Publications

2020
O. Afacan, W. S. Hoge, T. E. Wallace, A. Gholipour, S. Kurugol, and S. K. Warfield, “Simultaneous Motion and Distortion Correction Using Dual-Echo Diffusion-Weighted MRI,” Journal of Neuroimaging, 2020.Abstract

 

BACKGROUND AND PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

S. Kurugol, et al., “Correction to: Prospective pediatric study comparing glomerular filtration rate estimates based on motion-robust dynamic contrast-enhanced magnetic resonance imaging and serum creatinine (eGFR) to 99m Tc DTPA,” Pediatric radiology, pp. 1–2, 2020.
S. Kurugol, et al., “Prospective pediatric study comparing glomerular filtration rate estimates based on motion-robust dynamic contrast-enhanced magnetic resonance imaging and serum creatinine (eGFR) to 99mTc DTPA,” Pediatric Radiology, 2020.
2019
J. Coll-Font, et al., “Bulk motion-compensated DCE-MRI for functional imaging of kidneys in newborns,” Journal of Magnetic Resonance Imaging, 2019.
Y. Lamash, et al., “Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI,” Journal of Magnetic Resonance Imaging, vol. 49, no. 6, pp. 1565–1576, 2019.
S. Kurugol, et al., “Feed and wrap magnetic resonance urography provides anatomic and functional imaging in infants without anesthesia,” Journal of Pediatric Urology, 2019.
S. Kurugol, O. Afacan, A. Stemmer, R. S. Lee, J. S. Chow, and S. K. Warfield, “Glomerular filtration rate estimation by motion-robust high spatiotemporal resolution DCE-MRI with radial VIBE and comparison with plasma clearance of 99mTc-DTPA,” Proceedings of Int. Soc. of Magnetic Resonance Imaging, 2019.
J. Sourati, A. Gholipour, J. G. Dy, X. Tomas-Fernandez, S. Kurugol, and S. K. Warfield, “Intelligent labeling based on fisher information for medical image segmentation using deep learning,” IEEE transactions on medical imaging, vol. 38, no. 11, pp. 2642–2653, 2019.
J. Coll-Font, O. Afacan, J. Chow, and S. Kurugol, “Linear Time Invariant Model Based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019, pp. 430–437.
J. Coll-Font, et al., “Self-navigated bulk motion detection for feed and wrap renal dynamic radial VIBE DCE-MRI,” in Proceedings of Int. Soc. of Magnetic Resonance Imaging, 2019, 2019.
A. Mortazi, N. Khosravan, D. A. Torigian, S. Kurugol, and U. Bagci, “Weakly Supervised Segmentation by a Deep Geodesic Prior,” in International Workshop on Machine Learning in Medical Imaging, 2019, pp. 238–246.
2018
S. Kurugol, B. Marami, O. Afacan, S. K. Warfield, and A. Gholipour, “3D Motion Estimation and Correction of Motion in Sequential Slices of Kidney Diffusion-Weighted MRI,” in Proceedings of Int. Soc. of Magnetic Resonance Imaging, 2018. 2018.
J. Sourati, A. Gholipour, J. G. Dy, S. Kurugol, and S. K. Warfield, “Active deep learning with Fisher information for patch-wise semantic segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, Cham, 2018, pp. 83–91.
M. Haghighi, S. K. Warfield, and S. Kurugol, “Automatic renal segmentation in DCE-MRI using convolutional neural networks,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 1534–1537.
S. Kurugol, O. Afacan, C. Seager, R. S. Lee, J. S. Chow, and S. K. Warfield, “Compensating for Bulk Motion in Feed and Wrap Renal Dynamic Radial VIBE DCE-MRI using Bulk Motion Removal and Non-Rigid Registration,” in Proceedings of Int. Soc. of Magnetic Resonance Imaging, 2018, 2018.
Y. Lamash, S. Kurugol, and S. K. Warfield, “Semi-automated extraction of Crohns disease MR imaging markers using a 3D residual CNN with distance prior,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, Cham, 2018, pp. 218–226.
Y. Lamash, S. Kurugol, M. Freiman, and S. Warfield, “Semi-automatic method for generating multiplanar reformatting views of MR post-contrast T1-weighted images for visualizing and assessing pediatric Crohn’s disease,” in Proceedings of Int. Soc. of Magnetic Resonance Imaging, 2018. 2018.
S. Kurugol, et al., “Feed and Wrap MRU,” 61st Society for Pediatric Radiology Annual Meeting. Received Caffey award for best scientific paper, 2018. feedandwrapmru_sprabstract.pdf
M. Haghighi, S. K. Warfield, and S. Kurugol, “Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks,” in IEEE International Symposium on Biomedical Imaging (ISBI) , 2018.Abstract
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.

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