Publications

2022
Serge Vasylechko, Simon Keith Warfield, Sila Kurugol, and Onur Afacan. 2022. “SynthMap: a generative model for synthesis of 3D datasets for quantitative MRI parameter mapping of myelin water fraction.” In Medical Imaging with Deep Learning. Publisher's Version
Aziz Koçanaoğulları, Cemre Ariyurek, Onur Afacan, and Sila Kurugol. 2022. “Learning the Regularization in DCE-MR Image Reconstruction for Functional Imaging of Kidneys.” IEEE Access 10, Pp. 4102-4111. Publisher's VersionAbstract
Kidney DCE-MRI aims at both qualitative assessment of kidney anatomy and quantitative assessment of kidney function by estimating the tracer kinetic (TK) model parameters. Accurate estimation of TK model parameters requires an accurate measurement of the arterial input function (AIF) with high temporal resolution. Accelerated imaging is used to achieve high temporal resolution, which yields under-sampling artifacts in the reconstructed images. Compressed sensing (CS) methods offer a variety of reconstruction options. Most commonly, sparsity of temporal differences is encouraged for regularization to reduce artifacts. Increasing regularization in CS methods removes the ambient artifacts but also over-smooths the signal temporally which reduces the parameter estimation accuracy. In this work, we propose a single image trained deep neural network to reduce MRI under-sampling artifacts without reducing the accuracy of functional imaging markers. Instead of regularizing with a penalty term in optimization, we promote regularization by generating images from a lower dimensional representation. In this manuscript we motivate and explain the lower dimensional input design. We compare our approach to CS reconstructions with multiple regularization weights. Proposed approach results in kidney biomarkers that are highly correlated with the ground truth markers estimated using the CS reconstruction which was optimized for functional analysis. At the same time, the proposed approach reduces the artifacts in the reconstructed images.
Damien J Grattan-Smith, Jeanne Chow, Sila Kurugol, and Richard Alan Jones. 2022. “Quantitative renal magnetic resonance imaging: magnetic resonance urography.” Pediatric radiology, 52, Pp. 228-248.
2021
Barbara Villarini, Hykoush Asaturyan, Sila Kurugol, Onur Afacan, Jimmy D Bell, and Louise E Thomas. 2021. “3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities.” In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Pp. 166–171. IEEE.
Tess E Wallace, Onur Afacan, Camilo Jaimes, Joanne Rispoli, Kristina Pelkola, Monet Dugan, Tobias Kober, and Simon K Warfield. 2021. “Free induction decay navigator motion metrics for prediction of diagnostic image quality in pediatric MRI.” Magnetic resonance in medicine, 85, 6, Pp. 3169–3181.
Hykoush Asaturyan, Barbara Villarini, Karen Sarao, Jeanne S. Chow, Onur Afacan, and Sila Kurugol. 2021. “Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function.” Sensors, 21, 23. Publisher's VersionAbstract
There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time–intensity curves of the cortex and medulla for determining obstruction in the urinary tract. This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial–temporal information coupled with boundary-preserving fully convolutional dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively.
Onur Afacan, Edward Yang, Alexander P Lin, Eduardo Coello, Melissa L DiBacco, Phillip L Pearl, Simon K Warfield, and SSADH Deficiency Investigators Consortium. 2021. “Magnetic Resonance Imaging (MRI) and Spectroscopy in Succinic Semialdehyde Dehydrogenase Deficiency.” Journal of Child Neurology, Pp. 0883073821991295.
Florian Schmaranzer, Onur Afacan, Till D Lerch, Young-Jo Kim, Klaus A Siebenrock, Michael Ith, Jennifer L Cullmann, Tobias Kober, Markus Klarhoefer, Moritz Tannast, and others. 2021. “Magnetization-prepared 2 Rapid Gradient-Echo MRI for B1 Insensitive 3D T1 Mapping of Hip Cartilage: An Experimental and Clinical Validation.” Radiology, 299, 1, Pp. 150–158.
Seyhmus Guler, Alexander L Cohen, Onur Afacan, and Simon K Warfield. 2021. “Matched neurofeedback during fMRI differentially activates reward-related circuits in active and sham groups.” Journal of Neuroimaging.
Jaume Coll-Font, Onur Afacan, Jeanne S Chow, Richard S Lee, Simon K Warfield, and Sila Kurugol. 2021. “Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function.” Medical Image Analysis, 67, Pp. 101880.
Jaume Coll-Font, Onur Afacan, Scott Hoge, Harsha Garg, Kumar Shashi, Bahram Marami, Ali Gholipour, Jeanne Chow, Simon Warfield, and Sila Kurugol. 2021. “Retrospective Distortion and Motion Correction for Free-Breathing DW-MRI of the Kidneys Using Dual-Echo EPI and Slice-to-Volume Registration.” Journal of Magnetic Resonance Imaging, 53, 5, Pp. 1432–1443.
Serge Didenko Vasylechko, Simon K Warfield, Onur Afacan, and Sila Kurugol. 2021. “Self-supervised IVIM DWI parameter estimation with a physics based forward model.” Magnetic Resonance in Medicine.
2020
Jaume Coll-Font, Onur Afacan, Jeanne S Chow, Richard S Lee, Alto Stemmer, Simon K Warfield, and Sila Kurugol. 2020. “Bulk motion-compensated DCE-MRI for functional imaging of kidneys in newborns.” Journal of Magnetic Resonance Imaging, 52, 1, Pp. 207–216.
Sila Kurugol, Catherine M Seager, Hatim Thaker, Jaume Coll-Font, Onur Afacan, Reid C Nichols, Simon K Warfield, Richard S Lee, and Jeanne S Chow. 2020. “Feed and wrap magnetic resonance urography provides anatomic and functional imaging in infants without anesthesia.” Journal of pediatric urology, 16, 1, Pp. 116–120.
Sila Kurugol, Onur Afacan, Richard S Lee, Catherine M Seager, Michael A Ferguson, Deborah R Stein, Reid C Nichols, Monet Dugan, Alto Stemmer, Simon K Warfield, and others. 2020. “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, 50, 5, Pp. 698–705.
Jaume Coll-Font, Onur Afacan, Scott Hoge, Harsha Garg, Kumar Shashi, Bahram Marami, Ali Gholipour, Jeanne Chow, Simon Warfield, and Sila Kurugol. 2020. “Retrospective Distortion and Motion Correction for Free-Breathing DW-MRI of the Kidneys Using Dual-Echo EPI and Slice-to-Volume Registration.” Journal of Magnetic Resonance Imaging.
Onur Afacan, Scott W Hoge, Tess E Wallace, Ali Gholipour, Sila Kurugol, and Simon K Warfield. 2020. “Simultaneous Motion and Distortion Correction Using Dual-Echo Diffusion-Weighted MRI.” Journal of Neuroimaging, 30, 3, Pp. 276–285.