Hao Li1, Martin John Graves2, Nadeem Shaida2, Akash Prashar2, David John Lomas1, and Andrew Nicholas Priest2
1Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 2Department of Radiology, Addenbrooke’s Hospital, Cambridge, United Kingdom
Synopsis
We implemented
two advanced reconstruction methods for highly accelerated subtractive NCE-MRA,
which can exploit the sparsity of subtracted angiograms. One method is based on
k-space subtraction of complex raw data
with phase and intensity correction (KSPIC). Another method is to reconstruct bright-
and dark-blood data with an additional magnitude subtraction term in the cost
function to exploit the sparsity. The
performance of the two methods was evaluated in both retrospective and
prospective accelerated datasets using quantitative metrics and qualitative
scoring. Compared with conventional methods, they both showed improved image reconstruction
quality, while KSPIC had the best performance.
Introduction
Subtractive non-contrast-enhanced MR angiography (NCE-MRA)
techniques, such as fresh-blood-imaging
(FBI)1, display vasculature by
subtracting dark-blood images from bright-blood images. Different acceleration techniques can be used to reduce
their long acquisition times, including compressed sensing (CS), parallel
imaging (PI)2 and partial Fourier sampling (PF)1.
Different
reconstruction strategies can be used for accelerated subtractive NCE-MRA. A
common method is to perform reconstruction on bright- and dark-blood datasets
separately followed by magnitude subtraction (MS), however this does not take
advantage of sparsity in the subtracted angiogram. Rapacchi et al. proposed an improved method that combines
bright- and dark-blood reconstruction together and adds a magnitude subtraction
term in the cost function to exploit the sparsity3 (here called ‘combined magnitude subtraction’, CMS). Complex subtraction in k-space (KS) prior to reconstruction is
an alternative approach to promote sparsity. However, the signal of some
tissues becomes negative after subtraction and appears as background artefacts
due to the reversed polarity4,5.
In this
study, k-space Subtraction with Phase
and Intensity Correction (KSPIC), is implemented in 3D femoral FBI. It uses a
phase correction procedure to recover the polarity of negative values and an
intensity correction (IC) procedure6 that uses a weighted subtraction to suppress residual background signal. CMS was
also implemented for comparison, with IC applied as a post-processing procedure
to further improve background suppression (CMS-IC). Their performance is
compared with conventional KS and MS in both retrospective and prospective acceleration
studies. Methods
KSPIC is based on the k-space data after weighted subtraction (Fig.1A).
A background phase reference is acquired from the symmetric central region of the
dark-blood data to recover the polarity of negative values in the subtracted
images. The central region is also used for calibration in a SPIRiT
reconstruction7 and weight calculation for adaptive
combination8 of different channels. PF is applied
in both ky and kz directions.
The weighting factor in subtraction is
calculated by a robust linear regression model6. For KSPIC, regression is performed on partial images reconstructed
by a fast CS reconstruction prior to the full reconstruction (Fig.1B), while
for CMS, regression is performed on the fully reconstructed images (Fig.1C).
The split-Bregman algorithm9 with total variation minimisation is
used for CS reconstruction. Due to its rapid convergence, only one and ten iterations
are used for the fast and full CS reconstruction respectively.
Twelve healthy subjects were imaged
using a 1.5 T MR450 system (GE Healthcare, Waukesha, WI). Parameters included
ETL 60-90, FOV 40-44 cm, slice thickness 2 mm, TE 45-60ms, TR 2 or 3
heartbeats. For retrospective simulated reconstruction, ten fully sampled
femoral FBI datasets (224×224×80, 360 TRs) were acquired and accelerated using
acceleration factors (AFs) from 4 to 20. Peak signal-to-noise-ratio (PSNR) and
structural similarity index measure (SSIM) were calculated using the fully
sampled images as reference7. Contrast-to-noise ratio (CNR) of
artery-to-background10 and sharpness evaluation11 were also used as no-reference
quality metrics.
Ten
volunteers were imaged in the
prospective study, each with four datasets accelerated by 10× (84 TRs), 15× (58 TRs), 20× (44 TRs)
and 25× (36 TRs) respectively. Images reconstructed by four different methods were
assessed by two experienced radiologists, blinded to reconstruction methods and
AFs and in a randomised order. Results and discussion
Fig. 2 shows MIPs of prospective
acceleration using KS, MS, CMS-IC and KSPIC. Residual background tissues, such
as veins, can be observed on the MS images. CMS-IC has improved background
suppression by employing IC, while KSPIC has the best suppression. All the four
methods achieved good image quality at 10× acceleration, but image blurring and
artefacts appear on MS and CMS-IC at 25× acceleration. KS shows impaired delineation
of small branches.
