Neutron grating interferometry provides information on phase and small-angle scatter in addition to attenuation. Previously, phase grating moiré interferometers (PGMI) with two or three phase gratings have been developed. These phase-grating systems use the moiré far-field technique to avoid the need for high-aspect absorption gratings used in Talbot–Lau interferometers (TLI) that reduce the neutron flux reaching the detector. We first demonstrate, through theory and simulations, a novel phase grating interferometer system for cold neutrons that requires a single modulated phase grating (MPG) for phase-contrast imaging, as opposed to the two or three phase gratings in previously employed PGMI systems. The theory shows the dual modulation of MPG with a large period and a smaller carrier pitch P, resulting in large fringes at the detector. The theory was compared to the full Sommerfeld–Rayleigh diffraction integral simulator. Then, we proceeded to compare the MPG system to experiments in the literature that use a two-phase-grating-based PGMI with best-case visibility of around 39%. The simulations of the MPG system show improved visibility in comparison to that of the two-phase-grating-based PGMI. An MPG with a modulation period of 300 µm, the pitch of 2 µm, and grating heights with a phase modulation of ([Formula: see text], illuminated by a monochromatic beam produces visibility of 94.2% with a comparable source-to-detector distance (SDD) as the two-phase-grating-based PGMI. Phase sensitivity, another important performance metric of the grating interferometer, was compared to values available in the literature, viz. the conventional TLI with the phase sensitivity of 4.5 × 103 for an SDD of 3.5 m and a beam wavelength of 0.44 nm. For a range of modulation periods, the MPG system provides comparable or greater theoretical maximum phase sensitivity of 4.1 × 103 to 10.0 × 103 for SDDs of up to 3.5 m. This proposed MPG system appears capable of providing high-performance PGMI that obviates the need for the alignment of two phase gratings.

Background: Over the last decade, several theoretical tumor-models have been developed to describe tumor growth. Oncology imaging is performed using various modalities including computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT) and fluorodeoxyglucose-positron emission tomography (FDG-PET). Our goal is to extract useful, otherwise hidden, quantitative biophysical parameters (such as growth-rate, tumor-necrotic-factor, etc.) from these serial images of tumors by fitting mathematical models to images. These biophysical features are intrinsic to the tumor types and specific to the study-subject, and expected to add valuable information on the tumor containment or spread and help treatment plans. Thus, fitting realistic but practical models and assessing parameter-errors and degree of fit is important. Methods:We implemented an existing theoretical ode-compartment model and variants and applied them for the first time, in vivo. We developed an inversion algorithm to fit the models for tumor growth for simulated as well as in vivo experimental data. Serial SPECT/CT scans of mice breast-tumors were acquired, and SPECT data was used to segment the proliferating-layers of tumors.Results: Results of noisy data simulation and inversion show that 5 out of 7 parameters were recovered to within 4.3% error. In particular, tumor "growth-rate" parameter was recovered to 0.07% error. For model fitting to in vivo mice-tumors, regression analysis on the P-layer volume showed R 2 of 0.99 for logistic andGompertzian while surface area model yielded R weights of the models (giving their relative probability of being the best Kullback-Leibler (K-L) model among the set of candidate models) were ~0, 0.43 and 0.57 for surface-area, logistic and Gompertzian models.Conclusions: Model-fitting to mice tumor studies demonstrates feasibility of applying the models to in vivo imaging data to extract features. Akaike information criterion (AIC) evaluations show Gompertzian or logistic growth model fits in vivo breast-tumors better than surface-area based growth model. IntroductionOncology imaging is performed using various modalities including computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT) and fluorodeoxyglucose-positron emission tomography (FDG-PET).Quantification of cancer from images is important for assessment of disease progress and treatment. Tumors grow in a specific way and theoretical tumor-models of increasing complexity have been developed that may be potentially used to find (otherwise concealed) biophysical information from serial images of tumors. If accurate, tumor growthrate, cell-motility (diffusion), tumor-necrotic-factor, can be consistently and reliably extracted from oncological images, they may add valuable information on the tumor containment/spread and help treatment plan. For example, assessing the growth-rate accurately will help in treatment planning whether in surgery or in determining the numbe...

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