Related Experiment Video
Updated: Oct 24, 2025

10:00
Energy Dispersive X-ray Tomography for 3D Elemental Mapping of Individual Nanoparticles
Published on: July 5, 2016
12.0K
Invertibility of multi-energy X-ray transform.
Yijun Ding1, Eric W Clarkson2, Amit Ashok3
1Wyant College of Optical Sciences, University of Arizona, Tucson, Arizona, USA.
Medical Physics
|August 14, 2021
Summary
We established a condition for multi-energy X-ray transform invertibility, crucial for accurate imaging. This work proves global invertibility for systems using specific detectors and non-K-edge materials.
Area of Science:
- Medical Imaging
- Applied Mathematics
- Physics
Background:
- Multi-energy (ME) X-ray imaging offers enhanced material differentiation compared to conventional methods.
- The Alvarez-Macovski (AM) method represents energy-dependent X-ray attenuation profiles using coefficients.
- Understanding the invertibility of the ME X-ray transform is critical for accurate image reconstruction.
Purpose of the Study:
- To derive a sufficient condition for the invertibility of the multi-energy (ME) X-ray transform.
- To establish the equivalence between global and local invertibility for ME X-ray transforms.
- To analyze the factors influencing the invertibility of the ME X-ray transform.
Main Methods:
- Applied a general invertibility theorem to analyze the Jacobian of the ME X-ray transform mapping.
- Simplified the Jacobian integrand into three factors: total attenuation, basis functions, and energy-weighting functions.
- Utilized the Cramér-Rao lower bound (CRLB) for noise analysis and developed a maximum-likelihood (ML) estimator.
Main Results:
- The basis function factor is consistently negative for standard basis functions (photoelectric/Compton/Rayleigh) without K-edge materials.
- The energy-weighting factor's sign depends on source spectra and detector response; it remains constant for four specific detector types.
- Global invertibility of the ME X-ray transform is demonstrated for non-K-edge materials with these four detector types.
- The proposed ML estimator is unbiased and efficient, suitable for diverse imaging scenarios.
Conclusions:
- A framework for studying the invertibility of arbitrary ME X-ray transforms has been established.
- Global invertibility is proven for ME X-ray imaging systems employing four specific detector types for non-K-edge materials.
- The developed framework is adaptable for analyzing various ME X-ray imaging systems, including those with K-edge materials.
Related Concept Videos
X-ray Imaging
9.0K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
9.0K
Parseval's Theorem for Fourier transform
1.5K
Parseval's theorem is a fundamental principle in signal processing that enables the calculation of a signal's energy in either the time domain or the frequency domain. This theorem is pivotal in demonstrating energy conservation between these two domains, ensuring that the computed energy value remains consistent regardless of the domain of analysis.
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
To understand Parseval's theorem, it is essential to first comprehend how signal energy is typically calculated. When considering a...
1.5K
Continuous -time Fourier Transform
517
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
517
Properties of Fourier Transform II
405
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
405
Discrete-time Fourier transform
640
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
One of the notable...
640
Properties of the z-Transform II
224
The property of Accumulation in signal processing is derived by analyzing the accumulated sum of a discrete-time signal and using the time-shifting property to determine its z-transform. This principle reveals that the z-transform of the summed signal is related to the z-transform of the original signal by a multiplicative factor.
Moreover, the convolution property indicates that the convolution of two signals in the time domain corresponds to the product of their z-transforms in the frequency...
Moreover, the convolution property indicates that the convolution of two signals in the time domain corresponds to the product of their z-transforms in the frequency...
224

