Novel approaches to address spectral distortions in photon counting x-ray CT using artificial neural networks

Touch, M; Clark, Darin P.; Barber, William C.; Badea, Cristian T.

doi:10.1117/12.2217037

Cited by 1 publication

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References 23 publications

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“…Zimmerman and Schmidt 3,19 studied noise but used noisy training data from a single exposure of the calibration phantom. Touch et al 4,20 used a neural network to correct projection data for defects and deadtime of photon counting detectors. They then reconstructed data from individual PHA bins to produce images of the object attenuation at a set of different xray energies instead of the basis set coefficient images produced by the estimator of this paper.…”

Section: Introductionmentioning

confidence: 99%

Near optimal neural network estimator for spectral x-ray photon counting data with pileup

Alvarez¹

2017

Preprint

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A neural network estimator to process x-ray spectral measurements from photon counting detectors with pileup. The estimator is used with an expansion of the attenuation coefficient as a linear combination of functions of energy multiplied by coefficients that depend on the material composition at points within the object [R.E. Alvarez and A. Macovski, Phys. Med. Biol., 1976, 733-744]. The estimator computes the line integrals of the coefficients from measurements with different spectra. Neural network estimators are trained with measurements of a calibration phantom with the clinical x-ray system. One estimator uses low noise training data and another network is trained with data computed by adding random noise to the low noise data. The performance of the estimators is compared to each other and to the Cramer-Rao lower bound (CRLB). Methods: The estimator performance is measured using a Monte Carlo simulation with an idealized model of a photon counting detector that includes only pileup and quantum noise. Transmitted x-ray spectra are computed for a calibration phantom. The transmitted spectra are used to compute random data for photon counting detectors with pileup. Detectors with small and large dead times are considered. Neural network training data with extremely low noise are computed by averaging the random detected data with pileup for a large numbers of exposures of the phantom. Each exposure is equivalent to a projection image or one projection of a computed tomography scan. Training data with high noise are computed by using data from one exposure. Finally, training data are computed by adding random data to the low noise data. The added random data are multivariate normal with zero mean and covariance equal to the sample covariance of data for an object with properly chosen attenuation. To test the estimators, random data are computed for different thicknesses of three test objects with different compositions. These are used as inputs to the neural network estimators. The mean squared errors (MSE), variance and square of the bias of the neural networks' outputs with the random object data are each compared to the CRLB. Results: The MSE for a network trained with low noise data and added noise is close to the CRLB for both the low and high pileup cases. Networks trained with very low noise data have low bias but large variance for both pileup cases.

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Section: Introductionmentioning

confidence: 99%

Near optimal neural network estimator for spectral x-ray photon counting data with pileup

Alvarez¹

2017

Preprint

View full text Add to dashboard Cite

show abstract

Novel approaches to address spectral distortions in photon counting x-ray CT using artificial neural networks

Cited by 1 publication

References 23 publications

Near optimal neural network estimator for spectral x-ray photon counting data with pileup

Near optimal neural network estimator for spectral x-ray photon counting data with pileup

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