Mamba-Enhanced 3D Medical Image Reconstruction for Low-Dose Computed Tomography
Keywords:
low-dose computed tomography, Mamba, state space models, medical image reconstruction, regulatory governance, health systems, fairness, sustainabilityAbstract
Low-dose computed tomography has become a central strategy for reducing patient radiation exposure, but it introduces substantial reconstruction challenges including amplified noise, structured artifacts, and degraded tissue contrast. This paper examines the systems-level integration of Mamba-based state space models into three-dimensional medical image reconstruction pipelines for low-dose computed tomography. Rather than treating reconstruction as an isolated algorithmic task, the discussion positions Mamba-enhanced reconstruction within a broader socio-technical infrastructure that includes data governance, computational deployment, regulatory oversight, clinical accountability, and environmental sustainability. The analysis explores how selective state space mechanisms can support efficient long-range volumetric modeling while addressing the architectural trade-offs associated with convolutional neural networks and transformer-based models. The paper further considers the operational requirements of hospital-scale deployment, including interoperability with picture archiving and communication systems, model drift monitoring, failover planning, and postmarket surveillance. Emphasis is placed on fairness across patient populations, robustness under scanner heterogeneity, and the need for transparent validation frameworks that align with evolving regulatory expectations. The discussion argues that the value of Mamba-enhanced reconstruction depends not only on image quality metrics but also on the broader governance structures that ensure safe, equitable, and resilient clinical use. Future directions include federated learning, modular regulatory certification, and energy-aware model design. The paper contributes a cross-disciplinary perspective on how next-generation sequence modeling can be responsibly translated into low-dose computed tomography practice.
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