Rationale and Objectives
To investigate the impact of a prototypical deep learning–based super-resolution reconstruction
algorithm tailored to partial Fourier acquisitions on acquisition time and image quality
for abdominal T1-weighted volume-interpolated breath-hold examination (VIBESR) at 3 Tesla. The standard T1-weighted images were used as the reference standard
(VIBESD).
Materials and Methods
Patients with diverse abdominal pathologies, who underwent a clinically indicated
contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and
June 2021 were retrospectively included. Following the acquisition of the standard
VIBESD sequences, additional images for the non-contrast, dynamic contrast-enhanced and
post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using
the same raw data and employing a prototypical deep learning-based super-resolution
reconstruction algorithm. The algorithm was designed to enhance edge sharpness by
avoiding conventional k-space filtering and to perform a partial Fourier reconstruction
in the slice phase-encoding direction for a predefined asymmetric sampling ratio.
In the retrospective reconstruction, the asymmetric sampling was realized by omitting
acquired samples at the end of the acquisition and therefore corresponding to a shorter
acquisition. Four radiologists independently analyzed the image datasets (VIBESR and VIBESD) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness
of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image
quality and diagnostic confidence. These parameters were statistically compared and
interrater reliability was computed using Fleiss’ Kappa and intraclass correlation
coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented
and was statistically compared using the paired Wilcoxon test.
Results
A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included.
For VIBESR, breath-hold time was significantly reduced by approximately 13.6% (VIBESR 11.9 ± 1.2 seconds vs. VIBESD: 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to
be superior in images with VIBESR (p values ranged between p = 0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image
quality and diagnostic confidence were not compromised, as there was no evidence of
a difference between VIBESR and VIBESD (p > 0.05). The inter-reader agreement was substantial with a Fleiss’ Kappa of >0.7
in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers
observed a superior lesion conspicuity in VIBESR than in VIBESD (p values ranged between p = 0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBESD and VIBESR for all readers. Finally, there was an excellent inter-reader agreement regarding
lesion size (ICC > 0.9). For all readers, no statistically significant difference
was observed regarding detection of hepatic lesions between VIBESD and VIBESR.
Conclusion
The deep learning-based super-resolution reconstruction with partial Fourier in the
slice phase-encoding direction enabled a reduction of breath-hold time and improved
image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal
magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising
image quality or diagnostic confidence was possible by using this deep learning-based
reconstruction technique.
Key Words
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Article info
Publication history
Published online: July 06, 2022
Accepted:
June 4,
2022
Received in revised form:
May 20,
2022
Received:
April 6,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
