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Current Research Projects at the MRRF

Research Faculty

Vincent Magnotta, PhD Vincent A. Magnotta, PhD        
Daniel Thedens, PhD Daniel R. Thedens, PhD        
Mathews Jacob, PhD Mathews Jacob, PhD        
Merry Mani, PhD

Merry P. Mani, PhD

   
Sajan Goud Lingala, PhD Sajan Goud Lingala, PhD    

The MR Research Facility currently supports more than 40 research projects from ten different departments within the University of Iowa. The primary utilization of the equipment has been for neuroimaging studies. This includes several large studies from the Departments of Psychiatry, Neurology, Anesthesia, and Radiology. The majority of these studies have acquired anatomical brain scans for brain morphology studies. A smaller number of studies have utilized fMRI and diffusion tensor imaging (DTI). Other research projects have utilized MR imaging to study cardiac function, assess cartilage, liver fat content, and have evaluated subcutaneous facial implants. Four of our current research projects are outlined below.


T1ρ Imaging 

Nana Owusu, Ph.D Candidate 

My work entails a relaxation parameter (contrast) known as T1rho or longitudinal relaxation in the rotating frame. T1rho is a form of contrast in magnetic resonance imaging and this contrast is due to the exchange of protons in biological fluids. There is no consensus as to which species of protons in biological fluids contributes greatly to T1rho signal (e.g. due to pH, glucose, other macromolecules). Understanding more about the signal contribution may help clinicians in assessing treatment or explain more about ailments linked to abnormal metabolism like Huntington's disease, bipolar disorder I, cancer and epilepsy. I am also working on GE pulse sequence development for T1rho imaging. 


Neuroimaging Development and Clinical Applications

Chu-yu Lee, PhD

I am a neuroimaging scientist. I am developing advanced magnetic resonance imaging (MRI) techniques to assess microstructure, metabolism, and iron deposition in the brain. I am also investigating applications of these MRI techniques in cancer and other neurological disorders.  With an engineering background, I find that the most rewarding part of my job is to work with an interdisciplinary team of physicists, chemists, and clinicians to tackle challenges in diagnosis and disease management and ultimately to improve patient care.


Structured signal reconstruction from incomplete data                                                                               

Sampurna Biswas, PhD candidate                                                                                                                                                                                                                            

The main thrust of my research is on the development of algorithms for the reconstruction of structured signals from incomplete data. These algorithms are expected to have applications in medical imaging and sonar array processing. The underlying theme has been designing sampling schemes to observe the signal, devising transformation of the signal based on its inherent structure and then deriving theoretical recovery guarantees for the chosen sampling scheme and recovery algorithm. Signals collected from a patient undergoing a MRI scan, are correlated and share same sparsity patterns, across time. The image reconstruction can be posed as a low rank and sparse signal recovery. There exists sampling scheme in the literature that solves this recovery in two steps of subspace estimation. The guarantee for this scheme to provide an exact solution was not available. I proposed a generalized two-step low rank and joint sparse signal recovery with theoretical guarantees (in terms of number of measurements required) for each step. This is impactful in designing the acquisition scheme for a two-step recovery in dynamic MRI applications like CINE, myocardial perfusion and brain parametric mapping MRI.


Magnetic Resonance Imaging (MRI) reconstruction design for fast Imaging acquisition

Yassir AlBaqqal, PhD student

The ultimate goal of my research is to develop novel algorithms to reconstruct heavily undersampled sparse imaging. The designed schemes aim to achieve a higher patio-temporal resolution, signal-to-noise ratio and coverage in multidimensional multichannel MRI. In addition to improving patients comfort and compliance while imaging under the MRI device, the new developed schemes will allow patients with arrhythmia problems, pediatrics and obese people to breath freely without the need for any breath-hold scans. Shortening examination periods also reduces patient's stress, lowers the entire visit to the clinic and finally lowers the associated economic costs. Rapid imaging acquisitions will also allow for efficient extraction of quantitative information needed for the patients diagnosis eg. tumor characterization and veins blockages through myocardial perfusion MRI. Current application of interests include real time CINE MRI and contrast changing perfusion MRI.


Accelerating dynamic free-breathing cardiac MRI

Sunrita Poddar, PhD candidate

I work on the acquisition and reconstruction of  ungated dynamic cardiac data in the free-breathing mode. We have acquired these cardiac images in a time comparable to a breath-held scan, and found that the reconstruction quality is comparable to that obtained from a breath-held acquisition. In addition, our proposed technique is feasible in critically ill patients and also paediatric patients who are unable to hold their breath or follow breath-holding instructions. A short summary of this work can be found here and a more detailed description can be found in our paper. This work has been conducted in the context of CINE imaging. Our future work involves extending our developed algorithms to enable ungated free-breathing perfusion imaging and parameter mapping. Our vision is to develop a comprehensive cardiac exam which can conduct ungated free-breathing CINE, perfusion imaging and parameter mapping in a single short scan. 


MR Imaging using Deep Learning

Hemant Kumar Aggarwal, PhD

Hemant is developing algorithms for fast MR imaging using deep learning techniques. During his Ph.D., he has worked on compressed sensing, dictionary learning, low-rank matrix recovery, and joint sparse recovery related techniques. His first project was on compressive multispectral imaging that is an extension of the color image demosaicing problem often encountered in the design of low-cost single-sensor cameras. He had developed a uniform multispectral filter array to capture multiple bands using single-sensor and a demosaicing algorithm to reconstruct the full multispectral image from the undersampled raw image. He also worked on spatio-spectral total-variation based method for hyperspectral image denoising problem in the mixed noise scenario where mixed noise may have Gaussian noise as well as sparse noise that includes line strips, shot noise, and impulse noise. He further considered the blind source separation problem also known as the unmixing problem that focuses on identifying constituent classes and their fractions present at each pixel in a hyperspectral image. 


Multivoxel MR Spectroscopy 

Jia Xu, PhD 

I am currently studying human brain pH environment and metabolites with 31P 3D MRSI techniques. I am interested in developing CEST and 3D MRSI techniques that are sensitive to brain metabolism; I am also very interested in developing automated pipeline for spectroscopic data analysis.