Publications by Type: Conference Proceedings

Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing. The first contribution of this study is to model tag fading by considering the interplay between T₁ relaxation and the repeated application of radio frequency (RF) pulses during serial imaging sequences. This is a factor that has been overlooked in prior research on tMRI post-processing. Further, we have observed an emerging trend of utilizing raw tagged MRI within a deep learning-based (DL) registration framework for motion estimation. In this work, we evaluate and analyze the impact of commonly used image similarity objectives in training DL registrations on raw tMRI. This is then compared with the Harmonic Phase-based approach, a traditional approach which is claimed to be robust to tag fading. Our findings, derived from both simulated images and an actual phantom scan, reveal the limitations of various similarity losses in raw tMRI and emphasize caution in registration tasks where image intensity changes over time.

Magnetic Resonance Elastography (MRE) is a noninvasive method for quantitatively assessing the viscoelastic properties of tissues, such as the brain. MRE has been successfully used to measure the material properties and diagnose diseases based on the difference in mechanical properties between diseased and normal tissue. However, MRE is still an emerging technology that is not part of routine clinical imaging like structural Magnetic Resonance Imaging (MRI), and the acquisition equipment is not widely available. Thus, it is challenging to collect MRE, but there is an increasing interest in it. In this study, we explore using structural MRI images to synthesize the MRE-derived material properties of the human brain. We use deep networks that employ both MRI and Diffusion Tensor Imaging (DTI) to explore the best input images for MRE image synthesis. This work is the first study to report on the feasibility of MRE synthesis from structural MRI and DTI.

In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse engineered set of Signs/Symptoms from the WISER application was used as the training set and 31,100 simulated patient records were used as the testing set. Three sets of simulated patient records were generated by 5%, 10% and 15% perturbation of the Signs/ Symptoms of each chemical record. While all three methods achieved a 100% training accuracy, WISER, BDT and ANN produced performances in the range of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of dimensional reduction using ANN illustrated a dimensional collapse from 79 variables to 40 with little loss of classification performance.