Dual-tracer background subtraction approach for fluorescent molecular tomography

Diffuse fluorescence tomography requires high contrast-to-background ratios to accurately reconstruct inclusions of interest. This is a problem when imaging the uptake of fluorescently labeled molecularly targeted tracers in tissue, which can result in high levels of heterogeneously distributed background uptake. We present a dual-tracer background subtraction approach, wherein signal from the uptake of an untargeted tracer is subtracted from targeted tracer signal prior to image reconstruction, resulting in maps of targeted tracer binding. The approach is demonstrated in simulations, a phantom study, and in a mouse glioma imaging study, demonstrating substantial improvement over conventional and homogenous background subtraction image reconstruction approaches

Dual role of cerebral blood flow in regional brain temperature control in the healthy newborn infant.

Small shifts in brain temperature after hypoxia-ischaemia affect cell viability. The main determinants of brain temperature are cerebral metabolism, which contributes to local heat production, and brain perfusion, which removes heat. However, few studies have addressed the effect of cerebral metabolism and perfusion on regional brain temperature in human neonates because of the lack of non-invasive cot-side monitors. This study aimed (i) to determine non-invasive monitoring tools of cerebral metabolism and perfusion by combining near-infrared spectroscopy and echocardiography, and (ii) to investigate the dependence of brain temperature on cerebral metabolism and perfusion in unsedated newborn infants. Thirty-two healthy newborn infants were recruited. They were studied with cerebral near-infrared spectroscopy, echocardiography, and a zero-heat flux tissue thermometer. A surrogate of cerebral blood flow (CBF) was measured using superior vena cava flow adjusted for cerebral volume (rSVC flow). The tissue oxygenation index, fractional oxygen extraction (FOE), and the cerebral metabolic rate of oxygen relative to rSVC flow (CMRO2 index) were also estimated. A greater rSVC flow was positively associated with higher brain temperatures, particularly for superficial structures. The CMRO2 index and rSVC flow were positively coupled. However, brain temperature was independent of FOE and the CMRO2 index. A cooler ambient temperature was associated with a greater temperature gradient between the scalp surface and the body core. Cerebral oxygen metabolism and perfusion were monitored in newborn infants without using tracers. In these healthy newborn infants, cerebral perfusion and ambient temperature were significant independent variables of brain temperature. CBF has primarily been associated with heat removal from the brain. However, our results suggest that CBF is likely to deliver heat specifically to the superficial brain. Further studies are required to assess the effect of cerebral metabolism and perfusion on regional brain temperature in low-cardiac output conditions, fever, and with therapeutic hypothermia

Generalized paired-agent kinetic model for in vivo quantification of cancer cell-surface receptors under receptor saturation conditions

New precision medicine drugs oftentimes act through binding to specific cellsurface cancer receptors, and thus their efficacy is highly dependent on the availability of those receptors and the receptor concentration per cell. Pairedagent molecular imaging can provide quantitative information on receptor status in vivo, especially in tumor tissue; however, to date, published approaches to paired-agent quantitative imaging require that only ‘trace’ levels of imaging agent exist compared to receptor concentration. This strict requirement may limit applicability, particularly in drug binding studies, which seek to report on a biological effect in response to saturating receptors with a drug moiety. To extend the regime over which paired-agent imaging may be used, this work presents a generalized simplified reference tissue model (GSRTM) for pairedagent imaging developed to approximate receptor concentration in both nonreceptor-saturated and receptor-saturated conditions. Extensive simulation studies show that tumor receptor concentration estimates recovered using the GSRTM are more accurate in receptor-saturation conditions than the standard simple reference tissue model (SRTM) (% error (mean ± sd): GSRTM 0 ± 1 and SRTM 50 ± 1) and match the SRTM accuracy in non-saturated conditions (% error (mean ± sd): GSRTM 5 ± 5 and SRTM 0 ± 5). To further test the approach, GSRTM-estimated receptor concentration was compared to SRTMestimated values extracted from tumor xenograft in vivo mouse model data. The GSRTM estimates were observed to deviate from the SRTM in tumors with low receptor saturation (which are likely in a saturated regime). Finally, a general ‘rule-of-thumb’ algorithm is presented to estimate the expected level of receptor saturation that would be achieved in a given tissue provided dose N Sadeghipour et al Printed in the UK 394 PHMBA7 © 2016 Institute of Physics and Engineering in Medicine 62 Phys. Med. Biol. PMB 10.1088/1361-6560/62/2/394 Paper 2 394 414 Physics in Medicine & Biology Institute of Physics and Engineering in Medicine IOP 2017 1361-6560 1361-6560/17/020394+21$33.00 © 2016 Institute of Physics and Engineering in Medicine Printed in the UK Phys. Med. Biol. 62 (2017) 394–414 doi:10.1088/1361-6560/62/2/394 395 and pharmacokinetic information about the drug or imaging agent being used, and physiological information about the tissue. These studies suggest that the GSRTM is necessary when receptor saturation exceeds 20% and highlight the potential for GSRTM to accurately measure receptor concentrations under saturation conditions, such as might be required during high dose drug studies, or for imaging applications where high concentrations of imaging agent are required to optimize signal-to-noise conditions. This model can also be applied to PET and SPECT imaging studies that tend to suffer from noisier data, but require one less parameter to fit if images are converted to imaging agent concentration (quantitative PET/SPECT)

