Improved Photoacoustic-Based Oxygen Saturation Estimation With SNR-Regularized Local Fluence Correction.

As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO2) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6%-99.6% SO2) and in vivo rat arteries. Estimation error of absolute SO2 and change in SO2 reduced from 10.1% and 6.4%, respectively, without LFC to 2.8% and 2.0%, respectively, with LFC, while the accuracy of the LFC method was correlated with the number of wavelengths acquired. This paper demonstrates the need for an SNR-regularized LFC to accurately quantify SO2 with PA imaging.

Poly(3-hydroxybutyrate) production in an integrated electromicrobial setup: Investigation under stress-inducing conditions.

Poly(3-hydroxybutyrate) (PHB), a biodegradable polymer, can be produced by different microorganisms. The PHB belongs to the family of polyhydroxyalkanoate (PHA) that mostly accumulates as a granule in the cytoplasm of microorganisms to store carbon and energy. In this study, we established an integrated one-pot electromicrobial setup in which carbon dioxide is reduced to formate electrochemically, followed by sequential microbial conversion into PHB, using the two model strains, Methylobacterium extorquens AM1 and Cupriavidus necator H16. This setup allows to investigate the influence of different stress conditions, such as coexisting electrolysis, relatively high salinity, nutrient limitation, and starvation, on the production of PHB. The overall PHB production efficiency was analyzed in reasonably short reaction cycles typically as short as 8 h. As a result, the PHB formation was detected with C. necator H16 as a biocatalyst only when the electrolysis was operated in the same solution. The specificity of the source of PHB production is discussed, such as salinity, electricity, concurrent hydrogen production, and the possible involvement of reactive oxygen species (ROS).

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.

PURPOSE: A 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). METHODS: A 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. RESULTS: Iodine 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. CONCLUSION: A 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.

Gaussian process classification of superparamagnetic relaxometry data: Phantom study.

MOTIVATION: Superparamagnetic 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. OBJECTIVE: Our 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: The 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.

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.

Production of halophilic proteins using Haloferax volcanii H1895 in a stirred-tank bioreactor.

The success of biotechnological processes is based on the availability of efficient and highly specific biocatalysts, which can satisfy industrial demands. Extreme and remote environments like the deep brine pools of the Red Sea represent highly interesting habitats for the discovery of novel halophilic and thermophilic enzymes. Haloferax volcanii constitutes a suitable expression system for halophilic enzymes obtained from such brine pools. We developed a batch process for the cultivation of H. volcanii H1895 in controlled stirred-tank bioreactors utilising knockouts of components of the flagella assembly system. The standard medium Hv-YPC was supplemented to reach a higher cell density. Without protein expression, cell dry weight reaches 10 g L(-1). Two halophilic alcohol dehydrogenases were expressed under the control of the tryptophanase promoter p.tna with 16.8 and 3.2 mg gCDW (-1), respectively, at a maximum cell dry weight of 6.5 g L(-1). Protein expression was induced by the addition of L-tryptophan. Investigation of various expression strategies leads to an optimised two-step induction protocol introducing 6 mM L-tryptophan at an OD650 of 0.4 followed by incubation for 16 h and a second induction step with 3 mM L-tryptophan followed by a final incubation time of 4 h. Compared with the uncontrolled shaker-flask cultivations used until date, dry cell mass concentrations were improved by a factor of more than 5 and cell-specific enzyme activities showed an up to 28-fold increased yield of the heterologous proteins.

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.