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Fig. 1 | BMC Medical Imaging

Fig. 1

From: Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment

Fig. 1

Representative figures from a healthy volunteer (upper row: coverage of the whole brain; lower low: magnification of cortical/subcortical areas). Due to denoising of deep learning reconstruction (DLR), the signal-to-noise ratio significantly increased in CS2 with DLR (DLR-CS2) compared with CS2 without DLR and in CS4 with DLR (DLR-CS4) compared with CS4 without DLR. DLR-CS2 provided images with quality comparable to that of the reference standard. CS4 without DLR showed the worst results for qualitative analysis among the five sequences, with notable artifacts in the white matter and reduced sharpness in the border between the gray and white matter. Applying DLR to CS4 (DLR-CS4) enhanced the image quality by decreasing artifacts in the white matter and increasing sharpness in the border between gray and white matter, making its quality comparable to that of CS2. It is notable that DLR-CS4 and CS2 achieved similar qualitative analysis results, whereas the scan time of DLR-CS4 was reduced by approximately half (2 min 6 s vs. 4 min 6 s)

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BMC Medical Imaging

ISSN: 1471-2342

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