Spine Computed Tomography (SCT) is essential for identifying fractures, tumors and degenerative spine diseases, assisting medical practitioners in formulating an accurate diagnosis and treatment. One of the core element of SCT is reporting. The effectiveness of spine reporting is often limited by challenges such as an inadequate infrastructure and lack of experts. Automated SCT analysis has the potential to revolutionize spinal healthcare and improve patient outcomes. To achieve this objective, we proposed a framework for spine report generation that utilizes transformer architecture, trained on textual reports alongside the visual features extracted from the sagittal slices of the SCT volume. A foundation model is used to perform Knowledge Distillation (KD) alongside an encoder to ensure an optimal performance. The proposed framework is evaluated on the public dataset (VerSe20). The incorporation of KD results improved both the BERT and BLEU1 score on the dataset, from 0.7486 to 0.7522 and 0.6361 to 0.7291. Additionally, the proposed framework is evaluated using four different types of reports: original radiologist reports, reports without spine-level annotations, rephrased reports, and reports generated by ChatGPT-4o (ChatGPT). The evaluation without spine-level annotations demonstrates superior performance across most metrics, achieving the highest BLEU-1 and ROUGE-L scores, with a BLEU-1 of 0.9293 and a ROUGE-L score of 0.9297. In contrast, the other techniques achieved moderate scores across all metrics. Finally, experienced radiologists assessed the spine report and have given high rating to the original reports across all three criteria (completeness, conciseness and correctness), in comparison to the generated reports. This study’s findings suggest that omitting spine-level annotations can improve the quality of text generation.