Browsing by Author "Mawlawi, Osama"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Compressive Sensing in Positron Emission Tomography (PET) Imaging
    (2015-04-16) Valiollahzadeh, Majid; Clark, John; Veeraghavan, Ashok; Jacot, Jeffrey; Mawlawi, Osama; kelly, Kevin
    Positron emission tomography (PET) is a nuclear medicine functional imaging modality, applicable to several clinical problems, but especially in detecting the metabolic activity (as in cancer). PET scanners use multiple rings of gamma ray detectors that surround the patient. These scanners are quite expensive (1-3 million dollars), therefore a technology that would allow the reduction in the number of detectors per ring without affecting image quality, could reduce the scanner cost, thereby making this imaging modality more accessible to patients. In this thesis , a mathematical technique known as compressive sensing is applied in an effort to decrease the number of detectors required, while maintaining good image quality. A CS model was developed based on a combination of gradient magnitude and wavelet domains to recover missing observations associated with PET data acquisition. The CS model also included a Poisson-distributed noise term. The overall model was formulated as an optimization problem wherein the cost function was a weighted sum of the total variation and the L1-norm of the wavelet coefficients. Subsequently, the cost function was minimized subject to the CS model equations, the partially observed data, and a penalty function for noise suppression (the Poisson log-likelihood function). We refer to the complete model as the WTV model. This thesis also explores an alternative reconstruction method, wherein a different CS model based on an adaptive dictionary learning (DL) technique for data recovery in PET imaging was developed. Specifically, a PET image is decomposed into small overlapped patches and the dictionary is learned from these overlapped patches. The technique has good sparsifying properties and the dictionary tends to capture local as well as structural similarities, without sacrificing resolution. Recovery is accomplished in two stages: a dictionary learning phase followed by a reconstruction step. In addition to developing optimized CS reconstruction, this thesis also investigated: (a) the limits of detector removal when using the DL CS reconstruction algorithm; and (b) the optimal detector removal configuration per ring while minimizing the impact on image quality following recovery using the CS model. Results of these investigations can serve to help make PET scanners more affordable while maintaining image quality. These results can also be used to improve patient throughput by redesigning scanners so that removed detectors can be placed in axial extent to image a larger portion of the body. This will help increase scanner throughput hence improve scanner efficiency as well as patient discomfort due to long scan time.
  • Thumbnail Image
    Item
    Dictionary learning for data recovery in positron emission tomography
    (IOP Publishing, 2015) Valiollahzadeh, SeyyedMajid; Clark, John W. Jr.; Mawlawi, Osama
    Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline).A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions.For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients' studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification.