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Technical Note: The nearest neighborhood-based approach for estimating basis line-integrals using photon-counting

Jeonghyeon Nam¹, Okkyun Lee¹

¹Department of Robotics Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea.

Medical Physics

|June 25, 2021

Summary

A new calibration-based estimator for photon-counting detector (PCD) x-ray computed tomography (CT) uses nearest neighbor (NN) search. This NN-based method is as accurate as model-based estimators but computationally efficient.

Keywords:

k-d tree maximum likelihood nearest neighbor photon-counting detector

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Area of Science:

Medical Imaging
Computational Physics

Background:

Photon-counting detectors (PCDs) offer advantages in X-ray computed tomography (CT) due to their superior energy sensitivity and reduced noise.
Accurate material decomposition in CT requires robust calibration methods to account for detector response and spectral variations.

Purpose of the Study:

To develop a novel calibration-based estimator for PCD-based X-ray CT.
To introduce a computationally efficient alternative to existing model-based estimators.

Main Methods:

A nearest neighbor (NN)-based estimator was developed, utilizing calibration data to estimate basis line-integrals from PCD output.
A k-d tree was employed to accelerate the nearest neighbor search process, enhancing computational efficiency.

Main Results:

The NN-based estimator demonstrated performance comparable to the model-based maximum likelihood (ML) estimator in slab phantom studies.
Both methods achieved the Cramér-Rao lower bound and provided unbiased estimates for water, bone, and gold basis materials.
Validation in K-edge imaging showed nearly unbiased gold concentrations in the region of interest.

Conclusions:

The proposed NN-based method offers an accurate and computationally efficient calibration approach for PCD-based X-ray CT.
It requires only calibration measurements, simplifying the implementation and reducing computational burden compared to ML estimators.