. In MRI, their magnetization is typically not observed directly, but can influence the water magnetization through magnetization transfer (MT). Comparison of MRI scans differentially sensitized to MT allows estimation of the semisolid fraction and potential changes with disease. Here, we present an approach designed to improve this estimate by measuring the size of the MT effect in a single scan.

METHODS

A stimulated echo sequence was used to generate a spatial pattern in the longitudinal water magnetization, which was then given time to exchange with semisolids. After saturating the remaining water magnetization, reverse exchange was allowed to partly re-establish the original water magnetization pattern. The third excitation pulse then formed a stimulated echo out of this pattern.

RESULTS

MT data were obtained on 10 human subjects at 7 T with varying exchange times. The images showed the expected time dependence of signal associated with the forward and reverse exchange processes. Excellent suppression of non-exchanging background signal was achieved. As expected, this suppression came at the price of a substantial reduction in exchange-related signal (by ~75% compared to the signal in saturation recovery MT), in part because of the reliance on a 2-step exchange process.

CONCLUSION

The results demonstrate an MT signal can be observed in a single acquisition without subtraction. This may be advantageous for MT measurements when signal instabilities related to motion and physiological variations exceed thermal noise sources.

HF-SENSE: an improved partially parallel imaging using a high-pass filter

Background

One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE).

Methods

First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter.

Results

Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE.

Conclusions

The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE.

This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018–314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.

Improved k-t PCA Algorithm Using Artificial Sparsity in Dynamic MRI

The k-t principal component analysis (k-t PCA) is an effective approach for high spatiotemporal resolution dynamic magnetic resonance (MR) imaging. However, it suffers from larger residual aliasing artifacts and noise amplification when the reduction factor goes higher. To further enhance the performance of this technique, we propose a new method called sparse k-t PCA that combines the k-t PCA algorithm with an artificial sparsity constraint. It is a self-calibrated procedure that is based on the traditional k-t PCA method by further eliminating the reconstruction error derived from complex subtraction of the sampled k-t space from the original reconstructed k-t space. The proposed method is tested through both simulations and in vivo datasets with different reduction factors. Compared to the standard k-t PCA algorithm, the sparse k-t PCA can improve the normalized root-mean-square error performance and the accuracy of temporal resolution. It is thus useful for rapid dynamic MR imaging.

Accelerated noncontrast-enhanced 4-dimensional intracranial MR angiography using golden-angle stack-of-stars trajectory and compressed sensing with magnitude subtraction

PURPOSE

To evaluate the feasibility and performance of compressed sensing (CS) with magnitude subtraction regularization in accelerating non-contrast-enhanced dynamic intracranial MR angiography (NCE-dMRA).

METHODS

A CS algorithm was introduced in NCE-dMRA by exploiting the sparsity of the magnitude difference of the control and label images. The NCE-dMRA data were acquired using golden-angle stack-of-stars trajectory on six healthy volunteers and one patient with arteriovenous fistula. Images were reconstructed using (i) the proposed magnitude-subtraction CS (MS-CS); (ii) complex-subtraction CS; (iii) independent CS; and (iv) view-sharing with k-space weighted image contrast (KWIC). The dMRA image quality was compared across the four reconstruction strategies. The proposed MS-CS method was further compared with KWIC for temporal fidelity of depicting dynamic flow.

RESULTS

The proposed MS-CS method was able to reconstruct NCE-dMRA images with detailed vascular structures and clean background. It provided better subjective image quality than the other two CS strategies (P < 0.05). Compared with KWIC, MS-CS showed similar image quality, but reduced temporal blurring in delineating the fine distal arteries.

CONCLUSIONS

The MS-CS method is a promising CS technique for accelerating NCE-dMRA acquisition without compromising image quality and temporal fidelity. Magn Reson Med 79:867-878, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Dynamic Contrast-Enhanced MR Angiography Exploiting Subspace Projection for Robust Angiogram Separation

Dynamic contrast-enhanced magnetic resonance angiography (DCE MRA) has been widely used as a clinical routine for diagnostic assessment of vascular morphology and hemodynamics. It requires high spatial and temporal resolution to capture rapid variation of DCE signals within a limited imaging time. Subtraction-based approaches are typically employed to selectively delineate arteries while eliminating unwanted background signals. Nevertheless, in the presence of subject motion with time, conventional subtraction approaches suffer from incomplete background suppression that impairs the detectability of arteries. In this work, we propose a novel, DCE MRA method that exploits subspace projection (SP) based angiogram separation for robust background suppression. A new, SP-based DCE signal model is introduced, in which images are decomposed into stationary background tissues, motion-induced artifacts, and DCE angiograms of interest. Constrained image reconstruction with sparsity priors is performed to project motion-induced artifacts onto the predefined subspace while extracting DCE angiograms of interest. Simulations and experimental studies validate that the proposed method outperforms existing techniques with increasing reduction factors in suppressing artifacts and noise.

Incorporating reference in parallel imaging and compressed sensing

PURPOSE

To develop a new compressed sensing parallel imaging technique called READ-PICS that can effectively incorporate prior information from a reference scan for MR image reconstruction from highly undersampled multichannel measurements.

