Keywords: Diagnosis/Prediction, DSC & DCE Perfusion, Prostate, DCE
Motivation: Existing techniques for DCE-MRI analysis is time-consuming and often assume a fixed arterial input function (AIF) across various locations and patients, leading to imprecise outcomes.
Goal(s): 1) The development of a Deep learning model for fast DCE-MRI analysis, and 2) the design of location- and patient-specific AIFs.
Approach: We use deep-learning model for fast DCE-MRI analysis, and propose to represent dispersion-applied AIF to allow for location- and subject-specific AIFs by interpolation between constant AIFs.
Results: 1) Reduced per-patient processing time by one-tenth, 2) improved fitting accuracy, and 3) higher-contrast parameteric maps between the lesion and normal tisue.
Impact: Other scientists, clinicians and patients may benefit from the faster processing time, and higher-contrast parametric maps for cancer diagnosis.
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