Image quality assessment and white matter hyperintensity quantification in two accelerated high-resolution 3D FLAIR techniques: Wave-CAIPI and deep learning–based SPACE

Abstract

Aim

To compare the image quality obtained using two accelerated high-resolution 3D fluid-attenuated inversion recovery (FLAIR) techniques for the brain—deep learning–reconstruction SPACE (DL-SPACE) and Wave-CAIPI FLAIR.

Materials and methods

A total of 123 participants who underwent DL-SPACE and Wave-CAIPI FLAIR brain imaging were retrospectively reviewed. In a qualitative analysis, two radiologists rated the quality of each image, including the overall image quality, artifacts, sharpness, fine-structure conspicuity, and lesion conspicuity based on Likert scales. In a quantitative analysis, the signal-to-noise ratio (SNR) for the normal-appearing white matter (NAWM) and lesion and the contrast-to-noise ratio (CNR) for a lesion were calculated and compared. Moreover, the volumes of white matter hyperintensities (WMHs) obtained with the two techniques were automatically quantified and compared.

Results

The DL-SPACE FLAIR technique demonstrated a significantly higher fine-structure conspicuity (P < 0.001), lower degree of artifacts (P < 0.001) and higher overall image quality (P = 0.001). The mean SNR values were significantly higher with the DL-SPACE FLAIR technique (NAWM, 43.95 vs. 31.6; lesion, 31.35 vs. 21.28; all, P < 0.001). Additionally, the mean CNR of the WMH was significantly higher with the DL-SPACE FLAIR technique (11.34 vs. 8.22; P < 0.001). The periventricular and deep WMH volumes were significantly larger with the DL-SPACE FLAIR technique (1.91 ± 4.69 vs. 1.54 ± 4.18; P < 0.001 and 0.26 ± 0.42 vs. 0.23 ± 0.38; P = 0.002, respectively).

Conclusion

The DL-SPACE FLAIR technique produced images with superior quality, SNR and CNR compared with the Wave-CAIPI FLAIR technique with the same acquisition time.