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Blume, M., Martinez-Moller, A., Keil, A., Navab, N., & Rafecas, M. (2010). Joint Reconstruction of Image and Motion in Gated Positron Emission Tomography. IEEE Trans. Med. Imaging, 29(11), 1892–1906.
Abstract: We present a novel intrinsic method for joint reconstruction of both image and motion in positron emission tomography (PET). Intrinsic motion compensation methods exclusively work on the measured data, without any external motion measurements. Most of these methods separate image from motion estimation: They use deformable image registration/optical flow techniques in order to estimate the motion from individually reconstructed gates. Then, the image is estimated based on this motion information. With these methods, a main problem lies in the motion estimation step, which is based on the noisy gated frames. The more noise is present, the more inaccurate the image registration becomes. As we show both visually and quantitatively, joint reconstruction using a simple deformation field motion model can compete with state-of-the-art image registration methods which use robust multilevel B-spline motion models.
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Blume, M., Navab, N., & Rafecas, M. (2012). Joint image and motion reconstruction for PET using a B-spline motion model. Phys. Med. Biol., 57(24), 22pp.
Abstract: We present a novel joint image and motion reconstruction method for PET. The method is based on gated data and reconstructs an image together with amotion function. The motion function can be used to transform the reconstructed image to any of the input gates. All available events (from all gates) are used in the reconstruction. The presented method uses a B-spline motion model, together with a novel motion regularization procedure that does not need a regularization parameter (which is usually extremely difficult to adjust). Several image and motion grid levels are used in order to reduce the reconstruction time. In a simulation study, the presented method is compared to a recently proposed joint reconstruction method. While the presented method provides comparable reconstruction quality, it is much easier to use since no regularization parameter has to be chosen. Furthermore, since the B-spline discretization of the motion function depends on fewer parameters than a displacement field, the presented method is considerably faster and consumes less memory than its counterpart. The method is also applied to clinical data, for which a novel purely data-driven gating approach is presented.
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