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

Evaluation of the Abdominal Aorta and External Iliac Arteries Using Three-Dimensional Time-of-Flight, Three Dimensional Electrocardiograph-Gated Fast Spin-Echo, and Contrast-Enhanced Magnetic Resonance Angiography in Clinically Healthy Cats

Arterial thromboembolism is associated with high morbidity and mortality rates in cats. Definitive diagnosis requires advanced imaging modalities, such as computed tomography angiography (CTA) and contrast-enhanced (CE) magnetic resonance angiography (MRA). However, CTA involves exposure to a large amount of ionized radiation, and CE-MRA can cause systemic nephrogenic fibrosis. Non-contrast-enhanced (NE) MRA can help accurately diagnose vascular lesions without such limitations. In this study, we evaluated the ability of NE-MRA using three-dimensional electrocardiograph-gated fast spin-echo (3D ECG-FSE) and 3D time-of-flight (3D TOF) imaging to visualize the aorta and external iliac arteries in clinically healthy cats and compared the results with those obtained using CE-MRA. All 11 cats underwent 3D ECG-FSE, 3D TOF, and CE-MRA sequences. Relative signal intensity (rSI) for quantitative image analysis and image quality scores (IQS) for qualitative image analysis were assessed; the rSI values based on the 3D TOF evaluations were significantly lower than those obtained using 3D ECG-FSE (aorta 3D TOF: 0.57 ± 0.06, aorta 3D ECG-FSE: 0.83 ± 0.06, P < 0.001; external iliac arteries 3D TOF: 0.45 ± 0.06, external iliac arteries 3D ECG-FSE:0.80 ± 0.05, P < 0.001) and similar to those obtained using CE-MRA (aorta: 0.58 ± 0.05, external iliac arteries: 0.57 ± 0.03). Moreover, IQS obtained using 3D TOF were significantly higher than those obtained using 3D ECG-FSE (aorta 3D TOF: 3.95 ± 0.15, aorta 3D ECG-FSE: 2.32 ± 0.60, P < 0.001; external iliac arteries 3D ECG-FSE: 3.98 ± 0.08, external iliac arteries 3D ECG-FSE: 2.23 ± 0.56, P < 0.001) and similar to those obtained using CE-MRA (aorta: 3.61 ± 0.41, external iliac arteries: 3.57 ± 0.41). Thus, 3D TOF is more suitable and produces consistent image quality for visualizing the aorta and external iliac arteries in clinically healthy cats and this will be of great help in the diagnosis of FATE.

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.

Subtractive NCE-MRA: Improved background suppression using robust regression-based weighted subtraction

PURPOSE

To correct the intensity difference of static background signal between bright blood images and dark blood images in subtractive non-contrast-enhanced MR angiography using robust regression, thereby improving static background signal suppression on subtracted angiograms.

METHODS

Robust regression (RR), using iteratively reweighted least squares, is used to calculate the regression coefficient of background tissues from a scatter plot showing the voxel intensity of bright blood images versus dark blood images. The weighting function is based on either the Euclidean distance from the estimated regression line or the deviation angle. Results from RR using the deviation angle (RRDA), conventional RR using the Euclidean distance, and ordinary leastsquares regression were compared with reference values determined manually by two observers. Performance was evaluated over studies using different sequences, including 36 thoracic flow-sensitive dephasing data sets, 13 iliac flow-sensitive dephasing data sets, and 26 femoral fresh blood imaging data sets.

RESULTS

RR deviation angle achieved robust and accurate performance in all types of images, with small bias, small mean absolute error, and high-correlation coefficients with reference values. Background tissues, such as muscle, veins, and bladder, were suppressed while the vascular signal was preserved. Euclidean distance gave good performance for thoracic and iliac flow-sensitive dephasing, but could not suppress background tissues in femoral fresh blood imaging. Ordinary least squares regression was sensitive to outliers and overestimated regression coefficients in thoracic flow-sensitive dephasing.

CONCLUSION

Weighted subtraction using RR was able to acquire the regression coefficients of background signal and improve background suppression of subtractive non-contrast-enhanced MR angiography techniques. RR deviation angle has the most robust and accurate overall performance among three regression methods.

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.

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.

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.