Jinyi Qi - US grants

DESCRIPTION (provided by applicant): Positron emission tomography (PET) is a functional imaging modality that is capable of imaging biochemical processes in humans or animals through the use of radioactive tracers. PET/CT with [18F]fluorodeoxyglucose (FDG) is increasingly being used for staging, restaging and treatment monitoring for cancer patients with different types of tumors. However, current FDG-PET provides a low sensitivity to detect micrometastases and small tumor infiltrated lymph nodes. The goal of this project is to improve the efficacy of PET imaging through the development of novel image reconstruction methods and data analysis tools. During the current funding period, we have developed a method for tuning reconstruction algorithm based on the noise characteristics in measured patient data. This patient-adaptive reconstruction algorithm has been validated using computer simulations and phantom experiments. In the next phase of the project, we will implement the patient-adaptive algorithm on clinical PET scanners and validate the method using patient data. We will further expand the capability of PET imaging by developing novel methods to utilize the anatomical information provided by PET/CT scanners and by exploring the potential of dynamic PET for cancer detection and staging. The four specific aims of the project are (1) To implement the patient-adaptive MAP reconstruction on clinical scanners and to validate the algorithm using breast cancer patients;(2) To develop a novel approach to PET image reconstruction using anatomical information;(3) To develop statistically efficient image reconstruction methods for dynamic PET;(4) To identify spatial-temporal features in dynamic PET image for detection and characterization of breast cancer and to evaluate the performance using breast cancer patient data. The first aim is an important step towards translating image reconstruction technology development into patient health care. Once validated using breast cancer patients, the method is readily applicable to imaging other types of tumors. The second to the fourth aims will greatly enhance the capability of PET by taking advantage of the recent advances in instrumentation (wide availability of PET/CT) and the dynamic nature of PET imaging. We expect the new methods to be developed will be able to extract clinically relevant features from dynamic PET for detecting small tumors and characterizing the malignancy of primary tumors. All the results will be validated using breast cancer patient data with histologically verified ground truth. The success of this research will have a significant and positive impact on the management of patients with breast cancer. PUBLIC HEALTH RELEVANCE: Positron emission tomography (PET) is a medical imaging technique that can detect cancer and monitor treatment response. This research aims to improve the efficacy of PET by developing novel image reconstruction and data processing tools that will enable early detection and characterization of breast cancer. The success of this research is of substantial benefit to the general population of breast cancer suffers.

[unreadable] DESCRIPTION (provided by applicant): Iterative reconstruction algorithms that significantly improve image quality over filtered backprojection methods have been developed for emission tomography. However, most current reconstruction algorithms implicitly assume that the system model is exact. The daunting computational challenge associated with the direct use of an exact system model in each forward and back projection has often led people to adopt less accurate models. This results in increased noise and reduced resolution in reconstructed images, because the effect of the modeling error cannot be corrected in the existing methods. The goal of this grant is to develop a new class of iterative reconstruction methods that can compensate the effect of modeling error. The work is based on our thorough analysis of error propagation from each component in the system model into reconstructed images. The innovation of the new method is that it does not require an exact system model in every forward and back projection. The method can obtain high-resolution images when direct use of an accurate system model in the iterative reconstruction is impractical, and it can also reduce reconstruction time by using simplified fast forward and back projectors without sacrificing image quality. We will first develop the theory of high-resolution iterative image reconstruction with error correction capability. Then we will focus on the application and validation of the theory in positron emission tomography (PET). We will implement new reconstruction algorithms on microPET scanners, and will evaluate the lesion detection and quantitation performance using Monte Carlo simulations, physical phantom experiments, and real animal data. We believe that the new algorithms will provide high-resolution images and accurate quantitative information for understanding human diseases in small animal models. Upon success, we will extend the reconstruction algorithm to clinical imaging systems and will also apply the theory to other imaging modalities, such as X-ray CT, SPECT, MRI, and optical tomography. Lay abstract: Positron emission tomography (PET) is a functional imaging modality that is widely used in clinical and biological studies. This project will develop a novel image reconstruction method for PET which will provide high-resolution images and accurate quantitative information for understanding and treating human diseases. [unreadable] [unreadable] [unreadable]

