Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Functional positron emission tomography (fPET) provides a method to track molecular targets in the human brain. With a radioactively-labelled glucose ...

Monash University

In dynamic Magnetic Resonance Imaging (MRI), due to the limitation of imaging speed, it is commonly required to reconstruct the images from a reduced fourier encoded K-space sequence. This paper presents two temporal model based methods to estimate the un-acquired K-space data, called Adaptive K-space Updating (AKU) methods. The AKU reconstruction algorithms are directly applicable to the truncated K-space sequence generated by the well known Fourier Keyhole (FK) encoding scheme. The experimental study on the real MRI data shows that the proposed AKU methods can produce images with much lower reconstruction error than conventional FK method.

Adaptive K-space Updating Methods for Dynamic MRI Sequence Estimation.

Dynamic magnetic resonance imaging (MRI) acquires a sequence of images for the visualization of the temporal variation of tissue or organs. Keyhole methods such as Fourier keyhole (FK) and keyhole SVD (KSVD) are the most popular methods for image reconstruction in dynamic MRI. This paper provides a class of adaptive keyhole methods, called adaptive FK (AFK) and adaptive KSVD (AKSVD), for dynamic MRI reconstruction. The proposed methods are based on the conventional Fourier encoding and SVD encoding schemes. Instead of the conventional keyhole methods' duplication of un-acquired data from the reference images, the proposed methods use a temporal model to depict the inter-frame dynamic changes and to estimate the un-acquired data in each successive frame. Because the model is online identified from the acquired data, the proposed methods do not require the pre-imaging process, the navigator signals, and any prior knowledge of the imaged objects. Furthermore, the new methods use the conventional keyhole encoding schemes without the bias to any particular object characters, and the temporal model for updating information is in the general form of AR process without the preference to any particular motion types. Hence, the proposed methods are designed as a generic approach to dynamic MRI, other than for any specific class of objects. Studies on dynamic MRI data set show that the new methods can produce images with much lower reconstruction error than the conventional FK and KSVD.

Adaptive keyhole methods for dynamic magnetic resonance image reconstruction.

Parallel Magnetic Resonance Imaging (pMRI) is one of the most important technical advances in current MRI technology. Although many reconstruction methods have been proposed for pMRI, the reconstructed images still suffer visible artifacts especially when high acceleration is used. To improve the reconstruction quality of pMRI, this paper proposes a new reconstruction method. Instead of achieving perfect reconstruction under ideal assumptions, the proposed method uses weighted H(infinity) optimization to minimize the total reconstruction error, including that raised by the noise and uncertainty in sensitivity map. The experimental studies on in vivo data sets demonstrate that the proposed method outperforms significantly the existing methods at high acceleration.

Weighted H(infinity) optimization approach to parallel MR image reconstruction.

Recent advances in high field magnetic resonance technology have increased the interest in the phase of the complex data. Processed phase images are derived from the phase signal by removing the bias field and phase wraps from the initial data. However, the usefulness of this data has been hindered by artifacts at the brain/non-brain surface, particularly in cortical regions. A method is proposed that efficiently removes surface artifacts by performing Gaussian filtering with spatially varying parameters of unwrapped or complex filtered phase images. The proposed method is shown to produce improved images, revealing underlying structure and detail that are otherwise obscured by surface artifacts in images produced by traditional phase processing methods.

Spatially dependent filtering for removing phase distortions at the cortical surface.

Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state-of-the-art MR-PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR-PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR-PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR-PET neuroimaging research studies.

From simultaneous to synergistic MR-PET brain imaging: A review of hybrid MR-PET imaging methodologies.

Attenuation correction is one of the most crucial correction factors for accurate PET data quantitation in hybrid PET/MR scanners, and computing accurate attenuation coefficient maps from MR brain acquisitions is challenging. Here, we develop a method for accurate bone and air segmentation using MR ultrashort echo time (UTE) images.

Accurate hybrid template-based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications.

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance-positron emission tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multicontrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR-guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

MR-PET head motion correction based on co-registration of multicontrast MR images.

Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were jointly coupled in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.

Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex.

Mastering the "arcana of neuroimaging analysis", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, "end-to-end" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.

Zhaolin Chen

Monash Biomedical Imaging,
Department of Electrical and Computer Systems Engineering

Monash University

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Zhaolin Chen

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Monash Biomedical Imaging,Department of Electrical and Computer Systems Engineering

Monash University

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Monash Biomedical Imaging,
Department of Electrical and Computer Systems Engineering