A Novel In Vitro Device to Deliver Induced Electromagnetic Fields to Cell and Tissue Cultures.

We have developed a novel, to our knowledge, in vitro instrument that can deliver intermediate-frequency (100-400 kHz), moderate-intensity (up to and exceeding 6.5 V/cm pk-pk) electric fields (EFs) to cell and tissue cultures generated using induced electromagnetic fields (EMFs) in an air-core solenoid coil. A major application of these EFs is as an emerging cancer treatment modality. In vitro studies by Novocure reported that intermediate-frequency (100-300 kHz), low-amplitude (1-3 V/cm) EFs, which they called "tumor-treating fields (TTFields)," had an antimitotic effect on glioblastoma multiforme (GBM) cells. The effect was found to increase with increasing EF amplitude. Despite continued theoretical, preclinical, and clinical study, the mechanism of action remains incompletely understood. All previous in vitro studies of "TTFields" have used attached, capacitively coupled electrodes to deliver alternating EFs to cell and tissue cultures. This contacting delivery method suffers from a poorly characterized EF profile and conductive heating that limits the duration and amplitude of the applied EFs. In contrast, our device delivers EFs with a well-characterized radial profile in a noncontacting manner, eliminating conductive heating and enabling thermally regulated EF delivery. To test and demonstrate our system, we generated continuous, 200-kHz EMF with an EF amplitude profile spanning 0-6.5 V/cm pk-pk and applied them to exemplar human thyroid cell cultures for 72 h. We observed moderate reduction in cell density (<10%) at low EF amplitudes (<4 V/cm) and a greater reduction in cell density of up to 25% at higher amplitudes (4-6.5 V/cm). Our device can be readily extended to other EF frequency and amplitude regimes. Future studies with this device should contribute to the ongoing debate about the efficacy and mechanism(s) of action of "TTFields" by better isolating the effects of EFs and providing access to previously inaccessible EF regimes.

Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce.

BACKGROUND: Naturalistic driving studies, designed to objectively assess driving behavior and outcomes, are conducted by equipping vehicles with dedicated instrumentation (eg, accelerometers, gyroscopes, Global Positioning System, and cameras) that provide continuous recording of acceleration, location, videos, and still images for eventual retrieval and analyses. However, this research is limited by several factors: the cost of equipment installation; management and storage of the large amounts of data collected; and data reduction, coding, and analyses. Modern smartphone technology includes accelerometers built into phones, and the vast, global proliferation of smartphones could provide a possible low-cost alternative for assessing kinematic risky driving. OBJECTIVE: We evaluated an in-house developed iPhone app (gForce) for detecting elevated g-force events by comparing the iPhone linear acceleration measurements with corresponding acceleration measurements obtained with both a custom Android app and the in-vehicle miniDAS data acquisition system (DAS; Virginia Tech Transportation Institute). METHODS: The iPhone and Android devices were dashboard-mounted in a vehicle equipped with the DAS instrumentation. The experimental protocol consisted of driving maneuvers on a test track, such as cornering, braking, and turning that were performed at different acceleration levels (ie, mild, moderate, or hard). The iPhone gForce app recorded linear acceleration (ie, gravity-corrected). The Android app recorded gravity-corrected and uncorrected acceleration measurements, and the DAS device recorded gravity-uncorrected acceleration measurements. Lateral and longitudinal acceleration measures were compared. RESULTS: The correlation coefficients between the iPhone and DAS acceleration measurements were slightly lower compared to the correlation coefficients between the Android and DAS, possibly due to the gravity correction on the iPhone. Averaging the correlation coefficients for all maneuvers, the longitudinal and lateral acceleration measurements between iPhone and DAS were rlng=0.71 and rlat=0.83, respectively, while the corresponding acceleration measurements between Android and DAS were rlng=0.95 and rlat=0.97. The correlation coefficients between lateral accelerations on all three devices were higher than with the corresponding longitudinal accelerations for most maneuvers. CONCLUSIONS: The gForce iPhone app reliably assessed elevated g-force events compared to the DAS. Collectively, the gForce app and iPhone platform have the potential to serve as feature-rich, inexpensive, scalable, and open-source tool for assessment of kinematic risky driving events, with potential for research and feedback forms of intervention.

Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI.

PURPOSE: The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in "positive-if-all-positive" manner, from different models to localize tumors. METHODS: A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65years (range 54-78) and 21.9ng/mL (range 4.84-44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0-2000s/mm2 (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm3, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2). RESULTS: The proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients. CONCLUSION: Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.

A multivariate hypothesis testing framework for tissue clustering and classification of DTI data.

The primary aim of this work is to propose and investigate the effectiveness of a novel unsupervised tissue clustering and classification algorithm for diffusion tensor MRI (DTI) data. The proposed algorithm utilizes information about the degree of homogeneity of the distribution of diffusion tensors within voxels. We adapt frameworks proposed by Hext and Snedecor, where the null hypothesis of diffusion tensors belonging to the same distribution is assessed by an F-test. Tissue type is classified according to one of the four possible diffusion models, the assignment of which is determined by a parsimonious model selection framework based on Schwarz Criterion. Both numerical phantoms and diffusion-weighted imaging (DWI) data obtained from excised rat and pig spinal cords are used to test and validate these tissue clustering and classification approaches. The unsupervised clustering method effectively identifies distinct regions of interest (ROIs) in phantoms and real experimental DTI data.

