A multisource adaptive magnetic resonance image fusion technique for versatile contrast magnetic resonance imaging
Lei Zhang1, Fang-Fang Yin2, Brittany Moore1, Silu Han1, Jing Cai3
1 Medical Physics Graduate Program, Duke University; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
2 Medical Physics Graduate Program, Duke University; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA; Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
3 Medical Physics Graduate Program, Duke University; Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
Dr. Jing Cai
Department of Radiation Oncology, Duke University Medical Center, Durham, NC 27710
Source of Support: None, Conflict of Interest: None
Aim: Magnetic resonance imaging (MRI) has been widely used in radiation therapy (RT) treatment planning. The current practice to capture clinical indications like tumor from MRI is to review multiple types of MRI separately, which can be inefficient and the tumor contrast is limited by existing images. This study presented a novel approach to effectively integrate clinical meaningful information of multiple MRI to produce a set of fused MRI with versatile image contrasts. A multisource adaptive fusion technique was developed in this approach using limited number of standard MR images as input.
Methods: The multisource adaptive MRI fusion technique is designed with five key components: input multiple MRI, image preprocessing, fusion algorithm, adaptation methods, and output-fused MRI. A linear-weighting fusion algorithm is used to demonstrate the proof of concept. Fusion options (weighting parameters and image features) are precalculated and saved in a database for fast fusion operation. Input- and output-driven approaches are developed for MRI contrast adaptation. The technique is tested in human digital phantom 4D extended cardiac-torso (XCAT) for versatile contrast MRI generation.
Results: A graphic user interface was developed in Matlab environment. Input- and output-driven adaptation methods were implemented for interactive user operation to achieve different clinical goals. Using four input MR images (T1W, T2W, T2/T1W, and diffusion weighted), the fusion technique generated hundreds of fused MR images with versatile image contrasts.
Conclusion: A novel multisource adaptive image fusion technique capable of generating versatile contrast MRI from a limited number of standard MR images was demonstrated. This method has the potential to enhance the effectiveness and efficiency of MR applications in RT.