Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
This addresses a fundamental issue in medical imaging for applications such as MRI and CT, where subject motion complicates 3D reconstruction, though it appears incremental as it builds on existing CNN methods.
The paper tackles the problem of limited capture range and initialization requirements in 2D/3D image registration by proposing a CNN-based regression method to predict slice-to-volume transformations, achieving an average error of 7mm on simulated MRI data and enabling reconstruction in challenging scenarios like fetal MRI.
This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotation and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography and X-ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.