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Hyperspectral Neutron CT with Material Decomposition

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Thilo Balke (1, 2), Alexander M. Long (2), Sven C. Vogel (2), Brendt Wohlberg (2), Charles A. Bouman (1) ((1) Purdue University, (2) Los Alamos National Laboratory)

Image and Video Processing · Submitted 6 Oct 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.

how it is stated · asserted 0.92 read the record →

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arXiv ID2110.02438 (version 1)
Pith IDVX45V4OY, Pith's permanent record of this paper and its review
Citation2), (2) Los Alamos National Laboratory), Alexander M. Long (2), Brendt Wohlberg (2), Charles A. Bouman (1) ((1) Purdue University, Sven C. Vogel (2), Thilo Balke (1. "Hyperspectral Neutron CT with Material Decomposition." arXiv:2110.02438 (2021). https://pith.science/paper/VX45V4OY
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,559 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.83 · human participants 0.02 · controlled comparison 0.08 · randomized assignment 0.01 · states quantitative result 0.66 · compares with prior results 0.81 · mentions code or data availability 0.04.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. The extremely high noise due to low count measurements is overcome using a sparse coding approach. It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. background 1.00 0.07 0.05 asserted 0.97
s1 Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. background 0.67 0.11 0.02 asserted 0.90
s2 In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. background 0.72 0.15 0.07 asserted 0.99
s3 By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. background 0.48 0.19 0.13 hedged possibility 0.89
s4 We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. method 0.72 0.90 0.07 asserted 1.00
s5 The extremely high noise due to low count measurements is overcome using a sparse coding approach. method 0.94 0.75 0.08 asserted 1.00
s6 It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application. result 0.58 0.84 0.42 asserted 0.92

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.91.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material. Energy-dependent radiography image sequences can be created by utilizing neutron time-of-flight techniques. In combination with uniquely characteristic isotopic neutron cross-section spectra, isotopic areal densities can be determined on a per-pixel basis, thus resulting in a set of areal density images for each isotope present in the sample. By preforming ERNI measurements over several rotational views, an isotope decomposed 3D computed tomography is possible. We demonstrate a method involving a robust and automated background estimation based on a linear programming formulation. The extremely high noise due to low count measurements is overcome using a sparse coding approach. It allows for a significant computation time improvement, from weeks to a few hours compared to existing neutron evaluation tools, enabling at the present stage a semi-quantitative, user-friendly routine application.

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