Wolfgang Stefan

Kinetic Modeling and Constrained Reconstruction of Hyperpolarized [1-13C]-Pyruvate Offers Improved Metabolic Imaging of Tumors

Hyperpolarized [1-(13)C]-pyruvate has shown tremendous promise as an agent for imaging tumor metabolism with unprecedented sensitivity and specificity. Imaging hyperpolarized substrates by magnetic resonance is unlike traditional MRI because signals are highly transient and their spatial distribution varies continuously over their observable lifetime. Therefore, new imaging approaches are needed to ensure optimal measurement under these circumstances. Constrained reconstruction algorithms can integrate prior information, including biophysical models of the substrate/target interaction, to reduce the amount of data that is required for image analysis and reconstruction. In this study, we show that metabolic MRI with hyperpolarized pyruvate is biased by tumor perfusion and present a new pharmacokinetic model for hyperpolarized substrates that accounts for these effects. The suitability of this model is confirmed by statistical comparison with alternates using data from 55 dynamic spectroscopic measurements in normal animals and murine models of anaplastic thyroid cancer, glioblastoma, and triple-negative breast cancer. The kinetic model was then integrated into a constrained reconstruction algorithm and feasibility was tested using significantly undersampled imaging data from tumor-bearing animals. Compared with naïve image reconstruction, this approach requires far fewer signal-depleting excitations and focuses analysis and reconstruction on new information that is uniquely available from hyperpolarized pyruvate and its metabolites, thus improving the reproducibility and accuracy of metabolic imaging measurements.

Photoacoustic-based sO2 estimation through excised bovine prostate tissue with interstitial light delivery

Photoacoustic (PA) imaging is capable of probing blood oxygen saturation (sO2), which has been shown to correlate with tissue hypoxia, a promising cancer biomarker. However, wavelength-dependent local fluence changes can compromise sO2 estimation accuracy in tissue. This work investigates using PA imaging with interstitial irradiation and local fluence correction to assess precision and accuracy of sO2 estimation of blood samples through ex vivo bovine prostate tissue ranging from 14% to 100% sO2. Study results for bovine blood samples at distances up to 20 mm from the irradiation source show that local fluence correction improved average sO2 estimation error from 16.8% to 3.2% and maintained an average precision of 2.3% when compared to matched CO-oximeter sO2 measurements. This work demonstrates the potential for future clinical translation of using fluence-corrected and interstitially driven PA imaging to accurately and precisely assess sO2 at depth in tissue with high resolution.

Development of a dual-energy computed tomography quality control program: Characterization of scanner response and definition of relevant parameters for a fast-kVp switching dual-energy computed tomography system

PURPOSEnA prototype QC phantom system and analysis process were developed to characterize the spectral capabilities of a fast kV-switching dual-energy computed tomography (DECT) scanner. This work addresses the current lack of quantitative oversight for this technology, with the goal of identifying relevant scan parameters and test metrics instrumental to the development of a dual-energy quality control (DEQC).nnnMETHODSnA prototype elliptical phantom (effective diameter: 35 cm) was designed with multiple material inserts for DECT imaging. Inserts included tissue equivalent and material rods (including iodine and calcium at varying concentrations). The phantom was scanned on a fast kV-switching DECT system using 16 dual-energy acquisitions (CTDIvol range: 10.3-62 mGy) with varying pitch, rotation time, and tube current. The circular head phantom (22 cm diameter) was scanned using a similar protocol (12 acquisitions; CTDIvol range: 36.7-132.6 mGy). All acquisitions were reconstructed at 50, 70, 110, and 140 keV and using a water-iodine material basis pair. The images were evaluated for iodine quantification accuracy, stability of monoenergetic reconstruction CT number, noise, and positional constancy. Variance component analysis was used to identify technique parameters that drove deviations in test metrics. Variances were compared to thresholds derived from manufacturer tolerances to determine technique parameters that had a nominally significant effect on test metrics.nnnRESULTSnIodine quantification error was largely unaffected by any of the technique parameters investigated. Monoenergetic HU stability was found to be affected by mAs, with a threshold under which spectral separation was unsuccessful, diminishing the utility of DECT imaging. Noise was found to be affected by CTDIvol in the DEQC body phantom, and CTDIvol and mA in the DEQC head phantom. Positional constancy was found to be affected by mAs in the DEQC body phantom and mA in the DEQC head phantom.nnnCONCLUSIONnA streamlined scan protocol was developed to further investigate the effects of CTDIvol and rotation time while limiting data collection to the DEQC body phantom. Further data collection will be pursued to determine baseline values and statistically based failure thresholds for the validation of long-term DECT scanner performance.

Automated algorithms for improved pre-processing of magnetic relaxometry 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.

Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry

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.

Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles

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.

Quantitative 3D assessment of flow in a printed hydrogel vascular phantom

While tissue-mimicking phantoms have been useful in the validation of ultrasonic equipment and image processing techniques, they are often restricted to simple vascular geometries due to limitations in fabrication techniques. To address this need, a novel technique has been employed to fabricate 3D-printed photo-curable poly(ethylene glycol) (PEG) hydrogel constructs containing complex, small-scale (e.g., 100s of μm in diameter) vascular channels that can be imaged with ultrasound. In this study, we use color Doppler ultrasound to obtain 3D velocity vector fields through vascular geometries within hydrogel phantoms and compare these results to optical flow assessment and numerical modeling results.

