Improved pharmacokinetic parameter estimation from DCE-MRI via spatial-temporal information-driven unsupervised learning.

Abstract

Objective.Pharmacokinetic (PK) parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide quantitative characterization of tissue perfusion and permeability. However, existing deep learning methods for PK parameter estimation rely on either temporal or spatial features alone, overlooking the integrated spatial-temporal characteristics of DCE-MRI data. This study aims to remove this barrier by fully leveraging the spatial and temporal information to improve parameter estimation.Approach.A spatial-temporal information-driven unsupervised deep learning method (STUDE) was proposed. STUDE combines convolutional neural networks (CNNs) and a customized Vision Transformer to separately capture spatial and temporal features, enabling comprehensive modeling of contrast agent dynamics and tissue heterogeneity. Besides, a spatial-temporal attention feature fusion module was proposed to enable adaptive focus on both dimensions for more effective feature fusion. Moreover, the extended Tofts model imposed physical constraints on PK parameter estimation, enabling unsupervised training of STUDE. The accuracy and diagnostic value of STUDE was compared with the orthodox non-linear least squares (NLLS) and representative deep learning-based methods (i.e. gated recurrent unit, convolutional neural network, U-Net, and VTDCE-Net) on a numerical brain phantom and 87 glioma patients, respectively.Main results.On the numerical brain phantom, STUDE produced PK parameter maps with the lowest systematic and random errors even under low signal-to-noise ratio (SNR) conditions (SNR = 10 dB). On glioma data, STUDE generated parameter maps with reduced noise compared to NLLS and demonstrated superior structural clarity compared to other methods. Furthermore, STUDE outshined all other methods in the identification of glioma isocitrate dehydrogenase mutation status, achieving the area under the curve (AUC) values at 0.840 and 0.908 for the receiver operating characteristic curves ofK^transand V_e, respectively. A combination of all PK parameters improved AUC to 0.926.Significance.STUDE advances spatial-temporal information-driven and physics-informed learning for precise PK parameter estimation, demonstrating its potential clinical significance.