Using measurements of the spatial SNR to optimize phase contrast X-ray imaging

Ullherr, Maximilian; Balles, Andreas; Fella, Christian; Zabler, Simon

doi:10.1016/j.nima.2017.09.044

Cited by 6 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we calculate the standard SNR which is used in the very large majority of studies and comparable to the literature. Other quality measures such as the recently proposed spatial SNR could be investigated to evaluate the quality of X-ray images [31]. The phase retrieval and tomographic reconstruction were performed sequentially.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue

Yu¹,

Weber²,

Pacureanu³

et al. 2018

Opt. Express

View full text Add to dashboard Cite

, et al.. Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue. Optics Express, Optical Society of America, 2018, 26 (9) Abstract: X-ray phase contrast imaging offers higher sensitivity compared to conventional X-ray attenuation imaging and can be simply implemented by propagation when using a partially coherent synchrotron beam. We address the phase retrieval in in-line phase nano-CT using multiple propagation distances. We derive a method which extends Paganin's single distance method and compare it to the contrast transfer function (CTF) approach in the case of a homogeneous object. The methods are applied to phase nano-CT data acquired at the voxel size of 30 nm (ID16A, ESRF, Grenoble, France). Our results show a gain in image quality in terms of the signal-to-noise ratio and spatial resolution when using four distances instead of one. The extended Paganin's method followed by an iterative refinement step provides the best reconstructions while the homogeneous CTF method delivers quasi comparable results for our data, even without refinement step. Dreossi, E. Brun, P. Coan, A. Bravin, P. De Coppi, and A. Olivo, "High contrast microstructural visualization of natural acellular matrices by means of phase-based x-ray tomography," Sci. Rep. 5(1), 18156 (2015). 10. P. Cloetens, M. Pateyron-Salomé, J. Y. Buffière, G. Peix, J. Baruchel, F. Peyrin, and M. Schlenker, "Observation of microstructure and damage in materials by phase sensitive radiography and tomography," J.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue

Yu¹,

Weber²,

Pacureanu³

et al. 2018

Opt. Express

View full text Add to dashboard Cite

show abstract

“…While optimizing either MTF (modulation transfer function) or temporal SNR (signal-to-noise ratio) generally does not yield the best image quality, we recently proposed to use SNR spectra as a quality measure which includes modulation transfer and noise effects both with respect to an object at hand. SNR(u) can be measured eas-ily from a series of transmission images, and we demonstrated its use by finding the optimum magnification for phase contrast subµ imaging in the LMJ-CT setup [12].…”

Section: Snr Spectra and Transfer Functionsmentioning

confidence: 99%

“…Except for three scans (RX Easytom) all volume reconstructions were done with our free python X-ray imaging toolkit (pyXIT). pyXIT also allows for image segmentation, Paganin-type phase retrieval and Wienerdeconvolution [18,12]. Following volume reconstruction, all those steps were applied simultaneously to each data set.…”

Section: Volume Image Processingmentioning

confidence: 99%

Comparing image quality in phase contrast subμ X-ray tomography—A round-robin study

Zabler

Ullherr

Fella³

et al. 2020

Nuclear Instruments and Methods in Physics Research Section A:

Self Cite

View full text Add to dashboard Cite

How to evaluate and compare image quality from different sub-micrometer (subµ) CT scans? A simple yet clever test phantom is used for recording 13 scans in a number of commercial and some non-commercial scanners. From the resulting volume images, signal and noise power spectra are modeled for estimating spatial signalto-noise ratio (SNR spectrum). Using the same data, a time-and object-independent transfer function (MTF) is computed for each scan, including phase contrast strength and spatial resolution (MTF blur ). SNR and MTF are compared to transmission measurements of the same test phantom. Making SNR object-independent by normalization with respect to the object spectrum yields detection effectiveness (DE) a new measure which reveals how technical differences as well as operator-choices strongly influence scan quality for a given measurement time. Using DE both source-based and detector-based subµ CT scanners can be studied and optimized with respect to signal detection effectiveness using the metrics which are presented in this study. Future application of

show abstract

“…An image quality optimization based on SNR spectra or temporal SNR makes predictions which can be tested directly. This is done with a direct measurement, see A or [14] for a method. A SNR optimization should ideally be based on measurements for the specific use case.…”

Section: Testable Predictionsmentioning

confidence: 99%

“…An important aspect of the SNR Spectra is that they can directly be measured. The method described in [14] can be used for this purpose-also in applications other than phase contrast. In the following, we will give a short description of how to apply this method.…”

Section: A Measuring Snr Spectramentioning

confidence: 99%

SNR spectra as a quantitative model for image quality in polychromatic X-ray imaging

Ullherr

Zabler

2019

Nuclear Instruments and Methods in Physics Research Section A:

Self Cite

View full text Add to dashboard Cite

In polychromatic x-ray imaging for nondestructive testing, material science or medical applications, image quality is usually a problem of detecting sample structure in noisy data. This problem is typically stated this way: As many photons as possible need to be detected to get a good image quality.We instead propose to use the concept of signal detection, which is more universal. In signal detection, it is the sample properties which are detected. Photons play the role of information carriers for the signal. Signal detection for example allows modeling the effects which polychromaticity has on image quality.SNR spectra (= spatial SNR) are used as a quantity to describe if reliable signal detection is possible. They include modulation transfer and phase contrast in addition to noisiness effects. SNR spectra can also be directly measured, which means that theoretical predictions can easily be tested.We investigate the effects of signal and noise superposition on the SNR spectrum and show how selectively not detecting photons can increase the image quality.

show abstract

Using measurements of the spatial SNR to optimize phase contrast X-ray imaging

Cited by 6 publications

References 19 publications

Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue

Evaluation of phase retrieval approaches in magnified X-ray phase nano computerized tomography applied to bone tissue

Comparing image quality in phase contrast subμ X-ray tomography—A round-robin study

SNR spectra as a quantitative model for image quality in polychromatic X-ray imaging

Contact Info

Product

Resources

About