Gaussian process classification of superparamagnetic relaxometry data: Phantom study

MOTIVATIONnSuperparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image.nnnOBJECTIVEnOur goal is to examine the use of data-driven machine learning technique to detect a weak signal induced by a small cluster of SPIONs (surrogate tumor) in presence of background signal and measurement uncertainty. We aim to investigate the performance of both data-driven and image reconstruction models to characterize situations that one can replace the computationally-challenging reconstruction technique by the data-driven model.nnnMETHODSnWe utilize Gaussian process (GP) classification model and a physics-based image reconstruction method, tailored to SPMR datasets that are obtained from (i) in silico simulations designed based on mouse cancer models and (ii) phantom experiments using MagSense system (Imagion Biosystems, Inc.). We investigate the performance of the GP classifier against the reconstruction technique, for different levels of measurement noise, different scenarios of SPIONs distribution, and different concentrations of SPIONs at the surrogate tumor.nnnRESULTSnIn our in silico source detection analysis, we were able to achieve high sensitivity results using GP model that outperformed the image reconstruction model for various choices of SPIONs concentration at the surrogate tumor and measurement noise levels. Moreover, in our phantom studies we were able to detect the surrogate tumor phantoms with 5% and 7.3% of the total used SPIONs, surrounded by 9 low-concentration phantoms with accuracies of 87.5% and 96.4%, respectively.nnnCONCLUSIONSnThe GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data.

TU-F-CAMPUS-I-05: Semi-Automated, Open Source MRI Quality Assurance and Quality Control Program for Multi-Unit Institution

Purpose: Phantom measurements allow for the performance of magnetic resonance (MR) systems to be evaluated. Association of Physicists in Medicine (AAPM) Report No. 100 Acceptance Testing and Quality Assurance Procedures for MR Imaging Facilities, American College of Radiology (ACR) MR Accreditation Program MR phantom testing, and ACR MRI quality control (QC) program documents help to outline specific tests for establishing system performance baselines as well as system stability over time. Analyzing and processing tests from multiple systems can be time-consuming for medical physicists. Besides determining whether tests are within predetermined limits or criteria, monitoring longitudinal trends can also help prevent costly downtime of systems during clinical operation. In this work, a semi-automated QC program was developed to analyze and record measurements in a database that allowed for easy access to historical data. Methods: Image analysis was performed on 27 different MR systems of 1.5T and 3.0T field strengths from GE and Siemens manufacturers. Recommended measurements involved the ACR MRI Accreditation Phantom, spherical homogenous phantoms, and a phantom with an uniform hole pattern. Measurements assessed geometric accuracy and linearity, position accuracy, image uniformity, signal, noise, ghosting, transmit gain, center frequency, and magnetic field drift. The program was designed with open source tools, employing Linux, Apache, MySQL database and Python programming language for the front and backend. Results: Processing time for each image is <2 seconds. Figures are produced to show regions of interests (ROIs) for analysis. Historical data can be reviewed to compare previous year data and to inspect for trends. Conclusion: A MRI quality assurance and QC program is necessary for maintaining high quality, ACR MRI Accredited MR programs. A reviewable database of phantom measurements assists medical physicists with processing and monitoring of large datasets. Longitudinal data can reveal trends that although are within passing criteria indicate underlying system issues.

SU‐E‐I‐32: Improving Vessel Delineation in Brain Using Susceptibility Weighted MRI and Group Sparse Reconstruction

PURPOSEnTo optimize small vessel delineation on non-contrast enhanced susceptibility weighted MRI protocol for MRI guided intervention in the brain.nnnMETHODSnMultiple 5mm slices of the brain in a healthy volunteer were acquired with a 16-echo gradient echo sequence and a 32 channel head coil on a 3T scanner. K-space was under-sampled by a factor of 2. Images were reconstructed using 1) the vendor ASSET algorithm, 2) sparsity enforcing TV regularization applied to each slice and echo individually, and 3) group sparse reconstruction on each slice individually but all echoes simultaneously. The group sparse reconstruction increases the signal-tonoise ratio SNR of later echoes by utilizing the shared edges of brain structure between the echoes, which include the blood vessels. Quantitative PPM maps are computed from the complex images and are post processed by a method that removes background off-resonance effects. Magnitude images and corrected PPM maps are compared.nnnRESULTSnPPM maps show blood vessels with high contrast. The magnitude images and ppm maps using method 1 appear blurry compared to methods 2 and 3 because of anti-ringing filters. While small vasculatures appear very sharp in the ppm maps of methods 2 and 3, method 3 appears to have less smoothed magnitude images.nnnCONCLUSIONnTo obtain useful blood vessel delineation, low noise susceptibility weighted images are needed. Standard methods like SWI or SWAN require the combination of several slices using a minimum intensity projection to delineate vessels. The method considered here delineates vasculature with high spatial resolution and high SNR in a single slice. Iterative reconstruction methods have superior image quality but also require much more computation time. By reconstructing all echoes simultaneously, group sparse reconstruction produces the sharpest and clearest images.