Publications by Author: Ahmed A. Alshareef

Introduction

Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are “average” models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models.

Materials and Methods

Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components Txx⁠, Tyy⁠, and Tzz that capture the breadth, length, and height of the head, respectively, and shearing components (⁠Txy,Txz,Tyx,Tyz,Tzx⁠, and Tzy⁠) that capture the relative skewness of the head shape.

Results

An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components Txy,Txz,Tyx,Tyz,Tzx⁠, and Tzy on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the Tzz scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; Tyy contributes approximately 20%, whereas Txx contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible.

Conclusions

Head shape has a considerable influence on the injury predictions of computational head injury models. Available “average” head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.

Elevation in left ventricular (LV) myocardial stiffness is a key remodeling-mediated change that underlies the development and progression of heart failure (HF). Despite the potential diagnostic value of quantifying this deterministic change, there is a lack of enabling techniques that can be readily incorporated into current clinical practice. To address this unmet clinical need, we propose a simple protocol for processing routine echocardiographic imaging data to provide an index of left ventricular myocardial stiffness, with protocol specification for patients at risk for heart failure with preserved ejection fraction. We demonstrate our protocol in both a preclinical and clinical setting, with representative findings that suggest sensitivity and translational feasibility of obtained estimates.

Drug-coated balloon (DCB) therapy is a promising endovascular treatment for obstructive arterial disease. The goal of DCB therapy is restoration of lumen patency in a stenotic vessel, whereby balloon deployment both mechanically compresses the offending lesion and locally delivers an antiproliferative drug, most commonly paclitaxel (PTX) or derivative compounds, to the arterial wall. Favorable long-term outcomes of DCB therapy thus require predictable and adequate PTX delivery, a process facilitated by coating excipients that promotes rapid drug transfer during the inflation period. While a variety of excipients have been considered in DCB design, there is a lack of understanding about the coating-specific biophysical determinants of essential device function, namely, acute drug transfer. We consider two hydrophilic excipients for PTX delivery, urea (UR) and poly(ethylene glycol) (PEG), and examine how compositional and preparational variables in the balloon surface spray-coating process impact resultant coating microstructure and in turn acute PTX transfer to the arterial wall. Specifically, we use scanning electron image analyses to quantify how coating microstructure is altered by excipient solid content and balloon-to-nozzle spray distance during the coating procedure and correlate obtained microstructural descriptors of coating aggregation to the efficiency of acute PTX transfer in a one-dimensional ex vivo model of DCB deployment. Experimental results suggest that despite the qualitatively different coating surface microstructures and apparent PTX transfer mechanisms exhibited with these excipients, the drug delivery efficiency is generally enhanced by coating aggregation on the balloon surface. We illustrate this microstructure–function relation with a finite element-based computational model of DCB deployment, which along with our experimental findings suggests a general design principle to increase drug delivery efficiency across a broad range of DCB designs.

Strain energy and kinetic energy in the human brain were estimated by magnetic resonance elastography (MRE) during harmonic excitation of the head, and compared to characterize the effect of loading direction and frequency on brain deformation. In brain MRE, shear waves are induced by external vibration of the skull and imaged by a modified MR imaging sequence; the resulting harmonic displacement fields are typically “inverted” to estimate mechanical properties, like stiffness or damping. However, measurements of tissue motion from MRE also illuminate key features of the response of the brain to skull loading. In this study, harmonic excitation was applied in two different directions and at five different frequencies from 20 to 90 Hz. Lateral loading induced primarily left-right head motion and rotation in the axial plane; occipital loading induced anterior-posterior head motion and rotation in the sagittal plane. The ratio of strain energy to kinetic energy (SE/KE) depended strongly on both direction and frequency. The ratio of SE/KE was approximately four times larger for lateral excitation than for occipital excitation and was largest at the lowest excitation frequencies studied. These results are consistent with clinical observations that suggest lateral impacts are more likely to cause injury than occipital or frontal impacts, and also with observations that the brain has low-frequency (∼10 Hz) natural modes of oscillation. The SE/KE ratio from brain MRE is potentially a simple and powerful dimensionless metric of brain vulnerability to deformation and injury.

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.

Objective: Chronic subdural hematoma (cSDH) is prevalent globally and its management is evolving to minimize morbidity while  optimizing theater utilization. We present our institution’s experience with subdural evacuation port system (SEPS) as a first-line  treatment approach to cSDHs.

Methods: A retrospective review was performed of patients undergoing bedside SEPS placement in a  single institution. Pre- and post-procedural radiographic and clinical data were collected and analyzed to identify predictive variables of  procedural success for the SEPS approach. For procedure failures, subsequent procedures were analyzed for rates of success.

