[HTML][HTML] Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time
Diagnostic and interventional imaging, 2023•Elsevier
Purpose The purpose of this study was to evaluate the impact of a deep learning-based
super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric
interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T
compared to standard VIBE imaging (VIBE STD). Materials and methods This retrospective
single-center study was conducted between April 2021 and October 2021. Fifty patients with
a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 …
super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric
interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T
compared to standard VIBE imaging (VIBE STD). Materials and methods This retrospective
single-center study was conducted between April 2021 and October 2021. Fifty patients with
a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 …
Purpose
The purpose of this study was to evaluate the impact of a deep learning-based super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T compared to standard VIBE imaging (VIBESTD).
Materials and methods
This retrospective single-center study was conducted between April 2021 and October 2021. Fifty patients with a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 men and 23 women, with a mean age of 69 ± 13 (standard deviation [SD]) years (age range: 33–89 years). VIBESTD (precontrast, dynamic, postcontrast) was retrospectively processed with a deep learning-based super-resolution algorithm including a more aggressive partial Fourier setting leading to a simulated acquisition time reduction (VIBESR). Image analysis was performed by two radiologists regarding lesion detectability, noise levels, sharpness and contrast of pancreatic edges, as well as regarding diagnostic confidence using a 5-point Likert-scale with 5 being the best.
Results
VIBESR was rated better than VIBESTD by both readers regarding lesion detectability (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5], for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5]) for reader 2; both P <0.001), noise levels (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), sharpness and contrast of pancreatic edges (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), as well as regarding diagnostic confidence (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001). There were no significant differences between lesion sizes as measured by the two readers on VIBESR and VIBESTD images (P > 0.05). The mean acquisition time for VIBESTD (15 ± 1 [SD] s; range: 11–16 s) was longer than that for VIBESR (13 ± 1 [SD] s; range: 11–14 s) (P < 0.001).
Conclusion
Our results indicate that the newly developed deep learning-based super-resolution algorithm adapted to partial Fourier acquisitions has a positive influence not only on shortening the examination time but also on improvement of image quality in pancreatic MRI.
Elsevier
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