Arman Rahmim

Professor

Research Interests

Image Reconstruction
Machine learning and radiomics
medical physics
Molecular imaging
Quantitative Imaging
Theranostics

Relevant Thesis-Based Degree Programs

 
 

Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Anthropomorphic phantoms for quantification of ¹⁸F-PET and ¹⁷⁷Lu-SPECT imaging in radiopharmaceutical therapies (2023)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Development of a novel method to estimate kinetic micro-parameters in dynamic whole-body PET imaging protocols (2023)

For whole-body (WB) kinetic modeling based on a typical Positron Emission Tomography (PET) scanner, a multi-pass multi-bed scanning protocol is necessary because of the limited axial field of view. Such a protocol introduces loss of early dynamics in the time-activity curves (TACs) and sparsity in TAC measurements; thus inducing uncertainty in parameter estimation when using least squares estimation (LSE) (i.e., common standard), especially for kinetic micro-parameters. To address the issue above, this thesis proposes a method that can estimate micro-parameters by building a reference TAC database, and selecting optimal parameters based on analysis of multiple aspects of the TACs, while in our assessment of performance compared to conventional methods we focus on general image qualities, overall visibility, and tumor detectability.To achieve the research goal above, we developed a novel parameter estimation method called parameter combination-driven estimation (PCDE), which has two distinctive characteristics:1) improved capability of finding a correct correlation between early and late TACs at the cost of the resolution of the estimated parameter, and 2) exploitation of multiple aspects of TAC. To compare the general image quality between the two methods, we plotted tradeoff curves for the normalized bias (NBias) and the normalized standard deviation (NSD). We also evaluated the impact of different iteration numbers of the ordered-subset expectation maximization (OSEM) reconstruction algorithm on the tradeoff curves. In addition, for overall visibility, which is a measure of the capability of identifying suspicious lesions in WB (i.e., global inspection), the overall signal-to-noise ratio (SNR) and spatial noise (NSDspatial) were calculated and compared. Furthermore, the contrast-to-noise ratio (CNR) and relative error of the tumor-to-background ratio (RETBR) were calculated to compare tumor detectability within a specific organ (i.e., local inspection). Through the proposed method, the improved general image quality, overall visibility, and tumor detectability were verified in micro-parametric images with OSEM reconstructions. We expect our work to contribute to opening the door to use of a typical PET scanner to reliably estimate kinetic micro-parameters in WB imaging, which has been so far very challenging owing to significant uncertainties in estimates when using LSE methods.

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