The XTRA Option at the NEUTRA Facility—More Than 10 Years of Bi-Modal Neutron and X-ray Imaging at PSI

Lehmann, Eberhard; Mannes, David; Kaestner, Anders; Hovind, Jan; Trtik, Pavel; Ströbl, Markus

doi:10.3390/app11093825

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…NEUTRA 2.0 will retain the capability of the XTRA option for in-line bimodal neutron and X-ray imaging [13]. It is currently foreseen that the available X-ray source (GE ISOVOLT-Titan 320) will be transferred to NEUTRA 2.0, but will require longer cables between the source itself and its power supply.…”

Section: Retained Existing Instrumentationmentioning

confidence: 99%

NEUTRA 2.0 – Upgrade of a pioneering instrument

Trtik,

Filges,

Bollhalder

et al. 2023

J. Phys.: Conf. Ser.

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After more than 25 years of successful operation of the thermal neutron imaging instrument NEUTRA, the NEUTRA 2.0 upgrade project has been approved. This upgrade implies a complete reconstruction of the instrument including a redesign of the shielding bunker. The inner usable area of the bunker will be increased thus creating ample space required in modern neutron imaging instruments for complex set-ups with bulk sample environments. Likewise, full access to an upstream measuring position, MP1, of the beamline will allow utilizing about half an order and one order of magnitude higher flux than at the currently accessible measurement positions MP2 and MP3, respectively. These measures will enable higher temporal resolution neutron imaging investigations and the use of the Neutron Microscope detector at NEUTRA. Together with more available space the higher flexibility of the interior arrangement will enable accommodating components for advanced neutron imaging techniques such as in-situ simultaneous bimodal neutron/x-ray imaging, time-of-flight imaging and thermal neutron grating interferometry. While the upgrade will enable advanced neutron imaging capabilities at NEUTRA 2.0, the instrumentation and techniques that were pioneered at NEUTRA in the past, like the XTRA option for in-line bimodal neutron/X-ray imaging and the NEURAP insert for neutron imaging of highly radioactive samples will be retained in the suite of the available modalities at the beamline.

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Section: Retained Existing Instrumentationmentioning

confidence: 99%

NEUTRA 2.0 – Upgrade of a pioneering instrument

Trtik,

Filges,

Bollhalder

et al. 2023

J. Phys.: Conf. Ser.

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“…Bi-modal neutron and x-ray imaging may be an interesting option for this problem. Using a frozen initial state, x-ray tomography could be performed to obtain the 3D snow structure, followed by neutron imaging to capture the faster water transport dynamics (Lehmann and others, 2021).…”

Section: Experimental Challengesmentioning

confidence: 99%

A method for imaging water transport in soil–snow systems with neutron radiography

Lombardo,

Lehmann,

Kaestner

et al. 2023

Ann. Glaciol.

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Liquid water at the ground–snow interface is thought to play a crucial role in the release of glide-snow avalanches, which can be massive and threaten infrastructure in alpine regions. Several mechanisms have been postulated to explain the formation of this interfacial water. However, these mechanisms remain poorly understood, in part because suitable measurement techniques are lacking. Here, we demonstrate the use of neutron radiography for imaging water transport in soil–snow systems. Columns of sand, gravel and snow were used to simulate the capillary forces of the soil–vegetation–snow layering found in nature. The columns were connected to a water reservoir to maintain a constant-pressure boundary condition and placed in a climatic chamber within the neutron beam. We show that neutron radiography is capable of measuring changes in the optical density distribution (related to liquid water content) within all three layers of the model system. Results suggest that a porous interface between the sand and snow may induce the formation of a water layer in the basal snowpack. Improved understanding of the water transport in soil–snow systems should lead to better prediction of glide-snow avalanche release and could also benefit other fields such as snow hydrology.

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“…Neutron radiography (nR) utilizes this contrast to measure a plethora of objects ranging from materials science, energy materials to plant physiology, engineering, archeology and biology, to name a few 1 , 2 . These measurements are often performed at research reactor facilities 3 – 8 . Recently, with the advances of accelerator-based neutron sources and detector technology 9 , wavelength-dependent or hyperspectral neutron radiography (HSnR) and computed tomography (HSnCT) have grown as essential non-destructive techniques that complement reactor-based neutron imaging capabilities 2 , 10 – 13 .…”

Section: Introductionmentioning

confidence: 99%

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

Tang,

Venkatakrishnan,

Chowdhury

et al. 2024

Sci Rep

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We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

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The XTRA Option at the NEUTRA Facility—More Than 10 Years of Bi-Modal Neutron and X-ray Imaging at PSI

Cited by 13 publications

References 18 publications

NEUTRA 2.0 – Upgrade of a pioneering instrument

NEUTRA 2.0 – Upgrade of a pioneering instrument

A method for imaging water transport in soil–snow systems with neutron radiography

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

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