A methodology for testing for statistically significant differences between fully 3D PET reconstruction algorithms

Furuie, S.S.; Herman, Gabor T.; Narayan, T. K.; Kinahan, Paul E.; Karp, Joel S.; Lewitt, R.M.; Matej, Samuel

doi:10.1088/0031-9155/39/3/003

Cited by 73 publications

(49 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 5 shows that the average cerebellum TACs with the 10-mg/day dose were fairly close to each other using the simple noise model [39] (Fig. 5a) and Monte Carlo simulations [40] (Fig.…”

Section: Simple Noise Model Vs Monte Carlo Simulationsmentioning

confidence: 60%

“…The aim of these analyses was to confirm the feasibility of the simple noise model [39] for our simulation purposes. The Monte Carlo simulator provides a realistic model for the data acquisition taking into account the system dead-time, random and scatter coincidences, detector normalization, and attenuation correction.…”

Section: Dynamic Numerical Phantomsmentioning

confidence: 97%

“…the variance is assumed to be Gaussian [39]. The noise added to the sinogram data in our phantom was the sum of Poisson and Gaussian.…”

Section: Dynamic Numerical Phantomsmentioning

confidence: 99%

See 2 more Smart Citations

Voxel-based NK1 Receptor Occupancy Measurements with [18F]SPA-RQ and Positron Emission Tomography: A Procedure for Assessing Errors from Image Reconstruction and Physiological Modeling

et al. 2007

View full text Add to dashboard Cite

This study shows that even this kind of complicated receptor study can be statistically evaluated. The reconstruction methods had an effect on the variance in the voxel-based receptor occupancy calculation. The model calculation methods influenced the average bias. The test method was found useful in assessing the methodological sources of systematic and random error in receptor occupancy estimation with PET.

show abstract

“…Fig. 5 shows that the average cerebellum TACs with the 10-mg/day dose were fairly close to each other using the simple noise model [39] (Fig. 5a) and Monte Carlo simulations [40] (Fig.…”

Section: Simple Noise Model Vs Monte Carlo Simulationsmentioning

confidence: 60%

Section: Dynamic Numerical Phantomsmentioning

confidence: 97%

See 1 more Smart Citation

Voxel-based NK1 Receptor Occupancy Measurements with [18F]SPA-RQ and Positron Emission Tomography: A Procedure for Assessing Errors from Image Reconstruction and Physiological Modeling

et al. 2007

View full text Add to dashboard Cite

show abstract

“…This methodology is inspired on the one traditionally used in the field of image reconstruction from projections, which has already been applied in medicine [21][22][23] as well as electron microscopy [24][25][26].…”

Section: Methodology For Objective Comparisonmentioning

confidence: 99%

“…Statistical significance testing and confidence interval estimation were then applied to quantitatively assess the plausibility of the superiority of one method over the other from the point of view of the FOMs. Such statistical analyses were independently carried out for every value of bpp at which our LPIC and P-JPEG were evaluated, and were performed by means of a Student's t-test for paired data [22,36].…”

Section: Objective Comparison Based On Phantomsmentioning

confidence: 99%

Progressive Image Transmission in Telemicroscopy: A Quantitative Approach for Electron Microscopy Images of Biological Specimens

Ruiz¹

2002

Real-Time Imaging

View full text Add to dashboard Cite

T his work presents a rigorous and objective evaluation of two progressive image transmission techniques in a framework of telemicroscopy of biological specimens. First of all, a Lossless Progressive Image Codec (LPIC) which is based on a specific wavelet transform and an efficient encoding method is introduced. This system is then compared to the standard Progressive-JPEG (P-JPEG) for the specific task of detecting biological specimens in the images progressively received by a biologist controlling a remote transmission electron microscope (TEM). Both methods have been quantitatively compared by means of a taskoriented methodology that guarantees an objective comparison for the task at hand. Such a methodology is based upon a wide ensemble of artificial images resembling real electron microscopy images, as well as on a thorough set of figures of merit assessing the quality of the reconstructed images. The results that have been obtained allow to claim with statistical significance that our method outperforms the standard P-JPEG throughout the transmission process. Finally, both the progressive transmission methods LPIC and P-JPEG were applied over real electron microscopy images from different subfields of structural biology, and the results exhibited a similar behavior to that obtained for artificial images. Visualization results manifest that LPIC exhibits excellent levels of details from the very beginning, whereas P-JPEG undergoes severe blocking artifacts along the first stages of the transmission. Therefore, the conclusion of the work is that the superiority of the progressive image transmission method introduced here over P-JPEG becomes manifest for the task of detecting biological specimens in the progressively transmitted images.

show abstract

Computerized evaluation of image reconstruction algorithms

Browne

Herman

1996

Int. J. Imaging Syst. Technol.

View full text Add to dashboard Cite

In medical imaging an enormous variety of algorithms have been proposed to reconstruct a cross section of the human body. In assessing the relative task‐oriented performance of reconstruction algorithms, it is desirable to assign statistical significance to claims of superiority of one algorithm over another. However, very often the achievement of statistical significance demands a large number of observations. Performing such an evaluation on mathematical phantoms requires a means of running the competing algorithms on projection data obtained from a large number of randomly generated phantoms. Thereafter, various numerical measures of agreement between the reconstructed images and the original phantoms may be used to reach a conclusion which has some statistical substance. In this article we describe the software SuperSNARK, which automates an evaluation methodology for assigning statistical significance to the observed differences in performance of two or more image reconstruction algorithms. As a demonstration, we compare the relative efficacy of the maximum likelihood expectation maximization (ML‐EM) algorithm and the filtered backprojection (FBP) method for performing three medical tasks in positron emission tomography (PET)—estimation of total uptake by structures, detection of relatively higher uptake between pairs of symmetric structures, and estimation of uptake at individual points within structures. We find that for estimating total uptake ML‐EM outperforms FBP, for detecting relatively higher uptake there is not a statistically significant difference between the two methods, and for estimating pointwise uptake FBP outperforms ML‐EM. It is demonstrated that SuperSNARK makes it easy to apply the methodology of statistical hypothesis testing to substantiate such claims of task‐specific superiority of one reconstruction algorithm over another. © 1996 John Wiley & Sons, Inc.

show abstract

A methodology for testing for statistically significant differences between fully 3D PET reconstruction algorithms

Cited by 73 publications

References 9 publications

Voxel-based NK1 Receptor Occupancy Measurements with [18F]SPA-RQ and Positron Emission Tomography: A Procedure for Assessing Errors from Image Reconstruction and Physiological Modeling

Voxel-based NK1 Receptor Occupancy Measurements with [18F]SPA-RQ and Positron Emission Tomography: A Procedure for Assessing Errors from Image Reconstruction and Physiological Modeling

Progressive Image Transmission in Telemicroscopy: A Quantitative Approach for Electron Microscopy Images of Biological Specimens

Computerized evaluation of image reconstruction algorithms

Contact Info

Product

Resources

About