Article Figures & Data
Figures
- FIG 1.
Residual inception DenseNet (RID). A, RID model for virtual contrast enhancement (vT1c prediction) and enhancing tumor (ET) segmentation. B, RID model for whole tumor (WT) segmentation.
- FIG 2.
Residual inception DenseNet (RID). A, RID model for whole tumor segmentation. B, RID model for virtual contrast enhancement and enhancing tumor segmentation.
- FIG 3.
Building blocks of residual inception network. From left to right, dense block, convolution block, transition block, and projection block.
- FIG 4.
Synthesized virtual contrast enhanced T1w (vT1c) images in 3 different subjects. Ground truth (left column) and synthesized vT1c (right column) image pairs for 9 subjects.
- FIG 5.
Mosaic plot illustrating the distribution of 3 expert radiologists and their consensus along a 3-point Likert scale.
- FIG 6.
Importance of input sequences example. Top row, input images: T1w, FLAIR, T2, and the ground truth T1c. Bottom row, output images with (A) all inputs (T1w, FLAIR, and T2w) given to the model, (B) T1w replaced with zeros in the input, (C) FLAIR replaced with zeros in the input, and (D) T2 replaced with zeros in the input. The T2 and FLAIR inputs together provide contrast enhancement prediction, whereas T1w input provides primarily anatomic detail.
Tables
- Table 1:
Quantitative evaluation. Analysis of virtual enhancement prediction by using various masks generated by an external model
Mask SSIM NMSE Dice PSNR Whole brain 0.91 0.03 0.32 64.35 Whole tumor 0.90 0.01 0.35 48.99 Enhancing tumor 0.90 0.01 0.62 49.93 - Table 2:
Quantitative presence and location of the under/overestimation of synthetic contrast enhancement, the introduction of artifacts, and the image quality improvement on vT1c
Reviewer 1 (FY) Reviewer 2 (MA) Reviewer 3 (JD) Overestimate (O) 26 27 20 Underestimate (U) 71 69 58 Both (O and U) 11 8 34 Central 54 35 64 Peripheral 94 71 97 False distant enhancement 10 9 15 Missed distant enhancement 5 4 2 Artifact 17 22 25 Image quality improved 10 NA 3 Note:—NA indicates not applicable.
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