The growing use of superparamagnetic iron oxide nanoparticles (SPIONs) in early cancer detection technologies has created a demand for physiologically-based pharmacokinetic (PBPK) models that accurately model and predict the biodistribution of SPIONs in the mouse and human model. The objective of this work is to use a Bayesian approach built upon nested-sampling to select a model based on qualitative criteria of the fit of the model and the likelihood function landscape, as well as quantitative criteria of the evidence and maximum likelihood values. Four first-order PBPK compartmental models of ranging complexity are considered. Compartments included in the models comprise of a combination of the plasma, liver, spleen, tumor, and “other” (the remaining body tissue), with parameters including the volume, blood flow rate, and plasma:tissue distribution ratios. The model parameters for each model are evaluated using Bayesian inference, in addition to the respective evidence integrals, maximum log-likelihoods, and Bayes factors. The model containing all compartments and the model containing the plasma, liver, tumor and “other” had the highest log-likelihood and evidence values, indicating both a high goodness-of-fit and a high likelihood of the model given the data. This is similarly reflected in a faithful quality-of-fit and non-flat log-likelihood landscapes. Overall, these findings illustrate the strength of the Bayesian model selection framework in ranking different models to determine the best model that accurately represents the experimental data.
We present a novel method to pre-process magnetic relaxation (MRX) data. The method is used to estimates the initial magnetic field generated by Super Paramagnetic Nano Particles (SPIONs) from decay curves measured by superconducting quantum interference devices (SQUIDs). The curves are measured using a MagSense MRX Instrument (PrecisionMRX, Imagion Biosystems, Albuquerque, NM). We compare the initial field estimates to the standard method used by Imagion Biosystems. As compared to the standard method our new method results in more stable estimates in the presence of noise and allows monitoring of the long term stability of the MagSense MRX instrument. We demonstrate these findings with phantom scans conducted over the period of about one year.
Ovarian cancer survival rates could be greatly improved through effective early detection. However, several clinical studies have shown that proposed screening methodologies have no impact on overall survival. Our lab is participating in the development of a novel nanoparticle imaging device that can be incorporated as a third-line test to improve the specificity and sensitivity of the overall screening program. The device’s highly sensitive detectors can detect the residual magnetic field of only those nanoparticles that have become bound to cancer cells via specific antibody interactions. However, the reconstruction of the bound particle distribution from this residual field map is challenging due to the highly ill-posed nature of the inverse problem. Our lab has developed a sparse reconstruction algorithm to overcome this challenge. Here, we present the results of a blinded phantom study to simulate the pre-clinical scenario of detecting a tumor signal in the presence of a large signal from bound particles in the liver. Overall, our algorithm identified the correct location of bound particle sources with 84% accuracy. We were able to detect as little as 1.6ug of bound particles with 100% accuracy when the source was alone, and as little as 3.13ug when there was a stronger source present. We also show the effect of manual and automatic parameter selection on the performance of the algorithm. These results provide valuable information about the expected performance of the algorithm that we can use to optimize the design of future small animal studies as we work to bring this novel technology to the clinic.
Synthetic magnetic resonance imaging (MRI) is a method for obtaining parametric maps of tissue properties from one scan and using these to reconstruct multiple contrast weighted images. This reduces the scan time necessary to produce multiple series of different contrast weightings and potentially provides additional diagnostic utility. For synthetic MRI, current acquisition parameter selection and subsampling approaches (such as variable density Poisson disc sampling) are heuristic in nature. We develop a mutual information-based mathematical framework to quantify the information content of a parameter space composed of k-space and several pulse sequence acquisition parameters of interest for model-based image reconstruction. We apply this framework to the signal model for a multi-contrast inversion- and T2-prepared gradient echo sequence. This pulse sequence is modeled for in silico data and used for the acquisition of phantom data. Mutual information between parametric map uncertainty and measured data is determined for variable acquisition parameters to characterize the performance of each acquisition. Mutual information is calculated by Gauss-Hermite quadrature and a global search over acquisition parameter space. We demonstrate the possibility of mutual informationguided subsampling schemes on phantom image data. Fully-sampled images of a silicone gel phantom and a water phantom are acquired on a 3T imager. Subsampling methods are applied to this data before it is reconstructed using the Berkeley Advanced Reconstruction Toolbox (BART). This framework allows for the strategic selection of synthetic MR acquisition parameters and subsampling schemes for specific applications and also provides a quantitative understanding of parameter space information content in an acquisition for multi-parameter mapping.
In this paper, we present a model-based predictive control system that is capable of capturing physical and biological variations of laser-tissue interaction as well as heterogeneity in real-time during laser induced thermal therapy (LITT). Using a three-dimensional predictive bioheat transfer model, which is built based on regular
magnetic resonance imaging (MRI) anatomic scan and driven by imaging data produced by real-time magnetic resonance temperature imaging (MRTI), the computational system provides a regirous real-time predictive control during surgical operation process. The unique feature of the this system is its ability for predictive control
based on validated model with high precision in real-time, which is made possible by implementation of efficient
parallel algorithms. The major components of the current computational systems involves real-time finite element
solution of the bioheat transfer induced by laser-tissue interaction, solution module of real-time calibration
problem, optimal laser source control, goal-oriented error estimation applied to the bioheat transfer equation,
and state-of-the-art imaging process module to characterize the heterogeneous biological domain. The system
was tested in vivo in a canine animal model in which an interstitial laser probe was placed in the prostate region
and the desired treatment outcome in terms of ablation temperature and damage zone were achieved. Using the
guidance of the predictive model driven by real-time MRTI data while applying the optimized laser heat source
has the potential to provide unprecedented control over the treatment outcome for laser ablation.