TY - GEN
T1 - Learning to Generate Missing Pulse Sequence in MRI using Deep Convolution Neural Network Trained with Visual Turing Test
AU - Kumar, Vikas
AU - Sharma, Manoj Kumar
AU - Jehadeesan, Ramalingam
AU - Venkatraman, Balasubramaniam
AU - Suman, Garima
AU - Patra, Anurima
AU - Goenka, Ajit H.
AU - Sheet, Debdoot
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Magnetic resonance imaging (MRI) is widely used in clinical applications due to its ability to acquire a wide variety of soft tissues using multiple pulse sequences. Each sequence provides information that generally complements the other. However, factors like an increase in scan time or contrast allergies impede imaging with numerous sequences. Synthesizing images of such non acquired sequences is a challenging proposition that can suffice for corrupted acquisition, fast reconstruction prior, super-resolution, etc. This manuscript employed a deep convolution neural network (CNN) to synthesize multiple missing pulse sequences of brain MRI with tumors. The CNN is an encoder-decoder-like network trained to minimize reconstruction mean square error (MSE) loss while maximizing the adversarial attack. It inflicts on a relativistic Visual Turing Test discriminator (rVTT). The approach is evaluated through experiments performed with the Brats2018 dataset, quantitative metrics viz. MSE, Structural Similarity Measure (SSIM), and Peak Signal to Noise Ratio (PSNR). The Radiologist and MR physicist performed the Turing test with 76% accuracy, demonstrating our approach's performance superiority over the prior art. We can synthesize MR images of missing pulse sequences at an inference cost of 350.71 GFlops/voxel through this approach.
AB - Magnetic resonance imaging (MRI) is widely used in clinical applications due to its ability to acquire a wide variety of soft tissues using multiple pulse sequences. Each sequence provides information that generally complements the other. However, factors like an increase in scan time or contrast allergies impede imaging with numerous sequences. Synthesizing images of such non acquired sequences is a challenging proposition that can suffice for corrupted acquisition, fast reconstruction prior, super-resolution, etc. This manuscript employed a deep convolution neural network (CNN) to synthesize multiple missing pulse sequences of brain MRI with tumors. The CNN is an encoder-decoder-like network trained to minimize reconstruction mean square error (MSE) loss while maximizing the adversarial attack. It inflicts on a relativistic Visual Turing Test discriminator (rVTT). The approach is evaluated through experiments performed with the Brats2018 dataset, quantitative metrics viz. MSE, Structural Similarity Measure (SSIM), and Peak Signal to Noise Ratio (PSNR). The Radiologist and MR physicist performed the Turing test with 76% accuracy, demonstrating our approach's performance superiority over the prior art. We can synthesize MR images of missing pulse sequences at an inference cost of 350.71 GFlops/voxel through this approach.
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U2 - 10.1109/EMBC46164.2021.9630435
DO - 10.1109/EMBC46164.2021.9630435
M3 - Conference contribution
C2 - 34891974
AN - SCOPUS:85122503939
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3419
EP - 3422
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -