On the limitations of partial Fourier acquisition in phase-contrast MRI of turbulent kinetic energy

An applicability study of rapid artificial intelligence-assisted compressed sensing (ACS) in anal fistula magnetic resonance imaging

Objective

To evaluate the applicability of artificial intelligence-assisted compressed sensing (ACS) to anal fistula magnetic resonance imaging (MRI).

Methods

51 patients were included in this study and underwent T2-weighted sequence of MRI examinations both with ACS and without ACS technology in a 3.0 T MR scanner. Subjective image quality scores, and objective image quality-related metrics including scanning time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), were evaluated and statistically compared between the images collected with and without ACS.

Results

No significant difference in the subjective image quality of lesion conspicuity was observed between the two groups. However, ACS MRI decreased the acquisition time with regard to control group (74.00 s vs. 156.00 s). Besides, SNR of perianal and muscle in the ACS group was significantly higher than that of the control group (164.07 ± 33.35 vs 130.81 ± 29.10, p < 0.001; 109.87 ± 22.01 vs 87.61 ± 17.95, p < 0.001; respectively). The CNR was significantly higher in the ACS group than in the control group (54.02 ± 23.98 vs 43.20 ± 21.00; p < 0.001). Moreover, the accuracy rate of the ACS groups in evaluating the direction and internal opening of the fistula was 88.89 %, exactly the same as that of the control group.

Conclusion

We demonstrated the applicability of using ACS to accelerate MR of anal fistulas with improved SNR and CNR. Meanwhile, the accuracy rates of the ACS group and the control were equivalent in evaluating the direction and internal opening of the fistula, based on the results of surgical exploration.

Joint optimization of Cartesian sampling patterns and reconstruction for single-contrast and multi-contrast fast magnetic resonance imaging

BACKGROUND AND OBJECTIVE

Compressed sensing (CS) has gained increased attention in magnetic resonance imaging (MRI), leveraging its efficacy to accelerate image acquisition. Incoherence measurement and non-linear reconstruction are the most crucial guarantees of accurate restoration. However, the loose link between measurement and reconstruction hinders the further improvement of reconstruction quality, i.e., the default sampling pattern is not adaptively tailored to the downstream reconstruction method. When single-contrast reconstruction (SCR) has been upgraded to its multi-contrast reconstruction (MCR) variant, the identical morphologic information as a priori source could be integrated into the reconstruction procedure. How to measure less and reconstruct effectively by using the shareable morphologic information of various contrast images is an attractive topic.

METHODS

An adaptive sampling (AS) based end-to-end framework (ASSCR or ASMCR) is proposed to address this issue, which simultaneously optimizes sampling patterns and reconstruction from under-sampled data in SCR or MCR scenarios. Several deep probabilistic subsampling (DPS) modules are used in AS network to construct a sampling pattern generator. In SCR and MCR, a convolution block and a data consistency layer are iteratively applied in the reconstruction network. Specifically, the learned optimal sampling pattern output from the trained AS sub-net is used for under-sampling. Incoherence measurement for single-contrast images and the combination of sampling patterns for multi-contrast data are guided by the SCR/MCR sub-net.

RESULTS

Experiments were conducted on two single-contrast and one multi-contrast public MRI datasets. Compared with several state-of-the-art reconstruction methods, SCR results show that a learned sampling pattern brings the quality of the reconstructed image closer to the fully-sampled reference. With the addition of different contrast images, under-sampled images with higher acceleration factors could be well recovered.

CONCLUSION

The proposed method could improve the reconstruction quality of under-sampled images by using adaptive sampling patterns and learning-based reconstruction.