METHODS

READ-PICS incorporates information from a high-spatial-resolution reference prior using the generalized series model, to achieve increased image sparsity and mitigated noise amplification simultaneously. To further improve the ill-conditioning of the parallel imaging system, an annular area in the central residual k-space is used for calibration. Additionally, the mixed L1-L2 norm of the coefficients from the prior component and residual component is used to enforce joint sparsity.

RESULTS

The evaluations on parametric imaging and multiscan experiment demonstrate superior performance of READ-PICS in terms of detail preservation and noise suppression compared to state-of-the-art technique, L1-Iterative self-consistent parallel imaging reconstruction, and prescan required method, correlation imaging.

CONCLUSIONS

The proposed method can significantly increase signal sparsity and improve the ill-conditioning of the parallel imaging system using reference adaptive regularization. This technique can be easily adapted to other imaging applications where multiple images need to be acquired sequentially and a reference prior is also available.

Vascular masking for improved unfolding in 2D SENSE-accelerated 3D contrast-enhanced MR angiography

PURPOSE

To describe and evaluate the method we refer to as "vascular masking" for improving signal-to-noise ratio (SNR) retention in sensitivity encoding (SENSE)-accelerated contrast-enhanced magnetic resonance angiography (CE-MRA).

MATERIALS AND METHODS

Vascular masking is a technique that restricts the SENSE unfolding of an accelerated subtraction angiogram to the voxels within the field of view known to have enhancing signal. This is a more restricted voxel set than that identified with conventional masking, which excludes only voxels in the air around the object. Thus, improved retention of SNR is expected. Evaluation was done in phantom and in vivo studies by comparing SNR and the g-factor in results reconstructed using vascular versus conventional masking. A radiological evaluation was also performed comparing conventional and vascular masking in R = 8 accelerated CE-MRA studies of the thighs (n = 21) and calves (n = 13).

RESULTS

Images reconstructed with vascular masking showed a significant reduction in g-factor and improved retention of SNR versus those reconstructed with conventional masking. In the radiological evaluation, vascular masking consistently provided reduced background noise, improved luminal signal smoothness, and better small vessel conspicuity.

CONCLUSION

Vascular masking provides improved SNR retention and improved depiction of the vasculature in accelerated, subtraction 3D CE-MRA of the thighs and calves.

High spatial and temporal resolution dynamic contrast-enhanced magnetic resonance angiography using compressed sensing with magnitude image subtraction

PURPOSE

We propose a compressed-sensing (CS) technique based on magnitude image subtraction for high spatial and temporal resolution dynamic contrast-enhanced MR angiography (CE-MRA).

METHODS

Our technique integrates the magnitude difference image into the CS reconstruction to promote subtraction sparsity. Fully sampled Cartesian 3D CE-MRA datasets from 6 volunteers were retrospectively under-sampled and three reconstruction strategies were evaluated: k-space subtraction CS, independent CS, and magnitude subtraction CS. The techniques were compared in image quality (vessel delineation, image artifacts, and noise) and image reconstruction error. Our CS technique was further tested on seven volunteers using a prospectively under-sampled CE-MRA sequence.

RESULTS

Compared with k-space subtraction and independent CS, our magnitude subtraction CS provides significantly better vessel delineation and less noise at 4× acceleration, and significantly less reconstruction error at 4× and 8× (P < 0.05 for all). On a 1-4 point image quality scale in vessel delineation, our technique scored 3.8 ± 0.4 at 4×, 2.8 ± 0.4 at 8×, and 2.3 ± 0.6 at 12× acceleration. Using our CS sequence at 12× acceleration, we were able to acquire dynamic CE-MRA with higher spatial and temporal resolution than current clinical TWIST protocol while maintaining comparable image quality (2.8 ± 0.5 vs. 3.0 ± 0.4, P = NS).

CONCLUSION

Our technique is promising for dynamic CE-MRA.

Exploiting sparsity to accelerate noncontrast MR angiography in the context of parallel imaging

Noncontrast techniques for peripheral MR angiography are receiving renewed interest because of safety concerns about the use of gadolinium in patients with renal insufficiency. One class of techniques involves subtraction of dark-blood images acquired during fast systolic flow from bright-blood images obtained during slow diastolic flow. The goal of this work was to determine whether the inherent sparsity of the difference images could be exploited to achieve greater acceleration without loss of image quality in the context of generalized autocalibrating partially parallel acquisition (GRAPPA). It is shown that noise amplification at high acceleration factors can be reduced by performing subtraction on the raw data, before calculation of the GRAPPA weights, rather than on the final magnitude images. Use of the difference data to calculate the GRAPPA weights decreases the geometry factor (g-factor), because the difference data represent a sparse image set. This demonstrates an inherent property of GRAPPA and does not require the use of compressed sensing. Application of this approach to highly accelerated data from healthy volunteers resulted in similar depiction of large arteries to that obtained with low acceleration and standard reconstruction. However, visualization of very small vessels and arterial branches was compromised.