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. 2019 Dec 17;10:1305. doi: 10.3389/fneur.2019.01305

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Copyright © 2019 Jeong, Lee, John, Robinette, Amit-Yousif, Barger, Mittal and Juhász.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Representative example of AMT PET-learned MRI-based tumor volume: PM(x) (voxels inside red contour) where multi-modal MRI data of Patient No. 13 (images acquired with Philips Protocol) were separately analyzed by U-Net_1(Siemens) (1st row), U-Net_2(Philips) (2nd row), U-Net₃ (3rd row), and U-Net₄ (4th row). U-Net_2(Philips) and U-Net₃ learned multi-modal MRI of Patient 13 and outperformed the other two U-Net systems to spatially match P(x) with the target, AMT-PET tumor volume: P(x) (blue contour), sensitivity/specificity/PPV/NPV = 0.65/1.00/0.40/1.00, 0.95/1.00/0.79/1.00, 0.94/1.00/0.75/1.00, and 0.91/1.00/0.68/1.00 for U-Net_1(Siemens), U-Net_2(Philips), U-Net₃ and U-Net₄, respectively. For comparison, T1-Gad tumor volume: M(x) (voxels inside green contour) was superimposed. White box indicates the region of interest where T1-Gad, T2, FLAIR, ADC map, and AMT-PET slices were captured to show the contours.