The role of 4D flow MRI for clinical applications in cardiovascular disease: current status and future perspectives

Magnetic resonance imaging (MRI) four-dimensional (4D) flow is a type of phase-contrast (PC) MRI that uses blood flow encoded in 3 directions, which is resolved relative to 3 spatial and temporal dimensions of cardiac circulation. It can be used to simultaneously quantify and visualize hemodynamics or morphology disorders. 4D flow MRI is more comprehensive and accurate than two-dimensional (2D) PC MRI and echocardiography. 4D flow MRI provides numerous hemodynamic parameters that are not limited to the basic 2D parameters, including wall shear stress (WSS), pulse wave velocity (PWV), kinetic energy, turbulent kinetic energy (TKE), pressure gradient, and flow component analysis. 4D flow MRI is widely used to image many parts of the body, such as the neck, brain, and liver, and has a wide application spectrum to cardiac diseases and large vessels. This present review aims to summarize the hemodynamic parameters of 4D flow MRI technology and generalize their usefulness in clinical practice in relation to the cardiovascular system. In addition, we note the improvements that have been made to 4D flow MRI with the application of new technologies. The application of new technologies can improve the speed of 4D flow, which would benefit clinical applications.

Autocalibrated cardiac tissue phase mapping with multiband imaging and k-t acceleration

PURPOSE

To develop an autocalibrated multiband (MB) CAIPIRINHA acquisition scheme with in-plane k-t acceleration enabling multislice three-directional tissue phase mapping in one breath-hold.

METHODS

A k-t undersampling scheme was integrated into a time-resolved electrocardiographic-triggered autocalibrated MB gradient-echo sequence. The sequence was used to acquire data on 4 healthy volunteers with MB factors of two (MB2) and three (MB3), which were reconstructed using a joint reconstruction algorithm that tackles both k-t and MB acceleration. Forward simulations of the imaging process were used to tune the reconstruction model hyperparameters. Direct comparisons between MB and single-band tissue phase-mapping measurements were performed.

RESULTS

Simulations showed that the velocities could be accurately reproduced with MB2 k-t (average ± twice the SD of the RMS error of 0.08 ± 0.22 cm/s and velocity peak reduction of 1.03% ± 6.47% compared with fully sampled velocities), whereas acceptable results were obtained with MB3 k-t (RMS error of 0.13 ± 0.58 cm/s and peak reduction of 2.21% ± 13.45%). When applied to tissue phase-mapping data, the proposed technique allowed three-directional velocity encoding to be simultaneously acquired at two/three slices in a single breath-hold of 18 heartbeats. No statistically significant differences were detected between MB2/MB3 k-t and single-band k-t motion traces averaged over the myocardium. Regional differences were found, however, when using the American Heart Association model for segmentation.

CONCLUSION

An autocalibrated MB k-t acquisition/reconstruction framework is presented that allows three-directional velocity encoding of the myocardial velocities at multiple slices in one breath-hold.

Partial Fourier reconstruction for improved resolution in 3D hyperpolarized 13 C EPI

PURPOSE

Asymmetric in-plane k-space sampling of EPI can reduce the minimum achievable TE in hyperpolarized 13 C with spectral-spatial radio frequency pulses, thereby reducing T 2 * weighting and signal-losses. Partial Fourier image reconstruction exploits the approximate Hermitian symmetry of k-space data and can be applied to asymmetric data sets to synthesize unmeasured data. Here we tested whether the application of partial Fourier image reconstruction would improve spatial resolution from hyperpolarized [1- 13 C ]pyruvate scans in the human brain.

METHODS

Fifteen healthy control subjects were imaged using a volumetric dual-echo echo-planar imaging sequence with spectral-spatial radio frequency excitation. Images were reconstructed by zero-filling as well as with the partial Fourier reconstruction algorithm projection-on-convex-sets. Resulting images were quantitatively evaluated with a no-reference image quality assessment.

RESULTS

The no-reference image sharpness metric agreed with perceived improvements in image resolution and contrast. The [1- 13 C ]lactate images benefitted most, followed by the [1- 13 C ]pyruvate images. The 13 C -bicarbonate images were improved by the smallest degree, likely owing to relatively lower SNR.

CONCLUSIONS

Partial Fourier imaging and reconstruction were shown to improve the sharpness and contrast of human HP 13 C brain data and is a viable method for enhancing resolution.