Geometric distortions resulting from large pose changes reduce the accuracy of motion measurements and interfere with the ability to generate artifact‐free information. Our goal is to develop an algorithm and pulse sequence to enable motion‐compensated, geometric distortion compensated diffusion‐weighted MRI, and to evaluate its efficacy in correcting for the field inhomogeneity and position changes, induced by large and frequent head motions.
Dual echo planar imaging (EPI) with a blip‐reversed phase encoding distortion correction technique was evaluated in five volunteers in two separate experiments and compared with static field map distortion correction. In the first experiment, dual‐echo EPI images were acquired in two head positions designed to induce a large field inhomogeneity change. A field map and a distortion‐free structural image were acquired at each position to assess the ability of dual‐echo EPI to generate reliable field maps and enable geometric distortion correction in both positions. In the second experiment, volunteers were asked to move to multiple random positions during a diffusion scan. Images were reconstructed using the dual‐echo correction and a slice‐to‐volume registration (SVR) registration algorithm. The accuracy of SVR motion estimates was compared to externally measured ground truth motion parameters.
Our results show that dual‐echo EPI can produce slice‐level field maps with comparable quality to field maps generated by the reference gold standard method. We also show that slice‐level distortion correction improves the accuracy of SVR algorithms as slices acquired at different orientations have different levels of distortion, which can create errors in the registration process.
Dual‐echo acquisitions with blip‐reversed phase encoding can be used to generate slice‐level distortion‐free images, which is critical for motion‐robust slice to volume registration. The distortion corrected images not only result in better motion estimates, but they also enable a more accurate final diffusion image reconstruction.
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively.