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Abstract #3036

Network structure impacts on quantitative transport mapping network for compartment model inversion in perfusion image quantification

QIhao Zhang1,2, Dominick Romano3, Renjiu Hu2, Benjamin Weppner2, Thanh Nguyen2, Pascal Spincemaille1, and Yi Wang2
1Weill Cornell Medicine, New York, NY, United States, 2Weill Cornell Medicine, New York, NY, United States, 3Cornell University, New York, NY, United States

Synopsis

Keywords: Cancer, DSC & DCE Perfusion, Head & Neck tumor Imaging; Quantitative perfusion imaging; Deep learning image processing

Motivation: The accuracy of deep learning methods for multi-compartment perfusion modeling may be affected by the choice of network structure.

Goal(s): To compare various deep learning network structures on the accuracy of perfusion parameter estimation using a previously proposed deep learning method QTMnet.

Approach: Four different architectures were trained on synthetically generated perfusion data and tested on nasopharyngeal carcinoma DCE MRI images.

Results: Among the architectures studied, CMUNeXt provided the highest accuracy in simulation while Attention U-net provided the best contrast between tumor and normal tissue in the DCE MRI data.

Impact: CMUNeXt and attentation U-net improve accuracy QTMnet based perfusion parameter estimates.

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Keywords