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Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images
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10.1118/1.2988161
    + View Affiliations - Hide Affiliations
    Affiliations:
    1 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 2M9 Canada
    2 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G 2M9 Canada, Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ontario, M5G 2M9 Canada, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, M5G 2M9 Canada, Department of Otolaryngology-Head and Neck Surgery, University of Toronto, Toronto, Ontario, M5G 2M9 Canada, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, M5G 2M9 Canada, Institute of Medical Science, University of Toronto, Toronto, Ontario, M5G 2M9 Canada
    a) Author to whom correspondence should be addressed. Electronic mail: Jeff.Siewerdsen@uhn.on.ca. Telephone: 416-946-4501 (x5516); Fax: 416-946-6529.
    Med. Phys. 35, 5043 (2008); http://dx.doi.org/10.1118/1.2988161
/content/aapm/journal/medphys/35/11/10.1118/1.2988161
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/35/11/10.1118/1.2988161

Figures

Image of FIG. 1.
FIG. 1.

Model hypotheses and task functions investigated in this work. (a) Sphere detection has sphere absent and sphere present, (b) shape discrimination has a sphere and a cylinder, and (c) texture discrimination has a smooth and crenellated cylinder as the two hypotheses, respectively. Task functions adapted from Ref. 17.

Image of FIG. 2.
FIG. 2.

(a) Image of the flat-panel detector and the task phantom used to acquire DE images of the various stimuli corresponding to the different imaging tasks. (b) Radiographic projection of various stimuli. Experimental setup and phantom adapted from Ref. 17.

Image of FIG. 3.
FIG. 3.

Sphere detection performance as a function of dose allocation in DE imaging (i.e., the fraction of dose allocated to the low-energy projection). Data points represent measurements from human observers, and curves represent model observers. (a–c) Soft-tissue and (d–f) bone image. Results are shown for the three decomposition algorithms (SLS, SSH, and ACNR).

Image of FIG. 4.
FIG. 4.

(a–c) Shape and (d–f) texture discrimination performance as a function of dose allocation for the soft-tissue image. Data points represent measurements from human observers, and curves represent model observers. Results are shown for the three decomposition algorithms (SLS, SSH, and ACNR).

Image of FIG. 5.
FIG. 5.

Experimental and theoretical observer performance for the shape and texture discrimination tasks evaluated as a function of decomposition parameters in the SSH and ACNR algorithms.

Image of FIG. 6.
FIG. 6.

Illustration of basic agreement between Fourier-based descriptions of NEQ, task, and observer model with human observer performance in the discrimination of a textured and smooth disk. (a–c) Plot of NEQ and task function (weighted by the eye filter) for three values of the parameter employed in the SSH decomposition algorithm. Corresponding images of the (d–f) crenellated and (g–i) smooth cylinder. Since the value of determines the zero in the NEQ, the value for which the zero coincides with frequencies of interest for this task causes the discriminate performance to plummets. Such is clearly observed in (b), where the value places the zero of the NEQ near the peak of the task function, and the ability to discriminate images (e) and (h) is lost.

Tables

Generic image for table
TABLE I.

Summary of low- and high-energy convolution filters for three DE decomposition algorithms (SLS, SSH, and ACNR), which are special cases of the general form in Eq. (1). denotes the tissue cancellation parameters, ideally given as the ratio of the effective linear attenuation coefficient of the cancelled material , is the Dirac delta function, denotes the noise cancellation parameters, denotes the tissue cancellation parameters for the complementary image, and and are low- and high-pass convolution filters, respectively.

Generic image for table
TABLE II.

Nominal values for the decomposition parameters associated with each of the decomposition techniques.

Generic image for table
TABLE III.

Number of cases in each study for a given observer. Each case was repeated five times (using different projections so that all cases were statistically independent).

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/content/aapm/journal/medphys/35/11/10.1118/1.2988161
2008-10-16
2014-04-19
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/35/11/10.1118/1.2988161
10.1118/1.2988161
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