TH‐CD‐206‐09: Learning‐Based MRI‐CT Prostate Registration Using Spare Patch‐Deformation Dictionary

Purpose: To enable MRI‐guided prostate radiotherapy, MRI‐CT deformable registration is required to map the MRI‐defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey‐level intensity characteristics between MRI and CT images, the integration of MRI into CT‐based radiotherapy is very challenging. We are developing a learning‐based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch‐wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch‐wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI‐CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate‐cancer patients’ data and its performance was compared with the commonly used free‐form‐deformation‐based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity‐based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR‐CT registration approach based on patch‐deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient‐specific treatment variation measured during the course of radiotherapy, is therefore well‐suited for a number of MRI‐guided adaptive radiotherapy, and potentially enhance prostate radiotherapy treatment outcome.