Ms. Lili Zhou Profile

Lili Zhou

at Stony Brook Univ

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Publications (2)

Proceedings Article | 6 March 2008 Paper

SPECT image system optimization using ideal observer for detection and localization

Lili Zhou, Gene Gindi

Proceedings Volume 6917, 69171D (2008) https://doi.org/10.1117/12.770676

KEYWORDS: Collimators, Imaging systems, Tolerancing, Signal detection, Single photon emission computed tomography, Tomography, Signal attenuation, Data modeling, Sensors, Image resolution

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Proceedings Article | 2 March 2006 Paper

Low-contrast lesion detection in tomosynthetic breast imaging using a realistic breast phantom

Lili Zhou, Jorge Oldan, Paul Fisher, Gene Gindi

Proceedings Volume 6142, 61425A (2006) https://doi.org/10.1117/12.652343

KEYWORDS: Breast, Mammography, Reconstruction algorithms, Expectation maximization algorithms, 3D modeling, Sensors, Tissues, Nipple, Signal attenuation, Breast imaging

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Tomosynthesis mammography is a potentially valuable technique for detection of breast cancer. In this simulation study, we investigate the efficacy of three different tomographic reconstruction methods, EM, SART and Backprojection, in the context of an especially difficult mammographic detection task. The task is the detection of a very low-contrast mass embedded in very dense fibro-glandular tissue - a clinically useful task for which tomosynthesis may be well suited. The project uses an anatomically realistic 3D digital breast phantom whose normal anatomic variability limits lesion conspicuity. In order to capture anatomical object variability, we generate an ensemble of phantoms, each of which comprises random instances of various breast structures. We construct medium-sized 3D breast phantoms which model random instances of ductal structures, fibrous connective tissue, Cooper's ligaments and power law structural noise for small scale object variability. Random instances of 7-8 mm irregular masses are generated by a 3D random walk algorithm and placed in very dense fibro-glandular tissue. Several other components of the breast phantom are held fixed, i.e. not randomly generated. These include the fixed breast shape and size, nipple structure, fixed lesion location, and a pectoralis muscle. We collect low-dose data using an isocentric tomosynthetic geometry at 11 angles over 50 degrees and add Poisson noise. The data is reconstructed using the three algorithms. Reconstructed slices through the center of the lesion are presented to human observers in a 2AFC (two-alternative-forced-choice) test that measures detectability by computing AUC (area under the ROC curve). The data collected in each simulation includes two sources of variability, that due to the anatomical variability of the phantom and that due to the Poisson data noise. We found that for this difficult task that the AUC value for EM (0.89) was greater than that for SART (0.83) and Backprojection (0.66).

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