Vinay Pai

Single-shot x-ray differential phase-contrast and diffraction imaging using two-dimensional transmission gratings

We describe an x-ray differential phase-contrast imaging method based on two-dimensional transmission gratings that are directly resolved by an x-ray camera. X-ray refraction and diffraction in the sample lead to variations of the positions and amplitudes of the grating fringes on the camera. These effects can be quantified through spatial harmonic analysis. The use of 2D gratings allows differential phase contrast in several directions to be obtained from a single image. When compared to previous grating-based interferometry methods, this approach obviates the need for multiple exposures and separate measurements for different directions and thereby accelerates imaging speed.

Recent Advances in Wearable Sensors for Health Monitoring

Wearable sensor technology continues to advance and provide significant opportunities for improving personalized healthcare. In recent years, advances in flexible electronics, smart materials, and low-power computing and networking have reduced barriers to technology accessibility, integration, and cost, unleashing the potential for ubiquitous monitoring. This paper discusses recent advances in wearable sensors and systems that monitor movement, physiology, and environment, with a focus on applications for Parkinsons disease, stroke, and head and neck injuries.

Interpretation of dark-field contrast and particle-size selectivity in grating interferometers

In grating-based x-ray phase sensitive imaging, dark-field contrast refers to the extinction of the interference fringes due to small-angle scattering. For configurations where the sample is placed before the beamsplitter grating, the dark-field contrast has been quantified with theoretical wave propagation models. Yet when the grating is placed before the sample, the dark-field contrast has only been modeled in the geometric optics regime. Here we attempt to quantify the dark-field effect in the grating-before-sample geometry with first-principle wave calculations and understand the associated particle-size selectivity. We obtain an expression for the dark-field effect in terms of the sample materials complex refractive index, which can be verified experimentally without fitting parameters. A dark-field computed tomography experiment shows that the particle-size selectivity can be used to differentiate materials of identical x-ray absorption.

In vivo cardiac diffusion-weighted magnetic resonance imaging: quantification of normal perfusion and diffusion coefficients with intravoxel incoherent motion imaging.

ObjectivesDiffusion-weighted imaging (DWI) and the introduction of the intravoxel incoherent motion (IVIM) model have provided a unique method for evaluating perfusion and diffusion within a tissue without the need for a contrast agent. Despite its relevance, cardiac DWI has thus far been limited by low b values because of signal loss induced by physiological motion. The goal of this study was to develop a methodology for estimating IVIM parameters of in vivo cardiac magnetic resonance imaging using an efficient DWI acquisition framework. This was achieved by investigating various acquisition strategies (principal component analysis [PCA] filtering and temporal maximum intensity projection [PCATMIP] and single trigger delay [TD]) and fitting methods. Material and MethodsSimulations were performed on a synthetic dataset of diffusion-weighted signal intensity (SI) to determine the fitting method that would yield IVIM parameters with the greatest accuracy. The required number of b values to correctly estimate IVIM parameters was also investigated. Breath-hold DWI scans were performed for 12 volunteers to collect several TD values during diastole. Thirteen b values ranging from 0 to 550 s/mm2 were used. The IVIM parameters derived using the data from all the acquired TDs (PCATMIP technique) were compared with those derived using a single acquisition performed at an optimized diastolic time point (1TD). ResultsThe main result of this study was that PCATMIP, when combined with a fitting model that accounted for T1 and T2 relaxation, provided IVIM parameters with less variability. However, an acquisition performed with 1 optimized diastolic TD provided results that were as good as those provided using PCATMIP if the R-R variability during the acquisition was sufficiently low (±5%). Furthermore, the use of only 9 b values (that could be acquired in 2 breath-holds), instead of 13 b values (requiring 3 breath-holds), was sufficient to determine the IVIM parameters. ConclusionsThis study demonstrates that IVIM is technically feasible invivo and reports for the first time the perfusion fraction, f, and the diffusion coefficients, D and D*, for the cardiac DWI of healthy volunteers. Motion-induced signal loss, which is the main problem associated with cardiac DWI, could be avoided with the combined use of sliding acquisition during the cardiac cycle and image postprocessing with the PCATMIP algorithm. This study provides new perspectives for perfusion imaging without a contrast agent and demonstrates that IVIM parameters can act as promising tools to further characterize microvascular abnormalities or dysfunction.

