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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