We propose a joint segmentation and groupwise registration method for cardiac perfusion images by using temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal, while segmentation minimizes a pixel’s dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared to two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutually complementary registration and segmentation information in one framework, while other methods solve the two problems separately. 1. Introduction Dynamic contrast-enhanced (DCE) magnetic resonance (MR) images (or perfusion MRI) have developed as a popular noninvasive tool for the functional analysis of internal organs. Contrast agent is injected intravenously into the patient, and a series of MR images are acquired over a period of time. As the contrast agent flows through the blood stream, the intensity of corresponding regions increases. Since the image acquisition process can take up to 20 minutes, patient movement is inevitable. Additionally, elastic deformation of cardiac tissues due to patient breathing needs to be corrected. Perfusion images are characterized by rapid intensity change over time, low spatial resolution, and noise and make registration challenging. Previous techniques mostly employed a pairwise registration approach, that is, all images of a sequence are individually registered to a fixed reference image [1–3]. The success of such approaches depends upon the robustness of the cost function to intensity change. Although intensity change due to contrast agent flow poses challenges, it also provides important temporal information for registration and segmentation of the perfusion image sequence. In this work, we propose a method which makes use of the temporal dynamics of contrast agent flow to achieve joint segmentation and groupwise registration of an MR cardiac image sequence. The changing intensity due to contrast agent flow provides valuable segmentation by highlighting the organ of interest. It is interesting to note that different regions of a cardiac image sequence have different intensity-time characteristics (Figure 4). Temporal flow information was used for
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