Improved lesion detection in SPECT using MLEM reconstruction

Receiver operating characteristic (ROC) analysis was applied to determine if the maximum likelihood expectation maximization (MLEM) reconstruction algorithm improves lesion detectability in single photon emission computed tomography (SPECT) when compared with a filtered backprojection (FBP) algorithm. The performance of the reconstruction algorithms was evaluated based on cold lesion detection by human observers. Simulated SPECT projection data with added Poisson noise were constructed using the MLEM and the FBP algorithms. The parameters of the study, including lesion size and total counts, were chosen to maximize the statistical power of the test. Three hundred images were generated and nine observers were employed for the study. The average ROC curves for both reconstruction methods were measured. Diagnostic performance was quantified using the area under the ROC curves. MLEM showed improved detectability for all the observers. Observer P-values were calculated, and Student's t-test was performed in order to verify the statistical significance of the observed improvement. >

[1]  Ronald J. Jaszczak,et al.  Physical Factors Affecting Quantitative Measurements Using Camera-Based Single Photon Emission Computed Tomography (Spect) , 1981, IEEE Transactions on Nuclear Science.

[2]  K. Lange,et al.  EM reconstruction algorithms for emission and transmission tomography. , 1984, Journal of computer assisted tomography.

[3]  C. Metz,et al.  A New Approach for Testing the Significance of Differences Between ROC Curves Measured from Correlated Data , 1984 .

[4]  C. Metz ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[5]  C. Metz,et al.  Statistical significance tests for binormal ROC curves , 1980 .

[6]  L. J. Thomas,et al.  Noise and Edge Artifacts in Maximum-Likelihood Reconstructions for Emission Tomography , 1987, IEEE Transactions on Medical Imaging.

[7]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  J. Swets ROC analysis applied to the evaluation of medical imaging techniques. , 1979, Investigative radiology.

[10]  R. Jaszczak,et al.  Inverse Monte Carlo: A Unified Reconstruction Algorithm for SPECT , 1985, IEEE Transactions on Nuclear Science.

[11]  C E Floyd,et al.  Experimentally measured scatter fractions and energy spectra as a test of Monte Carlo simulations. , 1987, Physics in medicine and biology.

[12]  John A. Swets,et al.  Evaluation of diagnostic systems : methods from signal detection theory , 1982 .

[13]  C E Metz,et al.  Some practical issues of experimental design and data analysis in radiological ROC studies. , 1989, Investigative radiology.