Brain　computed　tomography　(CT)　report　generation,　which　aims　at　generating　accurate　and　descriptive　reports　for　Brain　CT　imaging,　has　gained　growing　attention　from　researchers.　Existing　works　mainly　train　a　language-generation　model　with　complex　image-text　pairs　for　supervision,　which　still　struggled　with　the　following　challenges:　1)　the　serious　long-tail　distribution　of　textual　supervise　signals　led　by　imbalanced　text　length　distribution,　and　2)　the　insufficient　medical　data　caused　by　expensive　expert　intervention.　In　this　paper,　we　propose　a　novel　Gaussian　heuristic　curriculum　learning　(GHCL)　model　to　effectively　tackle　the　long-tail　data　distribution　and　optimally　utilize　the　limited　training　data.　Specifically,　our　training　process　mimics　the　learning　process　of　physicians　in　a　step-wise　paradigm.　Firstly,　we　evaluate　the　scores　of　training　difficulty　for　each　sample　through　two　elaborately　designed　Gaussian　heuristic　metrics.　Then,　during　the　training　of　the　language-generation　model,　we　iteratively　select　the　most　suitable　batch　of　training　samples,　which　is　comprehensively　considered　by　the　calculated　scores　of　training　difficulty.　In　this　way,　GHCL　can　effectively　guide　the　progressive　learning　of　the　report　generation　model　and　boost　the　quality　of　generated　Brain　CT　reports.　We　comprehensively　compare　the　method　with　previous　state-of-the-art　models　on　the　Brain　CT　report　generation　dataset　BCT-CHR.　Experimental　results　demonstrate　that　our　method　surpasses　previous　state-of-the-art　approaches　and　GHCL　is　flexible　to　combine　with　existing　approaches　to　further　improve　the　performance.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2024.