Results:  268 patients were identified for a total of 326 initial procedures. Pre-procedural variables associated with improved odds of a good  outcome were: unilateral cSDH, prior use of anticoagulation, GCS > 13 at presentation, larger cSDH, and greater degree of midline shift (MLS). 65% success rate was observed for initial SEPS placement and an overall success of 78% after repeat SEPS. Bilateral SDH with  bilateral SEPS placement had 56% success, a significantly lower success rate than unilateral placement (p=0.0147). Patients with  subsequent failures underwent craniotomy. Patients who had a successful SEPS procedure had an average LOS of 13 ± 39 days compared  to 25 ± 65 in incidents of failure (p=0.047). Average follow-up after discharge was 2.8 ± 3.8 months.

Conclusions: Bedside SEPS  placement is a low-risk option for first-line treatment of cSDH and may spare patients from the risks of general anesthesia while  reducing burden on surgical theaters in resource-limited settings. Performing a repeat SEPS procedure is a reasonable surgical option if  the first procedure fails to completely evacuate the cSDH. 

Purpose

To determine how the biomechanical vulnerability of the human brain is affected by features of individual anatomy and loading.

Methods

To identify the features that contribute most to brain vulnerability, we imparted mild harmonic acceleration to the head and measured the resulting brain motion and deformation using magnetic resonance elastography (MRE). Oscillatory motion was imparted to the heads of adult participants using a lateral actuator (n = 24) or occipital actuator (n = 24) at 20 Hz, 30 Hz, and 50 Hz. Displacement vector fields and strain tensor fields in the brain were obtained from MRE measurements. Anatomical images, as well as displacement and strain fields from each participant were rigidly and deformably aligned to a common atlas (MNI-152). Vulnerability of the brain to deformation was quantified by the ratio of strain energy (SE) to kinetic energy (KE) for each participant. Similarity of deformation patterns between participants was quantified using strain field correlation (C_V). Linear regression models were used to identify the effect of similarity of brain size, shape, and age, as well as similarity of loading, on C_V.

Results

The SE/KE ratio decreased with frequency and was larger for participants undergoing lateral, rather than occipital, actuation. Head rotation about the inferior–superior axis was correlated with larger SE/KE ratio. Strain field correlations were primarily affected by the similarity of rigid-body motion.

Conclusion

The motion applied to the skull is the most important factor in determining both the vulnerability of the brain to deformation and the similarity between strain fields in different individuals.

Noninvasive measurements of brain deformation in human participants in vivo are needed to develop models of brain biomechanics and understand traumatic brain injury (TBI). Tagged magnetic resonance imaging (tagged MRI) and magnetic resonance elastography (MRE) are two techniques to study human brain deformation; these techniques differ in the type of motion and difficulty of implementation. In this study, oscillatory strain fields in the human brain caused by impulsive head acceleration and measured by tagged MRI were compared quantitatively to strain fields measured by MRE during harmonic head motion at 10 and 50 Hz. Strain fields were compared by registering to a common anatomical template, then computing correlations between the registered strain fields. Correlations were computed between tagged MRI strain fields in six participants and MRE strain fields at 10 Hz and 50 Hz in six different participants. Correlations among strain fields within the same experiment type were compared statistically to correlations from different experiment types. Strain fields from harmonic head motion at 10 Hz imaged by MRE were qualitatively and quantitatively similar to modes excited by impulsive head motion, imaged by tagged MRI. Notably, correlations between strain fields from 10 Hz MRE and tagged MRI did not differ significantly from correlations between strain fields from tagged MRI. These results suggest that low-frequency modes of oscillation dominate the response of the brain during impact. Thus, low-frequency MRE, which is simpler and more widely available than tagged MRI, can be used to illuminate the brain's response to head impact.

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.

We propose a robust framework for quantitatively comparing model-predicted and experimentally measured strain fields in the human brain during harmonic skull motion. Traumatic brain injuries (TBIs) are typically caused by skull impact or acceleration, but how skull motion leads to brain deformation and consequent neural injury remains unclear and comparison of model predictions to experimental data remains limited. Magnetic resonance elastography (MRE) provides high-resolution, full-field measurements of dynamic brain deformation induced by harmonic skull motion. In the proposed framework, full-field strain measurements from human brain MRE in vivo are compared to simulated strain fields from models with similar harmonic loading. To enable comparison, the model geometry and subject anatomy, and subsequently, the predicted and measured strain fields are nonlinearly registered to the same standard brain atlas. Strain field correlations (Cv), both global (over the brain volume) and local (over smaller sub-volumes), are then computed from the inner product of the complex-valued strain tensors from model and experiment at each voxel. To demonstrate our approach, we compare strain fields from MRE in six human subjects to predictions from two previously developed models. Notably, global Cv values are higher when comparing strain fields from different subjects (Cv~0.6–0.7) than when comparing strain fields from either of the two models to strain fields in any subject. The proposed framework provides a quantitative method to assess similarity (and to identify discrepancies) between model predictions and experimental measurements of brain deformation and thus can aid in the development and evaluation of improved models of brain biomechanics.