Technical Note
Improved acceleration of phase-contrast flow imaging with magnitude difference regularization

https://doi.org/10.1016/j.mri.2019.12.001Get rights and content

Abstract

Purpose

To develop a regularized image reconstruction algorithm for improved scan acceleration of phase-contrast (PC) flow MRI.

Methods

Based on the magnitude similarity between bipolar-encoded k-space data, magnitude-difference regularization was incorporated into the conventional compressed sensing (CS) reconstruction. The gradient of the magnitude regularization was derived so the reconstruction problem can be solved using non-linear conjugate gradient with backtracking line search. Phase contrast flow data obtained in the peripheral arteries of healthy and patient subjects were retrospectively undersampled for testing the proposed reconstruction method. Three-dimensional velocity-encoded PC flow MRI was performed with prospective 4-fold undersampling for measuring arotic flow velocity in a healthy volunteer.

Results

In the femoral arteries of healthy volunteers, the root-mean-square (RMS) errors of mean velocities were 0.56 ± 0.09 cm/s with CS-only reconstruction and 0.46 ± 0.08 cm/s with addition of magnitude regularization for three-fold acceleration; 1.34 ± 0.17 cm/s (CS only) and 1.08 ± 0.15 cm/s (magnitude regularized) for four-fold acceleration. In the iliac arteries of the patient, the RMS errors of mean velocities were 0.72 ± 0.12 cm/s and 0.56 ± 0.10 for three-fold acceleration, and 1.75 ± 0.21 and 1.24 ± 0.19 cm/s for four-fold acceleration (in the order of CS-only and magnitude regularized reconstructions). In the popliteal arteries, the RMS errors were 0.61 ± 0.10 cm/s and 0.42 ± 0.11 for three-fold acceleration, and 1.41 ± 0.19 and 1.12 ± 0.17 cm/s for four-fold acceleration. The maximum through-plane mean flow velocities were measured as 63.2 cm/s and 84.5 cm/s in ascending and descending aortas, respectively.

Conclusion

The addition of magnitude-difference regularization into conventional CS reconstruction improves the accuracy of image reconstruction using highly undersampled phase-contrast flow MR data.

Introduction

Phase contrast (PC) magnetic resonance imaging (MRI) has been established as a reliable tool for quantifying blood flow in the human vascular systems [1,2]. Applicable vascular territories are extensive, including the heart and nearby great vessels, hepatic and portal veins, carotid and cerebral arteries, and peripheral arteries [[3], [4], [5], [6]]. PC flow MRI can be used not only for measuring and characterizing complex blood flow but also estimating wall shear stress which is known to be associated with the development of high-risk plaque [7,8]. Owing to significant advances in pulse sequences, reconstruction algorithms and analysis techniques, PC MRI is now a necessary component of clinical protocols for patients with vascular disease.

The scan time of PC flow MRI is inevitably long due to the requirements of multiple acquisitions with varying bipolar gradients as well as high spatial and temporal resolution. Since the long scan time prevents more widespread use in clinical practice, various scan acceleration techniques have been applied in PC MRI. Non-2DFT readouts were the earliest approaches for accelerating PC MRI [9,10]. Parallel imaging such as SENSE and GRAPPA is now the reference acceleration technique for PC flow imaging as in other MRI applications [11,12]. Compressed sensing (CS), and low-rank-based reconstruction, as another general technique for accelerating MRI, can be used also for PC MRI, contributing to higher rate acceleration [[13], [14], [15], [16], [17], [18]]. More recently, PC-MRI-specific techniques were proposed, which reduce the total number of bipolar gradients for velocity-encoding and thus improve effective temporal resolution [19,20].

In this study, we propose another PC-MRI-specific technique which can improve image reconstruction from under-sampled PC data. The underlying rationale is that the magnitudes in reconstructed images barely vary over different bipolar gradients, which is embodied in the form of regularization of the cost function for image reconstruction. The feasibility of the proposed magnitude-regularized reconstruction is shown in healthy subjects and patients with peripheral artery disease (PAD).

Section snippets

Magnitude regularized compressed sensing

Phase contrast flow MRI is based on the sensitivity of bipolar gradient to the phase of moving spin. By subtracting two phase images obtained from different bipolar encoded sequences, one can remove the background phase and obtain spin's velocity value in the direction of the applied gradient. Since the reference and the bipolar encoded sequences have identical sequence parameters except the bipolar gradients, magnitude images, while not used for clinical diagnosis, should be similar to each

Results

Fig. 1 shows representative mean and peak velocities (a), and magnitude and phase images at the peak flow time (b) for the reference (full sampling) and three under-sampling schemes. Over 120 ROIs (resulting from 30 time frames and right and left arteries in two subjects), the RMS errors of mean and peak velocities were 0.38/0.92/1.26 cm/s and 1.59/3.04/7.11 cm/s, respectively for the undersamplings with no overlap, 50% overlap and 100% overlap between two bipolar-encoded data in the peripheral

Discussion

Regularization has long been used in various forms for image reconstruction from partially acquired data as it can improve the condition of corresponding ill-posed system matrix. While l2-norm of solution vector itself and l1-norm of finite difference are popular examples of regularization, the best type would depend on the nature of solution to be sought. PC flow MRI acquires multiple complex-valued data where phase values dominantly vary based on the size of the applied bipolar gradient but

Conclusions

We have developed a reconstruction algorithm for accelerating PC flow MRI, which combines the conventional compressed sensing reconstruction with magnitude regularization. The proposed magnitude regularization utilizes the magnitude similarity among different bipolar encoded data, and has shown to improve reconstruction accuracy as demonstrated by healthy and patient subject studies. The performance of the proposed technique in cases of arterial pathology needs to be further investigated.

CRediT authorship contribution statement

Taehoon Shin: Conceptualization, Methodology, Software, Writing - original draft, Funding acquisition, Supervision.Wanyong Shin: Software, Writing - review & editing.

Acknowledgement

This work has been supported by NRF-2019R1F1A1058872 and NIH R01 HL135500.

References (29)

  • S Ding et al.

    Improved coverage in dynamic contrast-enhanced cardiac MRI using interleaved gradient-echo EPI

    Magn Reson Med

    (1998)
  • KP Pruessmann et al.

    SENSE: sensitivity encoding for fast MRI

    Magn Reson Med

    (1999)
  • MA Griswold et al.

    Generalized autocalibrating partially parallel acquisitions (GRAPPA)

    Magn Reson Med

    (2002)
  • M Lustig et al.

    Sparse MRI: the application of compressed sensing for rapid MR imaging

    Magn Reson Med

    (2007)
  • Cited by (2)

    • Cerebral metabolic rate of oxygen (CMRO<inf>2</inf>) changes measured with simultaneous tDCS-MRI in healthy adults

      2022, Brain Research
      Citation Excerpt :

      The acquisition time of each PC-MRI was 10 seconds. Velocity encoding (VENC) parameter was 60 cm/s for arterial flow, which is in the range of what commonly used (60–100 cm/s) (Debbich et al., 2020; Mendieta et al., 2020; Rivera-Rivera et al., 2021; Shin and Shin, 2020). The resulting phase and magnitude PC-MRI images therefore represent a single timepoint measurement from each of the three sessions (pre-, during- and post-tDCS).

    View full text