Current Research Projects at the MRRF

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.

High Resolution Diffusion Imaging

Merry Mani, Ph.D

My research focuses on developing new and improved anatomical and structural brain imaging methods. During my PhD, I had focused on developing a simultaneously high spatial and angular resolution diffusion imaging technique. The sequence introduced the novel concept of joint k-q under-sampling by densely sampling the q-space points using a set of under-sampled random spiral interleaves that also densely sampled the k-space points to enable recovery of high resolution data. Using a compressed sensing-based technique, we were able to reconstruct the diffusion data at high spatial and angular resolution with about 8 times speed-up in imaging time compared to the conventional sequences. My current work is focused on developing ultra-high resolution diffusion sequences on the 7T GE950MR scanner here. Recently, we developed a calibration-free motion compensated reconstruction algorithm known as MUSSELS that can enable the acquisition of short echo-time diffusion weighted images from multi-shot diffusion acquisitions for high spatial resolution diffusion imaging applications.


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. 


Structural Imaging of the Brain: Analysis and Acquisition

Andrew Metzger, Ph.D Candidate 

Automated image analysis from magnetic resonance images of the brain have provided insight into the progression and manifestation of many neurological diseases and will likely be developed for diagnostic purposes as methods improve. Atlas based segmentation is widely used as a way to utilize prior knowledge to increase the accuracy of tissue classification of the adult brain. Due to the considerable anatomical development during the first few years of life, atlases based on adult anatomical priors do not accurately segment early age pediatric subjects. My masters thesis describes a method requiring minimal manual corrections to create atlases based on pediatric anatomical priors that will reliably classify tissue of pediatric MR images of the brain in an automated pipeline. I am also working to develop high field pulse sequences for parametric mapping and high resolution anatomical imaging.


Imaging of the Lung

Bill Kearney, Ph.D

We are developing pulse sequences and methodologies to image lung structure and function. The structure and function of the lung has been investigated using magnetic resonance with hyperpolarized He and Xe. Due to the high cost and complexity of using these agents, we are seeking more versatile and economical alternatives. Fluorine imaging is a promising approach that may replace exotic nuclei in both structural and functional pulmonary studies. Fluorine has a high intrinsic MR sensitivity, and a number of biologically benign reagents are available to investigate properties of both the gas and tissue volumes. Fluorine imaging may also provide information about the gas/tissue interface that is not accessible by any other method.


Correlated Spectroscopy and Quantitative Susceptibility Mapping

Cameron Cushing, Ph.D Candidate 

Despite advances in treatment capabilities, biological understanding, and imaging technologies, a diagnosis of glioblastoma remains a death sentence for patients. Pharmacological ascorbate therapy has promise as a safe adjuvant to standard of care treatment for primary glioblastoma as demonstrated by multiple clinical trials. Pharmacological ascorbate is hypothesized to act as a pro-drug for the production of hydrogen peroxide and superoxide via an iron-catalyzed auto-oxidation reaction. Auto-oxidation of ascorbate reduces Iron(III) to Iron(II). Methods that quantitate ascorbate in vivo or monitor the reduction of the labile iron pool by ascorbate may provide important information for managing ascorbate therapy. Direct quantitation of endogenous ascorbate has previously been demonstrated in vivo via magnetic resonance spectroscopy. Correlated spectroscopy (COSY), a two-dimensional spectroscopic method, has shown promise in separating and quantitating multiple chemical species in vivo. Preliminary data suggest that ascorbate may be quantitated through this method as well. Iron can be measured by the MR imaging techniques T2* relaxometry and quantitative susceptibility mapping (QSM). Iron measurement via MR imaging has historically neglected to examine the influence of redox active metal pools on these imaging methods. My preliminary results suggest that reduction of Iron(III) may alter these images in vivo.
 


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, Ph.D.

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.