Low b-Value Diffusion-Weighted Cardiac Magnetic Resonance Imaging: Initial Results in Humans Using an Optimal Time-Window Imaging Approach

Objectives:Diffusion-weighted imaging (DWI) using low b-values permits imaging of intravoxel incoherent motion in tissues. However, low b-value DWI of the human heart has been considered too challenging because of additional signal loss due to physiological motion, which reduces both signal intensity and the signal-to-noise ratio (SNR). We address these signal loss concerns by analyzing cardiac motion during a heartbeat to determine the time-window during which cardiac bulk motion is minimal. Using this information to optimize the acquisition of DWI data and combining it with a dedicated image processing approach has enabled us to develop a novel low b-value diffusion-weighted cardiac magnetic resonance imaging approach, which significantly reduces intravoxel incoherent motion measurement bias introduced by motion. Materials and Methods:Simulations from displacement encoded motion data sets permitted the delineation of an optimal time-window with minimal cardiac motion. A number of single-shot repetitions of low b-value DWI cardiac magnetic resonance imaging data were acquired during this time-window under free-breathing conditions with bulk physiological motion corrected for by using nonrigid registration. Principal component analysis (PCA) was performed on the registered images to improve the SNR, and temporal maximum intensity projection (TMIP) was applied to recover signal intensity from time-fluctuant motion-induced signal loss. This PCATMIP method was validated with experimental data, and its benefits were evaluated in volunteers before being applied to patients. Results:Optimal time-window cardiac DWI in combination with PCATMIP postprocessing yielded significant benefits for signal recovery, contrast-to-noise ratio, and SNR in the presence of bulk motion for both numerical simulations and human volunteer studies. Analysis of mean apparent diffusion coefficient (ADC) maps showed homogeneous values among volunteers and good reproducibility between free-breathing and breath-hold acquisitions. The PCATMIP DWI approach also indicated its potential utility by detecting ADC variations in acute myocardial infarction patients. Conclusions:Studying cardiac motion may provide an appropriate strategy for minimizing the impact of bulk motion on cardiac DWI. Applying PCATMIP image processing improves low b-value DWI and enables reliable analysis of ADC in the myocardium. The use of a limited number of repetitions in a free-breathing mode also enables easier application in clinical conditions.

Assessment of Cardiac Motion Effects on the Fiber Architecture of the Human Heart In Vivo

The use of diffusion tensor imaging (DTI) for studying the human heart in vivo is very challenging due to cardiac motion. This paper assesses the effects of cardiac motion on the human myocardial fiber architecture. To this end, a model for analyzing the effects of cardiac motion on signal intensity is presented. A Monte-Carlo simulation based on polarized light imaging data is then performed to calculate the diffusion signals obtained by the displacement of water molecules, which generate diffusion weighted (DW) images. Rician noise and in vivo motion data obtained from DENSE acquisition are added to the simulated cardiac DW images to produce motion-induced datasets. An algorithm based on principal components analysis filtering and temporal maximum intensity projection (PCATMIP) is used to compensate for motion-induced signal loss. Diffusion tensor parameters derived from motion-reduced DW images are compared to those derived from the original simulated DW images. Finally, to assess cardiac motion effects on in vivo fiber architecture, in vivo cardiac DTI data processed by PCATMIP are compared to those obtained from one trigger delay (TD) or one single phase acquisition. The results showed that cardiac motion produced overestimated fractional anisotropy and mean diffusivity as well as a narrower range of fiber angles. The combined use of shifted TD acquisitions and postprocessing based on image registration and PCATMIP effectively improved the quality of in vivo DW images and subsequently, the measurement accuracy of fiber architecture properties. This suggests new solutions to the problems associated with obtaining in vivo human myocardial fiber architecture properties in clinical conditions.

Coronary artery wall imaging in mice using osmium tetroxide and micro-computed tomography (micro-CT)

The high spatial resolution of micro‐computed tomography (micro‐CT) is ideal for 3D imaging of coronary arteries in intact mouse heart specimens. Previously, micro‐CT of mouse heart specimens utilized intravascular contrast agents that hardened within the vessel lumen and allowed a vascular cast to be made. However, for mouse coronary artery disease models, it is highly desirable to image coronary artery walls and highlight plaques. For this purpose, we describe an ex vivo contrast‐enhanced micro‐CT imaging technique based on tissue staining with osmium tetroxide (OsO4) solution. As a tissue‐staining contrast agent, OsO4 is retained in the vessel wall and surrounding tissue during the fixation process and cleared from the vessel lumens. Its high X‐ray attenuation makes the artery wall visible in CT. Additionally, since OsO4 preferentially binds to lipids, it highlights lipid deposition in the artery wall. We performed micro‐CT of heart specimens of 5‐ to 25‐week‐old C57BL/6 wild‐type mice and 5‐ to 13‐week‐old apolipoprotein E knockout (apoE−/−) mice at 10 μm resolution. The results show that walls of coronary arteries as small as 45 μm in diameter are visible using a table‐top micro‐CT scanner. Similar image clarity was achieved with 1/2000th the scan time using a synchrotron CT scanner. In 13‐week‐old apoE mice, lipid‐rich plaques are visible in the aorta. Our study shows that the combination of OsO4 and micro‐CT permits the visualization of the coronary artery wall in intact mouse hearts.