Project Summary/Abstract Positron emission tomography (PET) is a high-sensitivity molecular imaging modality widely used in oncology, neurology, and cardiology, with the ability to observe molecular-level activities inside a living body through the injection of specific radioactive tracers. In addition to the commonly used F-18-FDG, new tracers are being constantly developed and investigated to pinpoint specific pathways in various diseases. New PET scanners are also being proposed by exploiting time of flight (TOF) information, enabling depth of interaction capability, and extending the solid angle coverage. To realize the full potential of the new PET tracers and scanners, there is an increasing need for the development of advanced image reconstruction methods. This grant application proposes a new framework for regularized image reconstruction that synergistically integrates deep learning and regularized image reconstruction. The new framework is enabled by the recent advances in machine learning, which provide a tool to digest vast amount information embedded in existing medical images. The proposed method embeds a pre-trained deep neural network in an iterative image reconstruction framework and uses the deep neural network to regularize PET image directly. By training the deep neural network with a large amount of high-quality low-noise PET images, the proposed method can capture complex prior information from existing inter-subject and intra-subject data and thus is expected to substantially outperform the current state-of-the-art regularized image reconstruction method. The two specific aims of this exploratory proposal are (1) to develop the theoretical framework to synergistically integrate deep learning in regularized image reconstruction for PET and (2) to implement the proposed method and validate its effectiveness using existing animal data. Once the proposed method is validated using existing animal data, we will seek funding to acquire necessary human data for the implementation of the proposed method on clinical PET scanners.

Research applications of brain Positron Emission Tomography (PET) have been in place for over 40 years. The combination of quantitative PET systems with novel radiotracers has led to a numerous imaging para- digms to understand normal brain physiology including neurotransmitter dynamics and receptor pharmacology at rest and during activation. Brain-dedicated PET systems offer important advantages over currently available PET systems in terms of sensitivity and resolution. However, the state-of-the-art for brain PET has not progressed beyond the 20-year-old HRRT. Therefore, there is a compelling need to build the next generation of brain PET systems for human studies. This proposal brings together a highly experienced collaborative team from Yale, UC Davis, and United Imaging Healthcare America (UIHA). to develop the next generation NeuroEXPLORER (NX) PET system with the following Aims. Specific Aim 1: Design and Build the NeuroEXPLORER: In 2 years, we will complete the design and build the NX system. The design includes high performance LYSO-SiPM blocks with small detectors, 4-mm depth-of-interaction, 250 ps time-of-flight resolution, and axial length of ~50 cm, paired with CT for attenuation correction. This design will produce a factor of 10 greater effective sensitivity than the HRRT and practical resolution of 1.5-2 mm in the human brain. The system will include built-in real-time state-of-art motion tracking cameras and will be tested using novel phantom experiments to assess the full-range of operation to validate the dramatic improvement in small- region precision and accuracy. Specific Aim 2: Algorithm Development for Fully-Quantitative Brain PET. We will develop the novel algorithms for this system. Using EXPLORER experience. we will implement reconstruction algorithms to produce dynamic images with uniform ultra-high resolution in space and time, Extending Yale?s HRRT motion correction experience, we will develop camera-based motion detection and correction algorithms to deliver ultra-high resolution human brain images. Using the carotid artery shape and geometry, we will develop methods to accurately measure blood activity to be compared to human arterial data with the goal to permit kinetic modeling without arterial sampling. We will develop noise reduction methods with machine learning to reduce dose for studying health brains and to eliminate the need for the CT scan for attenuation correction. Specific Aim 3: Human Paradigm Demonstration. With human subjects, we will evaluate specific imaging paradigms to demonstrate the effectiveness of the NX system: 1) demonstration of the dramatic sensitivity increase (with a direct comparison to the HRRT) and its impact on detection of pharmacologic effects, 2) leveraging high sensitivity to reliably measure uptake in small nuclei; and 3) opening new frontiers of imaging neurotransmitter dynamics, including dopamine and opioid release. The ultimate goal is a fully functioning and characterized system that dramatically expands the scope of brain PET protocols and applications.

Project Summary/Abstract Positron emission tomography (PET) is a high-sensitivity molecular imaging modality widely used in oncology, neurology, and cardiology, with the ability to observe molecular-level activities inside a living body through the injection of specific radioactive tracers with positron emitters. Recently it has been shown that the lifetime of positronium, a metastable state formed by a pair of electron and positron, is sensitive to the microenvironment of the surrounding tissue, such as oxygen pressure. Such information is valuable for cancer staging and treatment planning. However, currently there is no practical method to imaging the positronium lifetime at a spatial resolution matching that of PET due to the limited photon detection sensitivity and lack of proper image reconstruction method. This proposal addresses these problems by developing a statistically based image reconstruction method for positronium lifetime imaging (PLI) and combining it with the newly developed total- body EXPLORER PET scanner at UC Davis. We will demonstrate the effectiveness of the method using phantom experiments on the EXPLORER scanner. The advantage of the PLI is that the lifetime measurement is independent of the tracer concentration and can be used together with standard PET imaging. The combination of PLI and PET will add another dimension to current PET imaging and provide useful information for understanding the microenvironment of the tissue for studying various human diseases.