Parsimonious model selection for tissue segmentation and classification applications: a study using simulated and experimental DTI data.

One aim of this work is to investigate the feasibility of using a hierarchy of models to describe diffusion tensor magnetic resonance (MR) data in fixed tissue. Parsimonious model selection criteria are used to choose among different models of diffusion within tissue. Using this information, we assess whether we can perform simultaneous tissue segmentation and classification. Both numerical phantoms and diffusion weighted imaging (DWI) data obtained from excised pig spinal cord are used to test and validate this model selection framework. Three hierarchical approaches are used for parsimonious model selection: the Schwarz criterion (SC), the F-test t-test (F- t), proposed by Hext, and the F-test F-test (F-F), adapted from Snedecor. The F - t approach is more robust than the others for selecting between isotropic and general anisotropic (full tensor) models. However, due to its high sensitivity to the variance estimate and bias in sorting eigenvalues, the F-F and SC are preferred for segmenting models with transverse isotropy (cylindrical symmetry). Additionally, the SC method is easier to implement than the F - t and F - F methods and has better performance. As such, this approach can be efficiently used for evaluating large MRI data sets. In addition, the proposed voxel-by-voxel segmentation framework is not susceptible to artifacts caused by the inhomogeneity of the variance in neighboring voxels with different degrees of anisotropy, which might contaminate segmentation results obtained with the techniques based on voxel averaging.

New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter.

To characterize anisotropic water diffusion in brain white matter, a theoretical framework is proposed that combines hindered and restricted models of water diffusion (CHARMED) and an experimental methodology that embodies features of diffusion tensor and q-space MRI. This model contains a hindered extra-axonal compartment, whose diffusion properties are characterized by an effective diffusion tensor, and an intra-axonal compartment, whose diffusion properties are characterized by a restricted model of diffusion within cylinders. The hindered model primarily explains the Gaussian signal attenuation observed at low b values; the restricted non-Gaussian model does so at high b. Both high and low b data obtained along different directions are required to estimate various microstructural parameters of the composite model, such as the nerve fiber orientation(s), the T2-weighted extra- and intra-axonal volume fractions, and principal diffusivities. The proposed model provides a description of restricted diffusion in 3D given by a 3D probability distribution (average propagator), which is obtained by 3D Fourier transformation of the estimated signal attenuation profile. The new model is tested using synthetic phantoms and validated on excised spinal cord tissue. This framework shows promise in determining the orientations of two or more fiber compartments more precisely and accurately than with diffusion tensor imaging.

AIR-SPAMM: alternative inversion recovery spatial modulation of magnetization for myocardial tagging.

Alternate inversion recovery spatial modulation of magnetization (AIR-SPAMM) can be used either for doubling the number of tags for a given tagging encoding gradient strength or for improving tagging contrast ratio. AIR-SPAMM requires only a single acquisition and utilizes inversion pulses spaced throughout the gradient recalled echo (GRE) cine acquisition to "lock" the recovering magnetization at a desired level. The theory of AIR-SPAMM is presented along with simulations and results from phantoms. AIR-SPAMM can be used either for imaging systole as demonstrated by initial in vivo results or potentially for imaging the entire cardiac cycle in a slice-interleaved manner.

Quantitative assessment of left ventricular perfusion defects using real-time three-dimensional myocardial contrast echocardiography.

Quantitative assessment of perfusion defects with myocardial contrast echocardiography can be a valuable tool in the evaluation of patients with coronary artery disease. However, the use of 2-dimensional echocardiography for this purpose is limited to a restricted number of imaging planes. Real-time 3-dimensional echocardiography (RT3D) is a novel technique that provides instantaneous volumetric images. The aim of this study was to validate the use of RT3D for the quantitative assessment of myocardial perfusion defects in a model of acute coronary occlusion. To this end, 20 sheep underwent acute ligation of the left anterior descending (n = 14) or the posterior branch of the circumflex (n = 6) artery under general anesthesia. The RT3D images were obtained after left atrial injection of the contrast agent EchoGen (perflenapent emulsion; 0.8-1 mL). Evans blue dye was injected into the occluded coronary artery for subsequent anatomic identification of underperfused myocardium. The mass of the entire left ventricle and of the underperfused myocardial region were measured after death. Blinded off-line calculation of left ventricular (LV) mass and perfusion-defect mass from RT3D images were performed using an interactive aided-manual tracing technique. Total LV mass ranged from 68 to 141 g (mean plus minus SD: 92 +/- 24 g). The mass of the perfusion defect ranged from 0 to 43 g (mean +/- SD: 16 +/- 9 g) or 0 to 36% of total LV mass (mean +/- SD: 18% +/- 9%). The RT3D estimation of total LV mass strongly correlated with the anatomic measurement (r = 0.91; y = -2.54 + 1.04x; standard error of the estimate [SEE] = 11.9 g). The RT3D calculation of the mass of underperfused myocardium also strongly correlated with the anatomic measurement, both in absolute terms (r = 0.96; y = 2.01 + 0.87x; SEE = 2.2 g) and when expressed as percentage of total LV mass (r = 0.96; y = 0.11 + 1.02x; SEE = 2.8%). Hence, RT3D with myocardial contrast opacification accurately predicts the amount of underperfused myocardium in an animal model of acute coronary occlusion. This technique may therefore be useful for the quantitative assessment of myocardial perfusion defects in patients with coronary artery disease.