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Maximum Likelihood – Expectation Maximum Reconstruction with Limited Dataset for Emission Tomography

Patel, Rahul
http://rave.ohiolink.edu/etdc/view?acc_num=akron1175781554

Abstract Details

2007, Master of Science in Engineering, University of Akron, Biomedical Engineering.
Medical Imaging provides a non-invasive technique to look at the structural and functional information of internal organs and structures. One of the most widely used Medical Imaging techniques is emission tomography, in which a radioisotope is given to a patient. Measurement of the radioactive distribution throughout the patient gives the physiological and patho-physiological information about the patient [2]. The two types of emission tomography are Positron Emission Tomography (PET) and Single Photon Emission Tomography (SPECT). The image is reconstructed using the data acquired, also known as the projection data, by a mathematical technique known as Filtered Back Projection (FBP). In emission tomography, it is difficult to locate the exact location from which an emission originated. If the detectors are placed apart, the probability of scatter being detected as signal decreases. The main risk with this method is limited projection data due to the limited number of total number of detectors. The main purpose of this study is to verify whether or not we can reconstruct images with a limited dataset. This method will provide us a better estimate of the exact location of the radioactivity. Traditional reconstruction algorithms like FBP can not reconstruct an image with a limited dataset. Hence we work with an iterative algorithm, Maximum Likelihood – Expectation Maximum (ML-EM). This research uses the most commonly used Shepp – Logan head phantom. To test whether we can reconstruct the image using a limited dataset without any statistical difference, we use the Chi-Square Goodness of Fit test. Since it is an iterative approach, we also look at the line profile of the reconstructed images with a different number of iterations. The primary conclusion drawn from this testing was that no statistically significant differences exist between the images reconstructed from a limited dataset and the original image. We have proven that by using Modified ML-EM algorithm, we can reconstruct the image with limited dataset.
Dale Mugler (Advisor)
ML-EM; detectors; RECONSTRUCTION; projection data; iterations; reconstructed

Recommended Citations

Citations

  • Patel, R. (2007). Maximum Likelihood – Expectation Maximum Reconstruction with Limited Dataset for Emission Tomography [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1175781554

    APA Style (7th edition)

  • Patel, Rahul. Maximum Likelihood – Expectation Maximum Reconstruction with Limited Dataset for Emission Tomography. 2007. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1175781554.

    MLA Style (8th edition)

  • Patel, Rahul. "Maximum Likelihood – Expectation Maximum Reconstruction with Limited Dataset for Emission Tomography." Master's thesis, University of Akron, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1175781554

    Chicago Manual of Style (17th edition)

akron1175781554
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© 2007, all rights reserved.
This open access ETD is published by University of Akron and OhioLINK.