Transferring deep convolutional network representations from SPECT to improve PET cardiac outcome prediction

Bryan Bednarski; Aakash D. Shanbhag; Ananya D. Singh; Robert J. H. Miller; Heidi Gransar; Keiichiro Kuronuma; Tali Sharir; Sharmila Dorbala; Marcelo F. Di Carli; Matthews B. Fish; Terrence D. Ruddy; Daniel Berman; Damini Dey; Piotr Slomka

doi:10.1117/12.2654439

3 April 2023 Transferring deep convolutional network representations from SPECT to improve PET cardiac outcome prediction

Bryan Bednarski, Aakash D. Shanbhag, Ananya D. Singh, Robert J. H. Miller, Heidi Gransar, Keiichiro Kuronuma, Tali Sharir, Sharmila Dorbala, Marcelo F. Di Carli, Matthews B. Fish, Terrence D. Ruddy, Daniel Berman, Damini Dey, Piotr Slomka

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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124640F (2023) https://doi.org/10.1117/12.2654439
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Cardiac PET, less common than SPECT, is rapidly growing and offers the additional benefit of first-pass absolute myocardial blood flow measurements. However, multicenter cardiac PET databases are not well established. We used multicenter SPECT data to improve PET cardiac risk stratification via a deep learning knowledge transfer mechanism.

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Bryan Bednarski, Aakash D. Shanbhag, Ananya D. Singh, Robert J. H. Miller, Heidi Gransar, Keiichiro Kuronuma, Tali Sharir, Sharmila Dorbala, Marcelo F. Di Carli, Matthews B. Fish, Terrence D. Ruddy, Daniel Berman, Damini Dey, and Piotr Slomka "Transferring deep convolutional network representations from SPECT to improve PET cardiac outcome prediction", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124640F (3 April 2023); https://doi.org/10.1117/12.2654439

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