Automated CT report generation remains challenging because it requires both fine-grained grounding of pathological entities in multi-view volumetric evidence and clinically coherent language that reflects diagnostic priors from historical cases. Existing methods often rely on noisy cross-modal alignment or underutilize retrieved report knowledge during decoding. We propose HEMP, which is a Hierarchical Entity-aligned Memory Prompting framework for LLM-based CT report generation. HEMP first performs two-stage entity-visual alignment where graph-constrained entity-conditioned attention retrieves initial evidence and an optimal transport refinement enforces principled coupling between entity nodes and visual tokens under marginal constraints. To inject case-conditioned linguistic priors, we design a Conditional Dual-Hopfield Memory module that retrieves case-conditioned report priors and integrates them through both implicit feature-level fusion into entity representations and explicit memory tokens appended to the prompt. Finally, we enforce clinical consistency via hierarchical severity supervision at the entity, region, and sign-group levels. Comprehensive Experiments demonstrate that our method outperforms previous methods and achieves SoTA performance.