<p>Image-guided radiotherapy (IGRT) has enhanced the precision of cancer treatment by integrating imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) into daily radiotherapy workflows. In head and neck cancer, where anatomical changes are common, accurate image registration between planning and treatment scans is essential to ensure dose accuracy. However, geometric distortions in CBCT (such as translation, rotation, and scaling resulting from patient positioning variations observed in daily CBCT images) can affect tumour targeting and dose delivery. This pilot study assesses a MATLAB-based image correction algorithm that uses rigid bony landmarks and point cloud registration together with spatial transformation to align CBCT with planning CT. Two head and neck cancer patients were retrospectively analysed, selected for their contrasting anatomical responses: one with substantial tumour regression and one with minimal change. Imaging was performed on the Halcyon V3.1 linear accelerator (Varian Medical Systems), with 25 daily CBCT scans per patient (85–96 slices per scan), resulting in 50 datasets for analysis. Spatial deviations were measured along the X, Y, and Z axes, and dose recalculations were performed for each treatment fraction. The correction method significantly improved spatial congruence and reduced geometric discrepancies caused by voxel spacing and acquisition parameters. Uncorrected scans showed dose deviations of up to ± 12% in organs at risk, notably the spinal cord and parotid glands. These findings demonstrate the feasibility and dosimetric relevance of automated CBCT correction in daily head and neck radiotherapy. Although limited in sample size, the study provides a detailed technical and dosimetric analysis of spatial distortions and supports future validation in larger patient cohorts.</p>