Chiral symmetry and scale invariance breaking in spin chains

The effects of the Dzyaloshinsky-Moriya interaction on finite size one-dimensional (1D) magnetic chains are investigated as a function of their length. The magnetic configuration the system adopts for varying boundary conditions are explored analytically, which leads to the appearance of chiral configurations that play a crucial role. The coercive and exchange bias fields show an unexpected chain length dependence, caused by the boundary conditions and by chiral symmetry breaking, which in turn leads to the breakdown of scale-invariance. Our treatment yields results in agreement with experimental evidence and ongoing research on phthalocyanine iron chains bonded to hydrogen.United States Department of Energy (DOE): DE FG02 87ER-45332 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT), CONICYT FONDECYT: 1160639 CEDENNA through the Financiamiento Basal para Centros Cienticos y Tecnologicos de Excelencia: FB0807 U.S. Department of Energy AFOSR Office of Basic Energy Science FA9550-16-1-0122 FA9550-18-1-043

Multiple-gate time domain diffuse fluorescence tomography allows more sparse tissue sampling without compromising image quality

Diffuse fluorescence tomography systems that employ highly sensitive photo-multiplier tubes for single-photon detection are pushing the sensitivity limits of the field. However, each of these detectors only offers a single data projection to be collected, implying these imaging systems either require many detectors or long scan times to collect full data sets for image reconstruction. This study presents a method of utilizing the time-resolved collection capabilities of time-correlated single-photon counting techniques to increase spatial resolution and to reduce the number of data projections to produce reliable fluorescence reconstructions. Experimental tissue phantom results demonstrate that using data at 10 time gates in the fluorescence reconstructions for only 40 data projections provided superior image accuracy when compared to reconstructions on 320 continuous-wave data projections

Information loss and reconstruction in diffuse fluorescence tomography

This paper is a theoretical exploration of spatial resolution in diffuse fluorescence tomography. It is demonstrated that, given a fixed imaging geometry, one cannot—relative to standard techniques such as Tikhonov regularization and truncated singular value decomposition—improve the spatial resolution of the optical reconstructions via increasing the node density of the mesh considered for modeling light transport. Using techniques from linear algebra, it is shown that, as one increases the number of nodes beyond the number of measurements, information is lost by the forward model. It is demonstrated that this information cannot be recovered using various common reconstruction techniques. Evidence is provided showing that this phenomenon is related to the smoothing properties of the elliptic forward model that is used in the diffusion approximation to light transport in tissue. This argues for reconstruction techniques that are sensitive to boundaries, such as L(1)-reconstruction and the use of priors, as well as the natural approach of building a measurement geometry that reflects the desired image resolution

Compressive sensing based reconstruction in bioluminescence tomography improves image resolution and robustness to noise

Bioluminescence Tomography attempts to quantify 3-dimensional luminophore distributions from surface measurements of the light distribution. The reconstruction problem is typically severely under-determined due to the number and location of measurements, but in certain cases the molecules or cells of interest form localised clusters, resulting in a distribution of luminophores that is spatially sparse. A Conjugate Gradient-based reconstruction algorithm using Compressive Sensing was designed to take advantage of this sparsity, using a multistage sparsity reduction approach to remove the need to choose sparsity weighting a priori. Numerical simulations were used to examine the effect of noise on reconstruction accuracy. Tomographic bioluminescence measurements of a Caliper XPM-2 Phantom Mouse were acquired and reconstructions from simulation and this experimental data show that Compressive Sensing-based reconstruction is superior to standard reconstruction techniques, particularly in the presence of noise