Fig. 3 shows the quantitative
evaluation of seven different methods in simulated acceleration. KS with phase
correction has the best performance in terms of SSIM, CNR and sharpness both
with IC (KSPIC) and without IC (KSPC). CMS/CMS-IC and MS/MS-IC have a higher
PSNR when the AF is smaller than eight. KS has the worst performance.
Subjective scores are summarised in
Fig. 4. KSPIC has the highest score at all AFs in terms of vessel delineation,
images noise and artefact, and background and venous contamination. CMS-IC shows
improved performance compared with MS, but does not outperform KS in terms of noise,
artefact and background suppression.
The phase reference correction can be performed
based on either bright- or dark-blood data, but in many cases dark-blood data led
to reduced artefacts (Fig. 5A). The size of the centre region used for phase
correction is also important. In this study, 12-20 was the optimal range of the
region length (width=1/2 length) (Fig. 5B). Using a larger size results in arterial
phase change in the dark-blood images (systolic acquisition) (Fig. 5C), leading
to central signal loss in the arteries (Fig. 5D).Conclusion
Compared with CMS and conventional MS
and KS reconstruction methods, KSPIC has the best reconstruction performance in
all the quantitative and qualitative measurements, permitting good image
quality to be maintained up to higher AFs. In conjunction with a post-processing
IC procedure, CMS can also improve background suppression and vessel
delineation. Acknowledgements
The authors acknowledge the support of the Addenbrooke’s Charitable Trust and the NIHR Cambridge Biomedical Research Centre. Hao Li acknowledges the China Scholarship Council and Cambridge Trust for fellowship support.References
1. Miyazaki M,
Sugiura S, Tateishi F, Wada H, Kassai Y, Abe H. Non-contrast-enhanced MR
angiography using 3D ECG-synchronized half-Fourier fast spin echo. J. Magn.
Reson. Imaging 2000;12:776–783.
2. Storey P,
Otazo R, Lim RP, Kim S, Fleysher L, Oesingmann N, Lee VS, Sodickson DK.
Exploiting sparsity to accelerate noncontrast MR angiography in the context of
parallel imaging. Magn. Reson. Med. 2012;67:1391–1400.
3. Rapacchi S,
Han F, Natsuaki Y, Kroeker R, Plotnik A, Lehrman E, Sayre J, Laub G, Finn JP,
Hu P. High spatial and temporal resolution dynamic contrast-enhanced magnetic
resonance angiography using compressed sensing with magnitude image
subtraction. Magn. Reson. Med. 2014;71:1771–1783.
4. Li H,
Priest AN, Graves MJ, Lomas DJ. Highly accelerated NCE-MRA: Phase correction to
remove background artefacts for complex subtraction. In: Proceedings of the
27th Annual Meeting of ISMRM, Montreal, Canada. ; 2019. p. 3534.
5. Li H,
Priest AN, Graves MJ, Lomas DJ. Highly Accelerated NCE-MRA Using Complex
Subtraction with Intensity Correction: Improved Reconstruction Accuracy and
Background Tissue Suppression. In: Proceedings of the 27th Annual Meeting of
ISMRM, Montreal, Canada. ; 2019. p. 834.
6. Li H, Shuo
W, Priest AN, Graves MJ, Lomas DJ. Background tissue suppression for
subtractive NCE-MRA techniques based on robust regression using the deviation
angle. In: Proceedings of the 27th Annual Meeting of ISMRM, Montreal, Canada. ;
2019. p. 7231.
7. Lustig M,
Pauly JM. SPIRiT: Iterative self-consistent parallel imaging reconstruction
from arbitrary k-space. Magn. Reson. Med. 2010;64:457–471.
8. Walsh DO, Gmitro AF, Marcellin MW. Adaptive reconstruction
of phased array MR imagery. Magn. Reson. Med. 2000;43:682–690.
9. Goldstein T, Osher S. The Split Bregman Method for
L1-Regularized Problems. SIAM J. Imaging Sci. 2009;2:323–343.
10. Horé A,
Ziou D. Image quality metrics: PSNR vs. SSIM. In: Proceedings - International
Conference on Pattern Recognition. ; 2010. pp. 2366–2369. doi:
10.1109/ICPR.2010.579.
11. Akasaka T, Fujimoto K, Yamamoto T, Okada T, Fushumi Y,
Yamamoto A, Tanaka T, Togashi K. Optimization of regularization parameters in
compressed sensing of magnetic resonance angiography: Can statistical image
metrics mimic radiologists’ perception? PLoS One 2016;11:1–14.
12. Ahmad R, Ding Y, Simonetti OP. Edge sharpness assessment
by parametric modeling: Application to magnetic resonance imaging. Concepts
Magn. Reson. Part A 2015;44:138–149.