Prog Med Phys.  2017 Sep;28(3):77-82. 10.14316/pmp.2017.28.3.77.

SPECT Image Analysis Using Computational ROC Curve Based on Threshold Setup

  • 1Department of Biomedical Engineering and Research Institute of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul, Korea.,
  • 2Department of Radiologic Technology Daegu Health College, Daegu, Korea.


We proposed the objective ROC analysis method based on the setting of threshold value for evaluation of single photon emission computed tomography (SPECT) image. This proposed ROC analysis method uses the quantification computational threshold value to each signal on the SPECT image. The SPECT images for this study were acquired by using Monte Carlo n-particle extended simulation code (MCNPX, Ver. 2.6.0, Los Alamos National Laboratory, USA). The basic SPECT detectors and specific water phantom were realized in the simulation, and we could get the simulation results by the simulation operation. We tried to analyze the reconstructed images using threshold value application based objective ROC method. We can get the accuracy information of reconstructed region in the image. This proposed ROC technique can be helpful when we have to evaluate the weak signal for the NM image. In this study, the proposed threshold value based computational ROC analysis method can provide better objectivity than the conventional ROC analysis method.


SPECT; Computational ROC; Threshold value; Objectivity

MeSH Terms

ROC Curve*
Tomography, Emission-Computed, Single-Photon*


  • Fig. 1. Diagram of the threshold value based receiver operation characteristic (ROC) analysis method.

  • Fig. 2. Original water pattern of the single photon emission computed tomography (SPECT) phantom with three radioisotope uptake regions (RURs) (a). Reconstructed tomographic image using the graphic processing unit (GPU) based fast iterative reconstruction algorithm with (b) 128, (c) 64, (d) 32, and (e) 16 projections.

  • Fig. 3. Results of threshold value based receiver operation characteristic (ROC) analysis method for the reconstructed images. (a), (b), (c), and (d) is the reconstructed images using 128, 64, 32, and 16 projection data, respectively. A, B and C is the results the threshold value based ROC analysis method.


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