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Journal of Medical Imaging

Segmented targeted least squares estimator for material decomposition in multibin photon-counting detectors
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Paper Abstract

We present a fast, noise-efficient, and accurate estimator for material separation using photon-counting x-ray detectors (PCXDs) with multiple energy bin capability. The proposed targeted least squares estimator (TLSE) is an improvement of a previously described A-table method by incorporating dynamic weighting that allows the variance to be closer to the Cramér–Rao lower bound (CRLB) throughout the operating range. We explore Cartesian and average-energy segmentation of the basis material space for TLSE and show that, compared with Cartesian segmentation, the average-energy method requires fewer segments to achieve similar performance. We compare the average-energy TLSE to other proposed estimators—including the gold standard maximum likelihood estimator (MLE) and the A-table—in terms of variance, bias, and computational efficiency. The variance and bias were simulated in the range of 0 to 6 cm of aluminum and 0 to 50 cm of water with Monte Carlo methods. The Average-energy TLSE achieves an average variance within 2% of the CRLB and mean absolute error of 3.68 ± 0.06 × 10 6    cm . Using the same protocol, the MLE showed variance within 1.9% of the CRLB ratio and average absolute error of 3.10 ± 0.06 × 10 6    cm but was 50 times slower in our implementations. Compared with the A-table method, TLSE gives a more homogenously optimal variance-to-CRLB ratio in the operating region. We show that variance in basis material estimates for TLSE is lower than that of the A-table method by as much as 36 % in the peripheral region of operating range (thin or thick objects). The TLSE is a computationally efficient and fast method for material separation with PCXDs, with accuracy and precision comparable to the MLE.

Paper Details

Date Published: 18 May 2017
PDF: 8 pages
J. Med. Img. 4(2) 023503 doi: 10.1117/1.JMI.4.2.023503
Published in: Journal of Medical Imaging Volume 4, Issue 2
Show Author Affiliations
Paurakh L. Rajbhandary, Stanford Univ. (United States)
Scott S. Hsieh, Stanford Univ. (United States)
Norbert J. Pelc, Stanford Univ. (United States)


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