We propose a method for improving the quantification of neutron imaging measurements with scintillator-camera based detectors by correcting for systematic biases introduced by scattered neutrons and other sources such as light reflections in the detector system. This method is fully experimental, using reference measurements with a grid of small black bodies (BB) to measure the bias contributions directly. Using two test samples, one made of lead alloy and having a moderate (20%) neutron transmission and one made of stainless-steel and having a very low (1%) transmission, we evaluated the improvement brought by this method in reducing both the average quantification bias and the uncertainty around this average bias after tomographic reconstruction. The results show that a reduction of the quantification bias of up to one order of magnitude can be obtained. For moderately transparent samples, little sensitivity is observed to the parameters used for the correction. For the more challenging sample with very low transmission, a correct placement of the BB grid is of utmost importance for a successful correction.

We describe in this paper the experimental procedure, the data treatment and the quantification of the black body correction: an experimental approach to compensate for scattering and systematic biases in quantitative neutron imaging based on experimental data. The correction algorithm is based on two steps; estimation of the scattering component and correction using an enhanced normalization formula. The method incorporates correction terms into the image normalization procedure, which usually only includes open beam and dark current images (open beam correction). Our aim is to show its efficiency and reproducibility: we detail the data treatment procedures and quantitatively investigate the effect of the correction. Its implementation is included within the open source CT reconstruction software MuhRec. The performance of the proposed algorithm is demonstrated using simulated and experimental CT datasets acquired at the ICON and NEUTRA beamlines at the Paul Scherrer Institut.

Purpose: The aim of this study was to compare the performance of quantitative methods, either semiautomated or automated, for left ventricular (LV) nonviable tissue analysis from cardiac magnetic resonance late gadolinium enhancement (CMR-LGE) images. Materials and Methods: The investigated segmentation techniques were: (i) n-standard deviations thresholding; (ii) full width at half maximum thresholding; (iii) Gaussian mixture model classification; and (iv) fuzzy c-means clustering. These algorithms were applied either in each short axis slice (single-slice approach) or globally considering the entire short-axis stack covering the LV (global approach). CMR-LGE images from 20 patients with ischemic cardiomyopathy were retrospectively selected, and results from each technique were assessed against manual tracing. Results: All methods provided comparable performance in terms of accuracy in scar detection, computation of local transmurality, and high correlation in scar mass compared with the manual technique. In general, no significant difference between single-slice and global approach was noted. The reproducibility of manual and investigated techniques was confirmed in all cases with slightly lower results for the nSD approach. Conclusions: Automated techniques resulted in accurate and reproducible evaluation of LV scars from CMR-LGE in ischemic patients with performance similar to the manual technique. Their application could minimize user interaction and computational time, even when compared with semiautomated approaches.

Structural properties of cohesive powders are dominated by their microstructural composition. Powders with a fractal microstructure show particularly interesting properties during compaction where a microstructural transition and a fractal breakdown happen before compaction and force transport. The study of this phenomenon has been challenging due to its long-range effect and the subsequent necessity to characterize these microstructural changes on a macroscopic scale. For the detailed investigation of the complex nature of powder compaction for various densification states along with the heterogeneous breakdown of the fractal microstructure we applied neutron dark-field imaging in combination with a variety of supporting techniques with various spatial resolutions, field-of-views and information depths. We used scanning electron microscopy to image the surface microstructure in a small field-of-view and X-ray tomography to image density variations in 3D with lower spatial resolution. Non-local spin-echo small-angle neutron scattering results are used to evaluate fitting models later used as input parameters for the neutron dark-field imaging data analysis. Finally, neutron dark-field imaging results in combination with supporting measurements using scanning electron microscopy, X-ray tomography and spin-echo small angle scattering allowed us to comprehensively study the heterogeneous transition from a fractal to a homogeneous microstructure of a cohesive powder in a quantitative manner.