Optimising Multimodal Image Registration Techniques: A Comprehensive Study of Non-Rigid and Affine Methods for PET/CT Integration

<b>Background/Objective</b>: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques-Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration-in terms of improving PET/CT image alignment. <b>Methods</b>: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4-8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). <b>Results</b>: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. <b>Conclusions</b>: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments.