Precision radioligand therapy calls for patient dosimetry, which in turn requires the knowledge of time integrated activity curves for various tissues of interest, often acquired through post therapy imaging. A number of imaging methods have been proposed, with considerations of both dosimetric accuracy and the practicality of the method. Clinical studies on patients, as the more traditional way of evaluating these methods, are faced with challenges from: (1) the lack of ground truth and (2) the burden of performing the clinical studies. A Monte Carlo simulation based approach, as is proposed in this work, would address the challenges and provide fast and reliable ways to assess different imaging method. The presented work demonstrates the potential of the proposed simulation based approach in the the design of the post radioligand therapy imaging methods for patient dosimetry purposes.
This abstract presents a new super resolution CBCT imaging method, named as suRi 2.0, that utilizes the natural detector element offsets between the top and bottom detector layers. A simple mathematical model is assumed to explain the feasibility of recovering the high resolution spatial information. In addition, a deep RNN network is developed to extract the high resolution details from the projections having lower spatial resolution. Experimental results show that CBCT images reconstructed from suRi 2.0 exhibit comparable spatial resolution to those obtained with smaller detector element binning.
For the U-Net based low dose CT (LDCT) imaging, there remains an interesting question: can the LDCT imaging neural network trained at one image resolution be transferred and applied directly onto another LDCT imaging application of different image resolution, provided that both the noise level and the structural content are similar? To answer this question, numerical simulations are performed with high-resolution (HR) and low-resolution (LR) LDCT images having comparable noise levels. Results demonstrated that the U-Net trained with LR CT images can be used to effectively reduce the noise on HR CT images, and vice versa. However, additional artifacts may be generated when transferring the same U-Net to a different LDCT imaging task with varied image spatial resolution due to the noise induced 2D features. For example, noticeable bright spots were generated at the edges of the FOV when the HR CT image is denoised by the LR CT image trained U-Net. In conclusion, this study suggests that it is necessary to retrain the U-Net for a dedicated LDCT imaging application.
The purpose of this study is to evaluate and compare the quantitative imaging performance of the dual-energy CT (DECT) and differential phase contrast CT (DPCT). The electron density (ρe) and the effective atomic number (Zeff) are selected as the two comparison bases for the DECT and DPCT imaging. From the numerically simulated data, image domain based decomposition algorithms are used to extract the ρe and Zeff information for three different spatial resolution levels (0.3 mm, 0.1 mm, and 0.03 mm). The contrast-to-noise-ratio (CNR) and modeled human observer studies have been investigated to compare the DECT and DPCT quantitative imaging performance. At low spatial resolution (0.3 mm), the DECT shows better quantitative imaging performance than DPCT. As a contrary, the DPCT outperforms the DECT for ultra high spatial resolution (0.03 mm) imaging. With the 0.1 mm spatial resolution, the DECT and DPCT shows similar quantitative imaging performance. In conclusion, the DECT is more favored for low spatial resolution applications, such as the diagnostic imaging tasks. However, the DPCT would be recommended for ultra high spatial resolution imaging tasks, such as the micro-CT imaging tasks.
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