PCATMIP: Enhancing Signal Intensity in Diffusion- Weighted Magnetic Resonance Imaging

Diffusion‐weighted MRI studies generally lose signal intensity to physiological motion, which can adversely affect quantification/diagnosis. Averaging over multiple repetitions, often used to improve image quality, does not eliminate the signal loss. In this article, PCATMIP, a combined principal component analysis and temporal maximum intensity projection approach, is developed to address this problem. Data are first acquired for a fixed number of repetitions. Assuming that physiological fluctuations of image intensities locally are likely temporally correlated unlike random noise, a local moving boxcar in the spatial domain is used to reconstruct low‐noise images by considering the most relevant principal components in the temporal domain. Subsequently, a temporal maximum intensity projection yields a high signal‐intensity image. Numerical and experimental studies were performed for validation and to determine optimal parameters for increasing signal intensity and minimizing noise. Subsequently, a combined principal component analysis and temporal maximum intensity projection approach was used to analyze diffusion‐weighted porcine liver MRI scans. In these scans, the variability of apparent diffusion coefficient values among repeated measurements was reduced by 59% relative to averaging, and there was an increase in the signal intensity with higher intensity differences observed at higher b‐values. In summary, a combined principal component analysis and temporal maximum intensity projection approach is a postprocessing approach that corrects for bulk motion‐induced signal loss and improves apparent diffusion coefficient measurement reproducibility. Magn Reson Med, 2011.

Does binding of Gd-DTPA to myocardial tissue contribute to late enhancement in a model of acute myocardial infarction?†

The long‐lasting signal enhancement by Gd‐DTPA in areas of myocardial infarction has been conventionally explained by low perfusion and an enhanced Gd distribution volume. To test whether binding of Gd to myocardial constituents is an additional factor contributing to this effect, Gd‐DTPA was allowed to equilibrate between homogenized porcine myocardial tissue and physiological saline. The relaxation rate (1/T1) of homogenate samples (n = 61) increased in proportion (r2 = 0.98) to the Gd concentration (0.025–0.5 mM) of the surrounding medium, with no evidence for augmented uptake. The diffusion‐limited uptake was only slightly more rapid than the subsequent Gd‐release. The amount of Gd released was in line with all of the Gd‐DTPA in the homogenate participating in water proton relaxation. The data from this acute myocardial infarction model do not support the notion that Gd‐DTPA binding in the early stages of myocardial damage contributes to delayed enhancement. Magn Reson Med 49:168–171, 2003. Published 2003 Wiley‐Liss, Inc.

The National Institutes of Health Affordable Cancer Technologies Program: Improving Access to Resource-Appropriate Technologies for Cancer Detection, Diagnosis, Monitoring, and Treatment in Low- and Middle-Income Countries

Point-of-care (POC) technologies have proved valuable in cancer detection, diagnosis, monitoring, and treatment in the developed world, and have shown promise in low-and-middle-income countries (LMIC) as well. Despite this promise, the unique design constraints presented in low-resource settings, coupled with the variety of country-specific regulatory and institutional dynamics, have made it difficult for investigators to translate successful POC cancer interventions to the LMIC markets. In response to this need, the National Cancer Institute has partnered with the National Institute of Biomedical Imaging and Bioengineering to create the National Institutes of Health Affordable Cancer Technologies (ACTs) program. This program seeks to simplify the pathway to market by funding multidisciplinary investigative teams to adapt and validate the existing technologies for cancer detection, diagnosis, and treatment in LMIC settings. The various projects under ACTs range from microfluidic cancer diagnostic tools to novel treatment devices, each geared for successful clinical adaptation to LMIC settings. Via progression through this program, each POC innovation will be uniquely leveraged for successful clinical translation to LMICs in a way not before seen in this arena.Point-of-care (POC) technologies have proved valuable in cancer detection, diagnosis, monitoring, and treatment in the developed world, and have shown promise in low-and-middle-income countries (LMIC) as well. Despite this promise, the unique design constraints presented in low-resource settings, coupled with the variety of country-specific regulatory and institutional dynamics, have made it difficult for investigators to translate successful POC cancer interventions to the LMIC markets. In response to this need, the National Cancer Institute has partnered with the National Institute of Biomedical Imaging and Bioengineering to create the National Institutes of Health Affordable Cancer Technologies (ACTs) program. This program seeks to simplify the pathway to market by funding multidisciplinary investigative teams to adapt and validate the existing technologies for cancer detection, diagnosis, and treatment in LMIC settings. The various projects under ACTs range from microfluidic cancer diagnostic tools to novel treatment devices, each geared for successful clinical adaptation to LMIC settings. Via progression through this program, each POC innovation will be uniquely leveraged for successful clinical translation to LMICs in a way not before seen in this arena.