A fully unsupervised approach to create patient-like phantoms via Convolutional neural networks

Junyu Chen; Ye Li; Eric Frey

Meeting ReportPhysics, Instrumentation & Data Sciences

A fully unsupervised approach to create patient-like phantoms via Convolutional neural networks

Junyu Chen, Ye Li and Eric Frey

Journal of Nuclear Medicine May 2020, 61 (supplement 1) 522;

Junyu Chen

1Johns Hopkins University Baltimore MD United States

2Johns Hopkins University Baltimore MD United States

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Ye Li

1Johns Hopkins University Baltimore MD United States

2Johns Hopkins University Baltimore MD United States

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Eric Frey

1Johns Hopkins University Baltimore MD United States

2Johns Hopkins University Baltimore MD United States

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Comparison of SSIM and MSE between the proposed method and the SyN algorithm.
Method SSIM MSE
Affine Only 0.828±0.008 69.213±2.748
Ours 0.955±0.007 37.340±5.078
SyN (MSE) 0.884±0.011 51.999±4.135
SyN (MI) 0.881±0.011 55.059±3.996
SyN (CC) 0.886±0.011 52.838±4.138

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Journal of Nuclear Medicine

Vol. 61, Issue supplement 1

May 1, 2020

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A fully unsupervised approach to create patient-like phantoms via Convolutional neural networks

Junyu Chen, Ye Li, Eric Frey

Journal of Nuclear Medicine May 2020, 61 (supplement 1) 522;

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A fully unsupervised approach to create patient-like phantoms via Convolutional neural networks

Junyu Chen, Ye Li, Eric Frey

Journal of Nuclear Medicine May 2020, 61 (supplement 1) 522;

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Method	SSIM	MSE
Affine Only	0.828±0.008	69.213±2.748
Ours	0.955±0.007	37.340±5.078
SyN (MSE)	0.884±0.011	51.999±4.135
SyN (MI)	0.881±0.011	55.059±3.996
SyN (CC)	0.886±0.011	52.838±4.138