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.
Dual-energy computed tomography (DECT) is a promising imaging modality. It has the potential to quantify different material densities and plays an important role in many clinical applications. To enable multiple material decomposition (MMD), the conventional analytical MMD algorithm assumes the presence of at most three materials in each image pixel, and each pixel is decomposed into a certain basis material triplet. However, the MMD algorithm requires strong prior knowledge of the mixture composition, and the decomposition performance is compromised around the boundaries of different compositions. In this work, we developed an analytical model based deep neural network MMD-Net to achieve multi-material decomposition in DECT. In particular, the type of the basis material triplet in each image pixel and the attenuation coefficients of each material are learned by dedicated convolution neural network modules, and the material-specific density maps are obtained from the analytical MMD algorithm. Physical experiments of a pig leg and a pork backbone specimen with inserted iodine concentrations were acquired to evaluate the performance of the MMD-Net. Results show that the proposed MMD-Net could provide high decomposition accuracy, and reduce the decomposition artifacts.
As one of the most advanced CT imaging modalities, spectral CT plays important roles in generating materialspecific information and adding vital clinical values for disease diagnosis and therapy. To obtain the spectral CT images, currently, advanced X-ray source or detector assembly is often required, which significantly increases the hardware cost and the patient burden. As a consequence, the accessibility of spectral CT is strongly limited and has not been widely implemented in daily clinics. To solve such difficulty, this work attempts to investigate a new CT data acquisition protocol and spectral CT image reconstruction algorithm. In particular, the X-ray tube voltage is slowly modulated during the gantry rotation. By doing so, spectral information that varies from one projection view to another can be acquired in one single CT scan. Afterwards, a model-based material decomposition algorithm that reconstructs the CT image from the acquired projections is utilized to perform multi-material decomposition. To evaluate the performance of this novel spectral CT imaging approach, a numerical phantom containing iodine and gadolinium solutions is imaged with different kVp modulations, i.e., different number of modulation periods per rotation. Results demonstrate that the proposed spectral CT image reconstruction algorithm can be used to accurately decompose the water, iodine and gadolinium basis images for different tube voltage modulation rates. Moreover, high-quality monochromatic images can be synthesized as well. In conclusion, a low-cost multi-material spectral CT imaging approach is developed based on the slow tube voltage modulation method.
The cone-beam CT (CBCT) imaging systems that based on flat panel detectors have been widely implemented in image-guided intervention and radiation therapy applications. However, the imaging performance of CBCT is strongly limited. One of such limitations is the lack of quantitative imaging capability, which is important for material recognition, image contrast enhancement, and dose reduction. Over the past decade, dual-energy computed tomography (DECT) has become a promising imaging technique in generating quantitative material information, whereas, multiple (>2) basis images with high quality and accuracy are hard to be obtained from the conventional DECT image reconstruction algorithms. In this work, an innovative deep learning technique is presented to realize three materials decomposition from the dual-energy CBCT scans. In this strategy, a dedicated end-to-end convolutional neural network (CNN) is developed. It accepts the low and high energy CBCT projections, and automatically outputs three different basis image volumes (water basis, iodine basis, CaCl2 basis) with high accuracy. Training data was synthesized numerically from the photos downloaded from ImageNet. Dual-energy projections of the Iodine/CaCl2 phantom with ground truth were acquired from our in-house benchtop CBCT system to validate the proposed method. Results demonstrate that this novel network is able to generate three different material bases with high accuracy (decomposition errors less than 5%). In conclusion, the proposed CNN based multi-material (≥ 3) decomposition approach shows promising benefits in high quality dual-energy CBCT imaging applications.
Reducing the radiation dose is always an important topic in modern computed tomography (CT) imaging. As the dose level reduces, the conventional analytical filtered backprojection (FBP) reconstruction algorithm becomes inefficient in generating satisfactory CT images for clinical applications. To overcome such difficulties, in this study we developed a novel deep neural network (DNN) for low dose CT image reconstruction by exploring the simultaneous sinogram domain and CT image domain denoising capabilities. The key idea is to jointly denoise the acquired sinogram and the reconstructed CT image, while reconstructing CT image in an end-to-end manner with the help of DNN. Specifically, this new DNN contains three compartments: the sinogram domain denoising compartment, the sinogram to CT image reconstruction compartment, and the CT image domain denoising compartment. This novel sinogram and image domain based CT image reconstruction network is named as ADAPTIVE-NET. By design, the first and third compartments of ADAPTIVE-NET can mutually update their parameters for CT image denoising during network training. Clearly, one advantage of using ADAPTIVE-NET is that the unique information stored in sinogram can be accessed directly during network training. Validation results obtained from numerical simulations demonstrate that this newly proposed ADAPTIVE-NET can effectively improve the quality of CT images acquired with low radiation dose levels.
As a quantitative CT imaging technique, the dual-energy CT (DECT) imaging method attracts a lot of research interests. However, material decomposition from high energy (HE) and low energy (LE) data may suffer from magnified noise, resulting in severe degradation of image quality and decomposition accuracy. To overcome these challenges, this study presents a novel DECT material decomposition method based on deep neural network (DNN). In particular, this new DNN integrates the CT image reconstruction task and the nonlinear material decomposition procedures into one single network. This end-to-end network consists of three compartments: the sinogram domain decomposition compartment, the user-defined analytical domain transformation operation (OP) compartment, and the image domain decomposition compartment. By design, both the first and third compartments are responsible for complicated nonlinear material decomposition, while denoising the DECT images. Natural images are used to synthesized the dual-energy data with assumed certain volume fractions and density distributions. By doing so, the burden of collecting clinical DECT data can be significantly reduced, therefore the new DECT reconstruction framework becomes more easy to be implemented. Both numerical and experimental validation results demonstrate that the proposed DNN based DECT reconstruction algorithm can generate high quality basis images with improved accuracy.
In this work, we present a novel convolutional neural network (CNN) enabled Moiré artifacts reduction framework for the three contrast mechanism images, i.e., the absorption image, the differential phase contrast (DPC) image, and the dark-field (DF) image, obtained from an x-ray Talbot-Lau phase contrast imaging system. By mathematically model the various potential non-ideal factors that may cause Moiré artifacts as a random fluctuation of the phase stepping position, rigorous theoretical analyses show that the Moiré artifacts on absorption images may have similar distribution frequency as of the detected phase stepping Moiré diffraction fringes, whereas, their periods on DPC and DF images may be doubled. Upon these theoretical findings, training dataset for the three different contrast mechanisms are synthesized properly using natural images. Afterwards, the three datasets are trained independently by the same modified auto-encoder type CNN. Both numerical simulations and experimental studies are performed to validate the performance of this newly developed Moiré artifacts reduction method. Results show that the CNN is able to reduce residual Moiré artifacts efficiently. With the improved signal accuracy, as a result, the radiation dose efficiency of the Talbot-Lau interferometry imaging system can be greatly enhanced.
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