Bulletin of the American Physical Society
APS April Meeting 2023
Volume 68, Number 6
Minneapolis, Minnesota (Apr 15-18)
Virtual (Apr 24-26); Time Zone: Central Time
Session JJ03: V: Physics in Medicine: Modeling and New Technologies for Healthcare ApplicationsUndergrad Friendly
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Sponsoring Units: GMED Chair: Wojciech Zbijewski, Johns Hopkins University Room: Virtual Room 3 |
Monday, April 24, 2023 6:00PM - 6:12PM |
JJ03.00001: Remote determination of phase separation with Femtosecond thermal lens spectroscopy for sensitive in-situ biomedical applications Debabrata Goswami We demonstrate sensitive detection of the liquid-gas phase interface through our novel Femtosecond laser-induced Thermal Lens Spectroscopy (FTLS) experiments. FTLS is built on the principle that the high repetition rate of femtosecond lasers induces a cumulative thermal effect even in highly volatile media. The cumulative heat load is significantly higher in a shorter time, which results in the divergence of the propagating laser beam through nearly transparent samples. A decrease in the system's refractive index due to heat deposition results in laser beam divergence, also providing sensitive spectroscopic fingerprints. The thermal lens effect is governed by an interplay of thermal load and thermal dissipation, which differ for each phase. In the liquid phase, thermal dissipation needs to be described in terms of convective and conductive processes, while in the solid phase, conduction effects dominate and are sufficient to explain the dissipation dynamics. Our experiments across several sample interfaces show how sensitive our FTLS technique is to changes in phase separation interfaces, which can provide an accurate size measure of aerosols. Mapping microscopic aerosol distribution is also critical for COVID-19 transmission, which occurs through the dispersion of aerosol droplets containing the virus. FTLS thus paves the way for selective and controlled laser ablation with biomedical applications, including inhibiting cell cycle pathways or cell division for disease mitigation. |
Monday, April 24, 2023 6:12PM - 6:24PM |
JJ03.00002: An Investigation of Spiked Hypofractionation for Treating Large Brain Tumors Lijun Ma, Eric Chang Purpose: |
Monday, April 24, 2023 6:24PM - 6:36PM |
JJ03.00003: Condition Number Phase Transitions for Model Selection in Magnetic Resonance Relaxometry Griffin S Hampton, Ryan Neff, Tyler Hecht, Pak-Wing Fok, Richard G Spencer In magnetic resonance (MR), the biexponential model describes the decay signal of macromolecules in ambient non-bound water. This provides an important means of assessing myelin status in the brain. The inversion-recovery experiment in MR, perhaps uniquely in experimental science, permits the nulling of a selected term in a biexponential model. At the inversion times (TI) resulting in component nulling, experimental data is monoexponential, so that fitting to the full biexponential model is underdetermined and results in parameter instability. We use this fact to identify the null points numerically by observing the standard deviation (SD), across noise realizations, of parameter estimates as a function of TI. Knowledge of these values of TI reduces the dimensionality of the parameter estimation problem. The Bayesian information criterion provides a further indication of the null points by identifying the range of TI values at which the signal is better-fitted with a mono- rather than a biexponential model. Finally, we use persistence homology from topological data analysis to characterize the range of acceptable parameter estimates to determine the null points. These methods all permit improved stability of parameter estimates for the two-dimensional biexponential model in MR. |
Monday, April 24, 2023 6:36PM - 6:48PM |
JJ03.00004: Improved Biexponential Decay Parameter Estimation from Input-Layer Regularization of a Neural Network Mirage Modi, Griffin S Hampton, Jonathan L Palumbo, Michael Rozowski, Mustapha Bouhrara, Wojciech Czaja, Richard G Spencer A biexponential decay model can be used to describe relaxation processes in magnetic resonance relaxometry (MRR) studies of biophysical systems. However, the inverse problem of biexponential signal parameter estimation is known to be ill-posed, with distinct parameter sets mapping to nearly identical biexponential decay curves. Recently, neural network (NN) approaches have been found to outperform the conventional method of nonlinear least squares (NLLS) for this problem. Several methods of regularization have been introduced into NN analysis to prevent overfitting; here, we further develop a NN architecture based on Tikhonov regularization at the input layer. We find that this input layer regularization (ILR) of noisy decay signals improves accuracy and precision over conventional NN approaches for two-parameter decay constant estimation, indicating the stabilization effect of ILR of a parameter estimation NN. Our main application of these results will be to MRR studies of macromolecular mapping in the brain. For these applications, we are developing a 3-parameter estimation procedure using ILR of a NN that, in addition to providing estimates of relaxation times, also defines values for component sizes in the biexponential. We make use of multiple CPU processors to parallelize dataset generation for more efficient dataset generation techniques to render these higher-dimensional analyses more tractable. We are also implementing a NN-guided selection of an optimal regularization parameter. |
Monday, April 24, 2023 6:48PM - 7:00PM |
JJ03.00005: A Deep Learning Approach to Predicting Age-Related Gait Speed Decline Michael Mckenna, Alison Deatsch, Jonathan L Palumbo, Qu Tian, Robert Jeraj, Eleanor Simonsick, Luigi Ferrucci, Richard G Spencer Gait speed is a critical measure of healthy aging outcomes due to its strong association with mortality and brain age. Understanding the hallmarks and predictors of healthy aging is essential to increasing population health span. Many attempts to predict gait speed decline and identify its key biomarkers use traditional regression-based models. However, these statistical models are only capable of testing a limited set of potential predictors. Deep learning (DL) techniques have been successful in a variety of biomedical applications and are well-suited to overcome this limitation, handling numerous input variables across multiple domains. Thus, we developed a 3-layer, DL binary classifier which predicts incident slow gait across several time frames. The neural network was trained on 15 biomarkers collected from 13,796 observations of the Baltimore Longitudinal Study of Aging. The biomarkers encompassed physiological and lifestyle variables previously identified as potentially relevant to gait speed decline. Model performance was evaluated by precision-recall statistics and compared to linear and logistic regressions. Sensitivity studies were performed on multiple domains to optimize performance. Sobol Indices identified high impact input variables. This model offers an advanced tool for prediction of incident slow gait which has the potential to aid in targeted care of individuals at risk of poor aging trajectories. |
Monday, April 24, 2023 7:00PM - 7:12PM |
JJ03.00006: Shoulder Integrity Evaluation of Whole Person Impairment Rating Specialists Jerry L Artz, Josiah Biernat, John Alchemy, Bruce L Bolon, Mari Johnson De Tora Whole Person Impairment (WPI) Ratings are medical evaluations performed on injured workers seeking insurance settlements in the workers’ compensation insurance system. The examinations performed and reports prepared by medical providers vary considerably in terms of consistency, accuracy, and reproducibility. Incomplete reports and variable outcomes often result in litigation and additional costs for all stakeholders in the system. We have created a systematic inventory model to evaluate quality, completeness, and consistency with a patented impairment method currently used in the marketplace. We have selected the shoulder exam to serve as a pilot to create a database for research. The “gold-standard” outlined in the American Medical Association Guides to the Evaluation of Permanent Impairment (AMA Guides) is used to determine the complete criteria for history, physical exam elements, diagnostic testing, and surgical procedures performed. The exam findings are entered into the system, and an “integrity score” (0-100%) is created for each examiner. Each evaluator is then compared to all other examiners in the study to generate a performance percentile report, indicating overall areas of strengths and weaknesses thereby identifying areas of investment and education for the medical examiners. This new data analysis method allows stakeholders to assess and predict the value of medical determinations using this system to expedite settlement and increase confidence in outcomes. |
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