PCa-Mamba: Spatiotemporal state space models for prostate cancer detection in multi-parametric MRI.
Authors
Affiliations (2)
Affiliations (2)
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 90045, CA, USA; School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, Shanghai, China. Electronic address: [email protected].
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, 90045, CA, USA.
Abstract
Multiparametric Magnetic Resonance Imaging (mpMRI), including T2-weighted imaging (T2), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging is an important technique for the diagnosis of clinically significant prostate cancer (csPCa). Unlike T2 and DWI, which capture spatial contrast, DCE-MRI captures the temporal dynamics before, during, and after the administration of the contrast agent. The temporal dynamics of DCE-MRI characterize the microvascular properties of target tissue, offering valuable information for csPCa diagnosis. However, due to the high data dimensionality and spatial-temporal data incompatibility, DCE-MRI has been largely overlooked or underutilized in prevalent deep learning-based csPCa detection methods. In this paper, we propose PCa-Mamba, a state-space model (SSM)-based framework that, for the first time, fully incorporates the temporal dynamics of DCE-MRI alongside the spatial contrast of T2 and DWI for csPCa detection in mpMRI. SSMs are efficient for long-range sequence modeling, making them well-suited for high-dimensional, heterogeneous mpMRI data. PCa-Mamba comprises two SSM modules: (i) a temporal SSM that captures the temporal dynamics, and (ii) a spatial SSM that extracts spatial contrast. Pharmacokinetic (PK) regularization is introduced to constrain the temporal SSM with the Tofts model, allowing the model to learn the PK properties of the target tissue while reducing overfitting. Permuted sequentialization is introduced into the spatial SSM to rearrange spatial patches along different scan directions. This enhances spatial context while preserving local adjacency. The spatial and temporal features are jointly optimized by interactive fusion for integrated spatiotemporal representations, effectively capturing the complementary information for csPCa detection. We further introduce a dropout mechanism that randomly excludes DCE-MRI during training, allowing the model to handle practical scenarios where DCE-MRI is unavailable. Extensive experiments on our in-house dataset and the PI-CAI dataset demonstrate PCa-Mamba's superiority over existing models for csPCa detection, as well as the advantages of mpMRI over bpMRI in lesion-wise csPCa diagnosis, especially for detecting small lesions and those located in the peripheral zone.